Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 10th May 2025, 09:54:16 EEST
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Session Overview | |
Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Cap: 60 |
Date: Tuesday, 27/Aug/2024 | |
9:30 - 11:00 | 100 SES 00 - LC 1: Link Convenors Meeting part 1 Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Fabio Dovigo Governance Meeting |
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100. Governance Meetings
Meetings/ Events Link Convenor Meeting - 1 Northumbria University, United Kingdom Presenting Author:. |
13:15 - 14:45 | 09 SES 01 A: Doubly-Latent Models of Compositional Effects:An Illustration Using Educational Large-scale Assessment Data Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Ioulia Televantou Research Workshop |
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09. Assessment, Evaluation, Testing and Measurement
Research Workshop Doubly-Latent Models of Compositional Effects:An Illustration Using Educational Large-scale Assessment Data European University Cyprus, Cyprus Presenting Author:This workshop discusses the methodological framework of recent empirical studies (Marsh et al., 2009; Televantou et al., 2015) that have addressed the impact of correcting for measurement error in student-level measures (i.e., student achievement) on compositional effects’ estimates. A compositional effect is revealed when students’ outcomes are associated with the aggregated characteristics of their peers in the school or the classroom, after controlling for pre-existing differences at the student level. Research findings often support what is taken to be the conventional wisdom, suggesting a positive, but weak effect of class- or school-aggregated achievement on students’ academic outcomes. Thus, for example, they suggest a positive association between the peers’ average achievement and a student’s academic achievement. Still, there is remarkably little agreement on this matter. Further, the workshop considers academic self-concept toward mathematics as an educational outcome (Dicke et al., 2018; Televantou et al., 2021; 2023), showing how doubly latent models can be used to investigate the so-called big-fish-little-pond effect (BFLPE). The BFLPE suggests that average achievement at the classroom level or the school negatively predicts academic self-concept, despite the positive effect of achievement on self-concept at the individual level, and it is a robust finding concerning controls for measurement in compositional analysis (Dicke et al., 2018; Televantou et al., 2021). Methodology, Methods, Research Instruments or Sources Used The conventional approach to the investigation of compositional effects is multilevel analysis. Multilevel modeling effectively considers the hierarchical structure of educational data (e.g., students nested within schools, with Level 1 representing individual-level variables nested within Level 2 or group-level variables). The methodological framework typically used until recently to control for unreliability due to measurement error was one of single-level confirmatory factor analysis and Structural Equation Modeling (SEM). SEM research is concerned with issues related to the factor structure: how multiple indicators are related to the latent variables (factors) they are intended to represent, the assessment of measurement error, and the investigation of relationships among the latent variables after controlling for measurement error (Marsh et al., 2009).The problem with using these models in educational settings is that, conventionally, they fail to take potential clustering in the data into account. These two dominant approaches in educational research, multilevel modeling and structural equation modeling, have been integrated into a single framework. Using the Big-Fish-Little-Pond-Effect hypothesis as their substantive basis—a classic compositional effect widely investigated in the field of educational psychology—Marsh et al. (2009) demonstrated a 2x2 taxonomy of multilevel structural equation models. Marsh, et al., used the term “manifest” in relation to measurement error or sampling error when no adjustments are made for the corresponding source of error and “latent” when measurement or sampling error is adjusted for.In this way, the doubly manifest model is the conventional multilevel model that makes no adjustments for measurement or sampling error, while the doubly latent model accommodates both measurement error at level 1 and level 2 as well as sampling error in the higher-level aggregates. The models control for measurement error using multiple indicators and for sampling error, assuming latent rather than manifest aggregation. Conclusions, Expected Outcomes or Findings The proposed workshop seeks to familiarize the attendees with the literature on the mixed findings regarding the magnitude and direction of school/class compositional effects on students’ individual outcomes. It aspires to spur discussion on the validity of empirical results from past and current research that evaluates compositional effects based on sub-optimal models failing to control for measurement error. Meanwhile, it demonstrates the robustness of the BFLPE to different modeling specifications and datasets used. Importantly, this workshop aims to equip educational researchers with the methodological knowledge that allows them to quantify the amount of bias in the compositional effect estimates that could be attributable to a failure to control for measurement error. Hence, by the end of this session, attendees will have achieved the following outcomes: I. Gain a comprehensive understanding of the significance of correcting for measurement error when testing compositional effects in educational contexts. II. Have been presented with research questions that could potentially be answered using large-scale educational survey data and doubly latent models. III. Understand how to perform relevant statistical analyses in the Mplus statistical package. IV. Be equipped with information on further resources for continued learning on the topics presented. References Dicke, T., Marsh, H. W., Parker, P. D., Pekrun, R., Guo, J., & Televantou, I. (2018). Effects of school-average achievement on individual self-concept and achievement: Unmasking phantom effects masquerading as true compositional effects. Journal of Educational Psychology, 110(8), 1112–1126. https://doi.org/10.1037/edu0000259 Marsh, H.W., Lüdtke, O., Nagengast, B., Trautwein, U., Morin, A.J.S., Abduljabbar, A.S. and Köller, O. (2012) Classroom climate and contextual effects: Conceptual and methodological issues in the evaluation of group-level effects. Educational Psychologist, 47 (2), pp. 106-124. 10.1080/00461520.2012.670488 Marsh, H.W., Lüdtke, O., Robitzsch, A., Trautwein, U., Asparouhov, T., Muthén, B. and Nagengast, B. (2009) Doubly-latent models of school contextual effects: Integrating Multilevel and structural equation approaches to control measurement and sampling error. Multivariate Behavioral Research, 44 (6), pp. 764-802. Televantou, I., Marsh, H. W., Dicke, T., & Nicolaides, C. (2021). Phantom and big-fish-little-pond-effects on academic self-concept and academic achievement: Evidence from English early primary schools. Learning and Instruction, 71, 101-399. Televantou, I., Marsh, H. W., Kyriakides, L., Nagengast, B., Fletcher, J. & Malmberg, L-E. (2015). Phantom effects in school composition research: consequences of failure to control biases due to measurement error in traditional multilevel models. School Effectiveness and School Improvement. 26(1), 75-101. https://doi.org/10.1080/09243453.2013.871302 Televantou, I., Marsh, H. W., Xu, K. M., Guo, J., & Dicke, T. (2023). Peer Spillover and Big-Fish-Little-Pond Effects with SIMS80: Revisiting a Historical Database Through the Lens of a Modern Methodological Perspective. Educational Psychology Review, 35(4), 100. |
15:15 - 16:45 | 09 SES 02 A: Perspective-Dependent Biases in the Assessment of Children’s Behavior Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Katharina Jakob Session Chair: Elias Avramidis Symposium |
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09. Assessment, Evaluation, Testing and Measurement
Symposium Perspective-Dependent Biases in the Assessment of Children’s Behavior Externalizing (e.g., hyperactivity, inattention) and internalizing problems (e.g., anxiety) are two broad categories of behavior that – when present to an elevated degree – lead to functional impairment and represent the cardinal symptoms of common disorders with begin in school age (Ahmad & Hinshaw, 2017). For example, in neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder (ADHD), neurodivergent participants have been found to face increased behavioral challenges compared to their neurotypical counterparts (e.g., risk-taking, disruptive behavior; Reinke et al., 2023). Groups of individuals who have been identified in research findings as accurate judges such as teachers or parents (Ferdinand et al., 2007) are commonly included in assessments of students’ behavior. Research suggests that assessments of various types of behavior problems through multiple informants can often provide crucial insights that help form a solid foundation for the development and evaluation of effective interventions in educational and clinical settings (De Los Reyes et al., 2015). Despite the advantages of including reports from several informants, disagreements to varying degrees between reports are frequently reported. However, previous studies rarely further investigated the lack of conformity between raters. More recently, studies have indicated that discrepant perceptions, often referred to as biases, can reveal multifaceted information about how children's behavior is perceived and expressed in various contexts (Achenbach, 2020). Rater biases, such as those related to children's competencies (i.e., positive illusory bias), gender, and special educational needs (SEN), should therefore be deemed as a crucial source of information in assessing behavior (Zurbriggen et al., 2023). Therefore, detected biases should be analyzed to understand, what variables can explain differences in informants’ ratings. This symposium reveals a common denominator in the three contributions – all gathered information from multiple sources regarding emerging behavior problems of school-aged children. Going beyond rater agreements, each contribution addressed unique aspects and possible biases leading to discrepancies among informants. The first contribution of the symposium investigates the consistency between different perspectives (e.g. teachers, parents, students) on students' internalizing and externalizing problems. Students' gender and SEN status are analyzed as possible sources of biases. The second contribution investigates biases in teachers' perceptions regarding their students' behavior and provides a thorough examination of the ambiguity of the term “behavior problems“ as applied by teachers to students and the influences on labeling besides students’ actual behavior (i.e., teachers’ stereotypical beliefs, general sensitivity to disruption, work-related stress experiences). Finally, the third contribution focuses on the discrepancies in the assessments of self- and other-perceived (a) social skills and (b) behavior problems of children with ADHD and ASD compared to non-diagnosed children. Overall, the results presented at this symposium contribute to the expansion of knowledge in the field of perspective-dependent phenomena and biases in the assessment of children’s behavior. References Achenbach, T. M. (2020). Bottom-Up and Top-Down Paradigms for Psychopathology: A Half-Century Odyssey. Annual Review of Clinical Psychology, 16(1), 1–24. https://doi.org/10.1146/annurev-clinpsy-071119-115831 Ahmad, S. I., & Hinshaw, S. P. (2017). Attention-Deficit/Hyperactivity Disorder, Trait Impulsivity, and Externalizing Behavior in a Longitudinal Sample. Journal of Abnormal Child Psychology, 45(6), 1077–1089. https://doi.org/10.1007/s10802-016-0226-9 De Los Reyes, A., Augenstein, T. M., Wang, M., Thomas, S. A., Drabick, D. A. G., Burgers, D. E., & Rabinowitz, J. (2015). The validity of the multi-informant approach to assessing child and adolescent mental health. Psychological Bulletin, 141(4), 858–900. https://doi.org/10.1037/a0038498 Ferdinand, R. F., Van Der Ende, J., & Verhulst, F. C. (2007). Parent–teacher disagreement regarding behavioral and emotional problems in referred children is not a risk factor for poor outcome. European Child & Adolescent Psychiatry, 16(2), 121–127. https://doi.org/10.1007/s00787-006-0581-0 Reinke, A. L., Stiles, K., & Lee, S. S. (2023). Childhood ADHD With and Without Co-occurring Internalizing/Externalizing Problems: Prospective Predictions of Change in Adolescent Academic and Social Functioning. Journal of Attention Disorders, 10870547231187146. https://doi.org/10.1177/10870547231187146 Zurbriggen, C. L. A., Nusser, L., Krischler, M., & Schmitt, M. (2023). Teachers’ judgment accuracy of students’ subjective well-being in school: In search of explanatory factors. Teaching and Teacher Education, 133, 104304. https://doi.org/10.1016/j.tate.2023.104304 Presentations of the Symposium Beyond Rater-Agreements: An Analysis of (In-)Consistencies in Multiple Informants’ Ratings among Students' Behavior
1. Introduction
Over the past decades, there has been increasing interest in the assessment of students’ behavior problems related to Attention-Deficit/Hyperactivity Disorder (ADHD; e.g., inattention) and other externalizing (e.g., conduct problems) and internalizing (e.g., anxiety) problems (e.g., Reinke et al., 2023). Empirical research suggests that the characterization of students’ behavioral problem phenomena requires multiple informants (e.g., teachers, parents, students themselves). In contrast to single-informant reports, this approach is expected to provide sufficient sensitivity and specificity; however, the vast majority of multi-informant assessments of ADHD symptoms and related problems rely on external sources (e.g.; Mulraney et al., 2022; Narad et al., 2015) and retrospective childhood ratings (e.g., Lundervold et al., 2020) leading to substantial underrepresentation of children’s self-perspectives in research. The current study aims to investigate the consistency between self-reports, parent reports, and teacher reports of students’ internalizing and externalizing problems. Further, it will be analyzed if students’ gender and diagnosis of special educational needs (SEN) can explain the specificity (i.e., method bias) in teacher and parent reports.
2. Method
The present study uses data from a random Finnish community sample of 1446 students (male= 47.6%) aged 9-11 years. Students’ externalizing problems (i.e., hyperactivity/inattention (H/I), conduct problems (CP)) and internalizing problems (i.e., emotional symptoms (ES), peer problems (PP)) were measured from students', parents’, and teachers' perspectives using the Strengths and Difficulties Questionnaire (Goodman, 1997). The dichotomous classification of SEN status used was based on information obtained from special education teachers in the participating schools about students’ received support in Finland’s three-tiered system. To assess the consistency, we applied a correlated trait-correlated method minus one (CT-C[M-1] model (Eid et al., 2003).
3. Findings & Conclusions
The initial CT-C(M–1) model indicate good model fit (χ2WLSMV (1283, N = 1378) = 2054.55, p < .001, CFI = .944, SRMR = .087, RMSEA = .021). Results show in general moderate to low consistency between student and teacher or parent reports. Thus, the method specificity for parent and teacher reports was moderate to high, confirming the importance of using different raters. As expected, gender and the status SEN could predict the specificity in other reports of students’ behavior to some extent, in particular for externalizing problems. Overall, the results highlighted the vital role of multi-informant approaches in the assessment of student's behavior problems.
References:
Goodman, R. (1997). The Strengths and Difficulties Questionnaire: A Research Note. Journal of Child Psychology and Psychiatry, 38(5), 581–586. https://doi.org/10.1111/j.1469-7610.1997.tb01545.x
Lundervold, A. J., Halmøy, A., Nordby, E. S., Haavik, J., & Meza, J. I. (2020). Current and Retrospective Childhood Ratings of Emotional Fluctuations in Adults With ADHD. Frontiers in Psychology, 11, 571101. https://doi.org/10.3389/fpsyg.2020.571101
Mulraney, M., Arrondo, G., Musullulu, H., Iturmendi-Sabater, I., Cortese, S., Westwood, S. J., Donno, F., Banaschewski, T., Simonoff, E., Zuddas, A., Döpfner, M., Hinshaw, S. P., & Coghill, D. (2022). Systematic Review and Meta-analysis: Screening Tools for Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 61(8), 982–996. https://doi.org/10.1016/j.jaac.2021.11.031
Narad, M. E., Garner, A. A., Peugh, J. L., Tamm, L., Antonini, T. N., Kingery, K. M., Simon, J. O., & Epstein, J. N. (2015). Parent–teacher agreement on ADHD symptoms across development. Psychological Assessment, 27(1), 239–248. https://doi.org/10.1037/a0037864
Reinke, A. L., Stiles, K., & Lee, S. S. (2023). Childhood ADHD With and Without Co-occurring Internalizing/Externalizing Problems: Prospective Predictions of Change in Adolescent Academic and Social Functioning. Journal of Attention Disorders, 10870547231187146. https://doi.org/10.1177/10870547231187146
Under Which Conditions Do Teachers Label Students as Having Behavior Problems?
1. Theory
Many teachers are concerned that students with behavior problems may strain teaching, classmates, or themselves (MacFarlane & Woolfson, 2013). Although these concerns seem intuitively understandable, research has shown that the term “behavior problems” refers to a perspective-dependent phenomenon of various forms and degrees (Beaman et al., 2007; Crawshaw, 2015) that is susceptible to perception biases (Eckstein, 2019). Therefore, it is highly unclear what teachers mean when they use this expression without further explanation. This gives rise to a research desideratum we address in this paper: studies should investigate the extent to which the teacher-assigned label “behavior problems” is substantiated by students’ actual behaviors and to what extent it is due to other, idiosyncratic conditions.
2. Methods
85 elementary school teachers and 1412 students (11.7 years) answered a survey. The teachers reported the degree to which they consider each student in their class to have behavior problems. As presumed predictors of these labeling tendencies, we investigated the frequency of students’ undisciplined behaviors (ω = .84), non-behavioral student characteristics (sex; learning ability [ω = .72]), teacher characteristics (general sensitivity to disturbances [ω = .71]; work-related stress experience [ω = .80]), and context factors (latent class means of students’ indiscipline and learning ability). A two-level structural equation model was set up and estimated in Mplus 8.10 (Marsh et al., 2009; Muthén & Muthén, 2017-2023). All effects were estimated while controlling for the others.
3. Findings
The model fitted the data well (X2 = 139.468, df = 71, p < .001; RMSEA = .024; CFI = .991). At level 1, significant effects on the teachers’ labeling tendencies were found for the individual students’ indiscipline (Beta = .50), sex (Beta = -.25), and learning ability (Beta = .21). At level 2, teachers’ general sensitivity to disturbances (Beta = .35) and work-related stress experience (Beta = .35) were found to be significant conditions of their general labeling tendency across all students; no significant effects were found for the latent class means of indiscipline and learning ability. In sum, the findings indicate that the label “behavior problems” was well substantiated by the students’ actual behaviors – but it was also due to various other conditions that had little or nothing to do with their behavior, such as teachers’ stereotypical beliefs (Anderson et al., 2012). Reflecting on the study’s strengths and limitations, we will discuss the implications of these results for future research and teaching practice.
References:
Anderson, D. L., Watt, S. E., & Noble, W. (2012). Knowledge of Attention Deficit Hyperactive Disorder (ADHD) and Attitudes Toward Teaching Children With ADHD: The Role of Teaching Experience. Psychology in the Schools, 49(6), 511-525. https://doi.org/doi.org/10.1002/pits.21617
Beaman, R., Wheldall, K., & Kemp, C. (2007). Recent research on troublesome classroom behaviour: A review. Australasian Journal of Special Education, 31(1), 45–60. https://doi.org/10.1080/10300110701189014
Crawshaw, M. (2015). Secondary school teachers’ perceptions of student misbehaviour. Australian Journal of Education, 59(3), 293–311.
Eckstein, B. (2019). Production and Perception of Classroom Disturbances – A new approach to investigating the perspectives of teachers and students. Frontline Learning Research, 7(2), 1-22. https://doi.org/10.14786/flr.v7i2.411
MacFarlane, K., & Woolfson, L. M. (2013). Teacher attitudes and behavior toward the inclusion of children with social, emotional and behavioral difficulties in mainstream schools: An application of the theory of planned behavior. Teaching and Teacher Education, 29, 46-52.
Marsh, H. W., Lüdtke, O., Robitzsch, A., Trautwein, U., Asparouhov, T., Muthén, B., & Nagengast, B. (2009). Doubly-latent models of school contextual effects: Integrating multilevel and structural equation approaches to control measurement and sampling error. Multivariate behavioral research, 44(6), 764-802.
Muthén, B. O., & Muthén, L. K. (2017-2023). Mplus user’s guide (8th ed.). Muthén & Muthén.
WITHDRAWN Positive Illusory Bias in ADHD and Autism spectrum disorder (ASD): A disorder-related phenomenon
Children with Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) frequently overestimate their own abilities in different contexts, reporting higher self-perceptions than the others’ external perceptions (Lau-Zhu et al., 2019). This tendency to overestimate one’s capabilities, compared to external evaluations, is called positive illusory bias (PIB, Owens et al., 2007). However, it is not clear whether the two clinical populations overestimate their own abilities in the same way and if this overestimation impacts multiple areas of functioning (Martin et al., 2019). The present study investigated the accuracy of self-perception of abilities of children with ADHD and ASD compared to non-diagnosed (ND) peers in different areas of functioning. Specifically, differences in the estimation of (a) social abilities and (b) behavioral problems in the three groups were analyzed.
Two hundred and twenty Italian children (85% M) between 8 and 16 years (M=11.48, SD=2.28) were included in the study. 50 children with ADHD (84% M), 49 with ASD (79% M) without intellectual disability and 121 ND (86% M) participants were enrolled and matched for sex, age, and intelligence quotient (IQ). Two parallel forms of a specific questionnaire measuring social abilities and behavioral problems were filled out by the children and their parents to compare their perceptions.
Two different estimation indices were computed based on the discrepancy between the child’s perception and the adult’s report on children’s social abilities and behavioral problems. Separate linear regressions were run for both estimation indices to investigate the association between the two estimation indices and different independent variables: control variables (i.e., age and IQ) and group (ADHD, ASD and ND). Our results showed a different pattern in the two estimation indices. The self-perception of social abilities, independently from the group, decreased with higher age and was significantly impaired only in the ADHD population, compared to both the ASD and ND groups. Conversely, both children with ADHD and ASD estimate their own behavioral problems in a similar way to that of their parents.
Our findings confirm that the overestimation of one’s own abilities, compared to external estimations, regards mainly subjects with ADHD (Capodieci et al., 2019). Moreover, this overestimation of abilities is not always present (Owens & Hoza, 2003). Our results revealed the importance of paying attention to the interpretation of self-reports during the assessment of abilities in children and adolescents with ADHD and helped in differentiating specific difficulties of self-perception abilities between ADHD and ASD.
References:
Capodieci, A., Crisci, G., & Mammarella, I. C. (2019). Does Positive Illusory Bias Affect Self-Concept and Loneliness in Children With Symptoms of ADHD? Journal of Attention Disorders, 23(11), 1274–1283. https://doi.org/10.1177/1087054718763735
Lau-Zhu, A., Fritz, A., & McLoughlin, G. (2019). Overlaps and distinctions between attention deficit/hyperactivity disorder and autism spectrum disorder in young adulthood: Systematic review and guiding framework for EEG-imaging research. Neuroscience & Biobehavioral Reviews, 96, 93–115. https://doi.org/10.1016/j.neubiorev.2018.10.009
Martin C. P., Peisch V., Shoulberg E. K., Kaiser N., Hoza B. (2019). Does a social self-perceptual bias mask internalizing symptoms in children with attention-deficit/hyperactivity disorder? Journal of Child Psychology and Psychiatry, 60(6), 630–637. https://doi.org/10.1111/jcpp.13024
Owens, J. S., Goldfine, M. E., Evangelista, N. M., Hoza, B., & Kaiser, N. M. (2007). A Critical Review of Self-perceptions and the Positive Illusory Bias in Children with ADHD. Clinical Child and Family Psychology Review, 10(4), 335–351. https://doi.org/10.1007/s10567-007-0027-3
Owens, J. S., & Hoza, B. (2003). The role of inattention and hyperactivity/impulsivity in the positive illusory bias. Journal of Consulting and Clinical Psychology, 71(4), 680–691. https://doi.org/10.1037/0022-006X.71.4.680
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17:15 - 18:45 | 09 SES 03 A: Understanding Educational Disparities Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Ana María Mejía-Rodríguez Paper Session |
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09. Assessment, Evaluation, Testing and Measurement
Paper Finnish Language Utilisation Rate and Mathematics Learning Outcomes of Students with Immigrant Background in Finland Tampere University, Finland Presenting Author:The role of immigrants has become increasingly important in the developed countries’ population structure. The integration of immigrants into the society requires among other things education of the younger generations, which creates possibilities for succeeding in future work life. However, students’ immigrant background has in research often been associated with weaker educational achievement, which poses problems also for educational equality. The PISA studies have revealed that in international comparison, the difference between students with immigrant background and the native students is among the largest in Finland. It has been speculated that immigrant students’ lacking skills in the language of teaching could be reflected on their performance also in other areas of assessment, but there is only little evidence supporting this claim. Schnepf (2007) compared the differences between native and immigrant students in 10 countries based on PISA, TIMMS and PIRLS data, showing that in the USA, continental Europe and the UK, the performance gaps were largely explained by lacking language skills. However, there is little previous research on the relationship between the language utilisation rate and performance in assessments. Many studies support the hypothesis that language utilisation rate at home and with peers is associated with better learning outcomes (Brenneman, Morris & Israelian 2007; Dronkers & van der Velden 2013; Hannover et al. 2013; Levels, Dronkers & Kraaykamp 2008), but some studies have not found a link between them (Agirdag, Jordens & van Houtte 2014; Agirdag & Vanlaar 2016). The aim of this study was to explore the effect of Finnish language utilisation rate on mathematics performance for immigrant students in Finland. The research questions were: 1. Do native students and students with immigrant background differ from each other in the mathematical thinking and reading, and the time spent on tasks? 2. How does the Finnish language utilisation rate explain the mathematical thinking performance of students with immigrant background, when their reading skills, time spent on tasks and gender are taken into account? Methodology, Methods, Research Instruments or Sources Used We used the data from one large municipality in Finland (N=942). We assessed 6th grade students’ performance in reading and mathematical thinking. We asked students which languages they used with their parents and siblings, and calculated language utilisation rate index based on the answers. Students’ reading skills were assessed by a curricular test developed by professionals working on the national curricular sample-based assessments. The items were first scored as correct/incorrect, and the total score was transformed into percentages of correctly solved items. Mathematical thinking was measured by an adaptive test consisting of two types of items. After four anchor items, the test adapted to students' performance level by selecting more difficult or easier items from a large item bank calibrated on earlier data from more than 10 000 students using Item Response Modelling. The test ended when the predefined accuracy rate was reached, or the student had completed 20 items. An estimate of the students' proficiency level was calculated and rescaled to a scale, in which 500 points was the average performance level in the calibration data. We analysed the results using multiple-group linear regression models in Mplus 8.0. Conclusions, Expected Outcomes or Findings The results show that children of international families (2,5 generation), and native students performed clearly better in adaptive mathematical thinking tasks than 1st and 2nd generation immigrant students. In contrast to previous research, the Finnish language utilisation rate did not predict their results in the test. The strongest predictor of mathematical thinking was the time spent on tasks, followed by reading skills. In group analyses, reading skills predicted mathematical thinking only for the native students and 2nd generation immigrant students. In terms of reading skills, it is noteworthy that the level of proficiency in the language of instruction has been found to be related to mathematical competence, but in this study, reading predicted mathematical thinking only in some subgroups. In the future, it would be important to delve deeper into the underlying causes of the performance gap to promote equal opportunities for students with immigrant background in Finnish society. The results of this study suggest that the Finnish language utilisation rate is not related to mathematical thinking skills. In the future, more attention should be paid to the importance of peer learning in language learning, for example by examining the relationship between the use of Finnish with friends and the ability of students with immigrant background in different subjects. Such an approach would help to get a broader picture of the relationship between language utilisation rate and learning outcomes. This study was relevant because it added to the knowledge on the relationship between language utilisation rate and learning outcomes. The results also confirmed the view that time spent on tasks is a stronger explanatory factor than the mere level of proficiency in the language of instruction in school. References Agirdag, O., Jordens, K., & van Houtte, M. (2014). Speaking Turkish in Belgian primary schools: Teacher beliefs versus effective consequences. Bilig: Journal of Social Sciences of the Turkish World 70 (3), 7–28. https://hdl.handle.net/11245/1.430345 Agirdag, O. & Vanlaar, G. (2016). Does more exposure to the language of instruction lead to higher academic achievement? A cross-national examination. International Journal of Bilingualism 22 (1), 123–137. https://journals.sagepub.com/doi/10.1177/1367006916658711 Brenneman, M. H., Morris, R. D. & Israelian, M. (2007). Language preference and its relationship with reading skills in English and Spanish. Psychol. Schs. 44 (2), 171–181. https://doi.org/10.1002/pits.20214 Dronkers, J., & van der Velden, R. (2013). Positive but also negative effects of ethnic diversity in schools on educational achievement? An empirical test with cross-national PISA data. In Windzio M. (eds.) Integration and Inequality in Educational Institutions. New York: Springer, 71–98. https://doi.org/10.1007/978-94-007-6119-3_4 Hannover, B., Morf, C. C., Neuhaus, J., Rau, M., Wolfgramm, C. & Zander-Musić, L. 2013. Immigrant adolescents' self-views and school success. J Appl Soc Psychol 43 (1) 175–189. https://doi.org/10.1111/j.1559-1816.2012.00991.x Levels, M., Dronkers, J., & Kraaykamp, G. 2008. Immigrant children’s educational achievement in western countries: Origin, destination, and community effects on mathematical performance. American Sociological Review 73 (5), 835–853. https://doi.org/10.1177/000312240807300507 Schnepf, S.V. (2007). Immigrants’ educational disadvantage. An examination across ten countries and three surveys. Journal of Population Economics, 20 (3), 527–545. 09. Assessment, Evaluation, Testing and Measurement
Paper Socio-demographic Correlates of Performance on a Spelling Test Among Sixth Grade Students With Spelling Difficulties University of the Aegean, Department of Primary Education Presenting Author:The ability to spell is a gradually developing, long, and complex process. It is directly linked to language skills, phonological, grammatical, and semantic awareness, cognitive functions (intelligence, perception, and memory), and metacognitive skills (Diamanti et al., 2014). Spelling ability is documented as a composite skill that is rather laborious to acquire and which is an essential part of writing (Oakley & Fellowes, 2016) Some children experience great difficulty as compared to their peers in learning to spell; these children typically have difficulty learning to read as well. It is important to have a good understanding of these complexities in order to comprehend how children learn to spell and why some children have severe difficulties with this process. Theories about how children learn to spell need to account for the full spectrum of patterns, as do theories about why some children have difficulty in spelling and how we can assist them(Treiman, 2017b, 2017a). The correlation between intelligence, language problems, and spelling is well established in research (Smith et al., 2016) and demonstrates that general intelligence and phonological awareness contribute to the acquisition of reading and spelling skills in children (Siddaiah & Padakannaya, 2015; Zarić et al., 2021). Research findings suggest that there may be other unknown environmental factors contributing to spelling, such as family environment, neighborhood school, print exposure, environmental toxins, nutrition, the number of siblings, experiences such as visits to the library, and the number of books in the home (Lewis et al., 2018). This study explores the correlation between students’ performance on a spelling test and specific socio-demographic characteristics, such as gender, father’s and mother’s occupation, who helps them with homework, the degree of satisfaction with their school performance, their parents’ degree of satisfaction with their school performance, difficulties they face in school subjects (reading, writing, arithmetic), essays, tests (oral and written), and use of leisure time. Research in Greece investigating the development of spelling ability in individuals with and without LD seems limited; however, in recent years in Greece, there has been interest in the linguistic factors related to the development of spelling ability as well as in the analysis of spelling errors of students with and without LD (Protopapas et al., 2013) In particular, the research hypotheses were: Η1: The gender of students with spelling difficulties will correlate with their spelling performance. Η2: Parents’ occupation (father and mother) will correlate with spelling performance. Η3: The satisfaction of children with spelling difficulties with their school progress will correlate with their spelling performance. Η4: Perceived parents’ satisfaction with school progress will correlate with their spelling performance. Η5: Children’s perceived difficulties in school subjects (dictation, reading, writing, arithmetic), essay writing, and oral or written assessments (tests) will correlate with performance in spelling. Η6: The use of free time will correlate with performance. Methodology, Methods, Research Instruments or Sources Used The study presents the pilot findings of a large-scale survey. Participants The sample consisted of 225 children: 111 (49.3%) boys and 114 (50.7%) girls. The average age of the children in the sample is 11.5 years, with the average age of boys being only two months older than girls. All the children attended the 6th grade of the primary school on the island of Rhodes: 50.2% of them attended school in the city of Rhodes, and 49.8% attended school in the semi-urban and rural areas. Instruments The following instruments were utilised to collect data for the study: 1) A self-report of two sections, to obtain socio-demographic data and family characteristics. 2) The DWT is a passage-spelling test. It is an age-appropriate passage with morphological variety developed by Zachos and Zachos in 1998 (Zachos & Zachos, 1998).. Procedure and data analysis Data collection took place in the school years 2017–20. The questionnaires were administered to the students by their teacher. Data analysis was based on descriptive statistics and the non-parametric Kruskal-Wallis and Mann-Whitney U tests for independent samples. Conclusions, Expected Outcomes or Findings The study showed that there was a correlation between students’ performance on the spelling test and the demographic variables: gender, mother’s occupation, reading help, children’s satisfaction with school performance, parents’ satisfaction with school performance, reading difficulties, spelling difficulties, essay difficulties, arithmetic difficulties, oral difficulties, writing difficulties, and leisure time use. Especially, the study indicated that most students scored high in spelling errors. Moreover, the study revealed statistically significant differences between children’s spelling performance in the DWT test and the following demographic variables: Gender, mother’s occupation, reading assistance, children’s satisfaction with school performance, parents’ satisfaction with school performance, reading difficulties, spelling difficulties, exposure difficulties, arithmetic difficulties, oral difficulties, writing difficulties, and leisure time utilisation. Students who had a tutor at home or another person for help made more spelling errors compared to students who had no help. Children who were dissatisfied with their own or their parents’ performance in school made a higher number of spelling mistakes. At the same time, children who reported having difficulties (a few to too many) in reading, spelling, composition, arithmetic, speaking, and writing made more spelling errors. Finally, students who stated that they go to their country house in their free time and students with fewer extracurricular activities made more spelling mistakes. The present study shows that certain socio-demographic characteristics are correlated with students’ spelling attainment. These findings emphasise both that children’s spelling ability is a complex process involving a variety of factors and that each student should be considered individually. In conclusion, the research highlights the need to consider socio-demographic factors in terms of teaching, educational reforms, and changes in issues of spelling: learning and dealing with spelling difficulties. References Diamanti, V., Goulandris, N., Stuart, M., & Campbell, R. (2014). Spelling of derivational and inflectional suffixes by Greek-speaking children with and without dyslexia. Reading and Writing, 27(2), 337–358. https://doi.org/10.1007/s11145-013-9447-2 Oakley, G., & Fellowes, J. (2016). A closer look at spelling in the primary classroom. Primary English Teachers Association Australia. Siddaiah, A., & Padakannaya, P. (2015). Rapid automatized naming and reading: A review. Psychological Studies, 60(1), 70–76. https://doi.org/10.1007/s12646-014-0280-8 Smith, B. L., Smith, T. D., Taylor, L., & Hobby, M. (2016). Relationship between Intelligence and Vocabulary: Perceptual and Motor Skills. https://doi.org/10.2466/pms.100.1.101-108 Treiman, R. (2017a). Learning to spell: Phonology and beyond. Cognitive Neuropsychology, 34(3–4), Article 3–4. https://doi.org/10.1080/02643294.2017.1337630 Treiman, R. (2017b). Learning to spell words: Findings, theories, and issues. Scientific Studies of Reading, 21(4), 265–276. https://doi.org/10.1080/10888438.2017.1296449 09. Assessment, Evaluation, Testing and Measurement
Paper Participation in ECE in Kosovo: (Re-)migration and Acquired Cultural Capital as a Resource for the Participation of ECE Institutions Universität Graz, Austria Presenting Author:Research shows that returns of people that fled can positively influence post-war recoveries on country (Wahba 2021). Education is argued to be of special relevance for post-war recoveries in general. Attendance of children early childhood education (ECE) can play an important role for individuals and society, as participation is in general associated with a positive language, cognitive, and social development supporting a more successful educational career (e.g. Melhuish et al., 2015) and specifically important for the well-being in conflict zones (e.g. Osmanli et al., 2021). Disparities in ECE attendance, according to Boudon's work (1974), can be understood as the result of an interplay of the situation of the family (as supportive factors or barriers) and rational educational decisions. Apart from location and availability (e.g. Sixt 2013), disparities in attendance in ECE are often found with regard to, economic and cultural capital of parents (e.g. Adema et al. 2016) and for immigration countries also the migrant status of families (e.g. Müller et al. 2014). For conflict contexts, the role remigration plays for attendance in ECE and further trajectories has not been well researched. In this article we therefore analyze the role remigration and war-related international connectivity plays for attendance in ECE in the Kosovan context. For more than 30 years Kosovo is classified as a crisis region, with different phases of war and stability, causing at least 4 different big waves of dynamic war- and crisis-related migration and remigration movements ( Hajdari and Krasniqi 2021). Studying inequalities in ECE attendance is of particular interest as children affected by big migrations waves in the 1990ies are now parents and in Kosovo (like in many conflict regions), with the exception of the preschool-year (age 5-6), non-compulsory, highly privatized and regional differences in availability can be found (Gjelaj et al., 2018). As studies reports on the risk of remigration to Kosovo in terms of reintegration, unemployment, economic situation (Möllers et al. 2017) as well as mental health and the associated loss of quality of life (Lersner et al. 2008) negative primary origin effects can be expected. However, when migration or war-related international personal encounter positively influenced parental acquisition of cultural and social capital (Farrell, Mahon and Mcdonagh, 2012) positive influence, in terms of educational aspiration and insights into the value of ECE, positive secondary origin effects seem plausible. Methodology, Methods, Research Instruments or Sources Used To analyze disparities in patters of attendance in ECE related to war-caused migrations we use data from home survey and student questionnaire of the TIMSS 2019 for Kosovo (Foy and LaRoche 2020) were the parents and the 4th Grade students themselves (nstudents= 4496; average age was 9.9) also reported on early learning. As remigration was not asked in the survey specifically, we look at 2 indicators to analyze patterns of ECE attendance (min. 3 years, 60 %): Immigration to Kosovo (at least one family member born outside of Kosovo, 8 %) and language practice in families (Every day communication between mother and child in English, German, Italian or French language, 21 %). As these languages are not spoken in Kosovo but major emigration countries, we find it plausible to assume that language competences have been acquired as part of a migration related experience. We calculated logistic regression analysis on EC attendance using the IEA IDB Analyzer, which allows for weighting and correct estimation of standard errors, given the complex sampling of the study. Conclusions, Expected Outcomes or Findings We find no significant difference in ECE attendance in relation to the immigration of at least one member of the nuclear family. Migration-relevant linguistic family practices are associated with a 2.6 times higher chance of attending ECE, and significant effects remain even when controlling for education and occupational status of parents. Disparities related to educational and economic capital of families can also be confirmed. The indicator language practice in the families, also appears as an independent explanatory factor in explaining achievement differences in mathematics in the fourth grade, and remains significant when controlling for economic and cultural capital as well as aspirations. The study is having a number of limitations, starting with the instrumentation and the nature of the survey data. The strength lies in the utilization of representative large-scale data for a conflict context, where data is scares. The results indicated that (re-)migration by itself, may not be supportive for ECE attendance in crisis contexts. Only when war- and crisis-related migration or opportunities for global encounters support the acquisitions of cultural capital, positive effects for educational decisions of parents can be expected. Obviously, additional qualitative studies and better instrumentation for surveys are needed to further look into when and how war-related migration can be considered a strengthen factor for early childhood education. Supportive findings, would support and emphasis the importance’s of providing high quality education for displaced people also for post-war recovery and educational opportunities of next generations. References Publication bibliography Adema, W., Clarke, C., Thévenon, O., & Queisser, M. (2016). Who uses childcare? Background brief on inequalities in the use of formal early childhood education and care (ECEC) amony very yound children. Available online at https://www.oecd.org/els/family/Who_uses_childcare-Backgrounder_inequalities_formal_ECEC.pdf, checked on 11/22/2022. Boudon, R. (1974). Education, opportunity, and social inequality: Changing prospects in Western society. Wiley series in urban research. New York, NY: Wiley. Farrell, M., Mahon, M. & McDonagh, J. (2012). The rural as a return migration destination. European Countryside, 4(1). https://doi.org/10.2478/v10091-012-0012-9 Foy, P. & LaRoche, S. (2020). Estimating Standard Errors in the TIMSS 2019 results. In M. O. Martin, M. von Davier & I. V. Mullis (Hrsg.), TIMSS-2019-MP-Technical-Report (14.1-15.1). TIMSS & PIRLS International Study Center, Lynch School of Education and Human Development, Bost College and International Association for the Evaluation of Educational Achievement. Gjelaj, M., Rraci, E. & Bajrami, K. (2018). Pre-school Education in Kosovo. Available online at https://www.researchgate.net/publication/334896051_PRE-SCHOOL_EDUCATION_IN_KOSOVO, checked on 09/15/2022 Hajdari, L. & Krasniqi, J. (2021). The economic dimension of migration: Kosovo from 2015 to 2020. Humanities and Social Sciences Communications, 8(1). https://doi.org/10.1057/s41599-021-00923-6 Lersner, U. von, Elbert, T. & Neuner, F. (2008). Mental health of refugees following state-sponsored repatriation from Germany. BMC psychiatry, 8, 88. https://doi.org/10.1186/1471-244X-8-88 Melhuish, E., Ereky-Stevens, K., Petrogiannis, K., Ariescu, A., Penderi, E., Rentzou, K., Tawell, A., Slot, P., Broekhuizen, M. & Leseman, P. (2015). A review of research on the effect so Early Childhood Education and Care (ECEC). Available online at https://ecec-care.org/fileadmin/careproject/Publications/reports/new_version_CARE_WP4_D4_1_Review_on_the_effects_of_ECEC.pdf, checked on 12/22/2022. Möllers, J., Traikova, D., Herzfeld, T. & Bajrami, E. (2017). Study on rural migration and return migration in Kosovo. Available online at http://hdl.handle.net/10419/168315, checked on 11/25/2022. Müller, N., Strietholt, R. & Hogrebe, N. (2014). Unlgeiche Zugänge zum Kindergarten. In K. Drossel, R. Strietholt & W. Bos (Hrsg.), Empirische Bildungsforschung und evidenzbasierte Reformen im Bildugnswegsen (S. 33–46). Waxmann. Osmanli, N., Babayev, A., Rustamov, I., & Munir, K. (2021). Emotional and behavioral problems of 7-11 year old children in war-torn nagorno – karabakh region in Azerbaijan. European Psychiatry, 64(S1). https://doi.org/10.1192/j.eurpsy.2021.1670 Sixt, M. (2013). Wohnort, Region und Bildungserfolg. Die strukturelle Dimension bei der Erklärung von regionaler Bildungsungleichheit. In R. Becker & A. Schulzer (Eds.), Bildungskontexte: Strukturelle Voraussetzungen und Ursachen ungleicher Bildungschancen (pp. 483–510). Wiesbaden: VS Verlag für Sozialwissenschaften. Wahba, J. (2021). Who benefits from return migration to developing countries? IZA World of Labor. Vorab-Onlinepublikation. Available online at https://doi.org/10.15185/izawol.123.v2, checked on 11/29/2022. |
Date: Wednesday, 28/Aug/2024 | |
9:30 - 11:00 | 09 SES 04 A: Utilizing International Assessment Data to Understand Variation in Cognitive and Non-cognitive Factors Across Europe and Beyond Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Stefan Johansson Session Chair: Mojca Rozman Symposium |
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09. Assessment, Evaluation, Testing and Measurement
Symposium Utilizing International Assessment Data to Understand Variation in Cognitive and Non-cognitive Factors Across Europe and Beyond The educational landscape in Europe faces a range of challenges, and this symposium proposal highlights affective issues in education such as students’ expectations, confidence, well-being, and student-reported instructional quality. The overall aim is to investigate how these factors vary across students and their relationship with student achievement. Four contributions from international scholars use data from the international assessments PISA (Programme for International Student Assessment) and TIMSS (Trends in International Mathematics and Science Study) to address these issues. We highlight the European perspective and draw on specific examples from more than 20 European and even more countries outside Europe. While the majority of studies conducted with ILSA data focus on student achievement, there is also an affective component of school adjustment that should not be ignored. The first two papers take a comparative perspective focusing on student math confidence. Using the TIMSS 2019 data for 8th graders across 39 countries, the first contribution provides a cross-national analysis of math-specific affective aspects like confidence, enjoyment, and value in math education, focusing on gaps in gender, socioeconomic status (SES), and immigrant status. The second paper, focusing on the Nordic countries, provides another related student perspective on math confidence. This study, using TIMSS 2019 data for fourth graders, examines how students perceive the quality of their instruction and its relation to their mathematics confidence and achievement. It shows that clear and effective teaching are associated with students’ confidence in math. The other two papers provide specific examples from North Macedonia and Slovenia. The contribution from Slovenia focuses on both cognitive and non-cognitive outcomes by exploring the role of students’ wellbeing in academic motivation and achievement. The PISA 2022 results showed that Slovenian students feel less well-being compared to their peers in other OECD countries, which corresponds with lower literacy skills. The research highlights the importance of positive relationships between teachers and students, emotional support, and a sense of belonging at school for academic success. These findings suggest that improving students’ overall well-being could greatly enhance their motivation and achievement in school. In North Macedonia, the PISA 2018 and 2022 results show many students struggling to reach basic levels in essential subjects. This problem is exacerbated by cuts in education funding, both in terms of GDP and government spending. The youth job market is particularly troubling, with high unemployment rates. This reflects a gap between what the education system teaches and what employers need. Despite these challenges, students in North Macedonia have high hopes for their education and careers, but there is a clear gap between these aspirations and their actual school performance. This contribution sheds light on the factors that can explain this misalignment between student achievement and career expectations. The session consolidates research on a theme that often receives too little attention. Collectively, these studies show the complex relationship between education policies, student well-being, academic performance, and job market outcomes. In summary, tackling educational challenges requires a comprehensive approach that looks at both cognitive and non-cognitive factors. These factors are essential for preparing students to meet their goals and contribute positively to society. The session investigates these issues both comparatively and in relation to specific countries to provide lessons learned from the international assessments. It is divided into six parts: four presentations, a discussion by a renowned expert, and an open discussion. References No references. Presentations of the Symposium What About the Affective Gap? A Cross-National Assessment of Math-Related Inequalities on Affective Components of Learning
Equity in education is defined as the guarantee that all students are provided with the opportunities to benefit from their educational system regardless of their gender, socioeconomic status (SES), and family background (OECD, 2014). In the last decades, investments have been made to identify and monitor educational gaps and to better understand the phenomenon of inequality across several groups, such as gender, SES, and immigrant background (e.g., Strello et al., 2023), and to identity the most urgent needs of intervention in diminishing educational inequality. However, these efforts have been predominantly based on achievement, although school adjustment is not defined only as achievement but instead as a child’s success in dealing with all struggles and tasks faced within the school environment (Ladd, 1989). Adding to the achievement components of schooling, there is also an affective component of school adjustment which, we argue, should not be ignored.
Hence, the goal of this study is to give a cross-national overview of the affective gaps based on gender, SES, and immigrant status, by focusing on three indicators of math-specific affective adjustment – confidence, enjoyment, and value. We analyzed the TIMSS 2019 dataset for 8th-grade students in the math domain. Thirty-nine countries were considered, amounting to a total of 224.080 students. Using regression analysis, we estimated gaps throughout different groups – male versus female (i.e., gender), high SES vs. low SES, and native versus non-native (i.e., immigrant background), on three different math-related affective outcomes – confidence, enjoyment, and value, leading to a set of 9 regression analyses. Analyses were performed for each country considering student weights. Although not the focus of this investigation, achievement gaps were also assessed and controlled for.
In what concerned gender, there seems to be a rather consistent affective gap benefitting boys, especially in their confidence towards mathematics, even when controlling for achievement. As for SES, results replicate those of achievement, in the sense that students with high SES score higher on math-related affective components of learning in the vast majority of countries – however, this gap diminishes significantly when controlling for achievement. Finally, when looking into immigrant status, results are rather mixed, especially for math confidence. As for enjoyment and math value, non-natives show a slight tendency for higher scores, and this tendency holds even while controlling for achievement. Detailed results, implications, limitations, and suggestions for future research are presented and discussed in light of existing research, policies, and strategies regarding inequalities in education.
References:
Ladd, G. W. (1989). Children’s social competence and social supports: Precursors of early school adjustment? In B. H. Schneider, J. Nadel., & R. Weissberg (Eds.), Social competence in development perspective (pp. 271-291). Amsterdam: Klumer Academic Publishers.
OECD (2014). Excellence through equity: Giving every student the chance to succeed. Results from PISA 2012. OECD Publishing. Retrieved from https://www.oecd.org/pisa/keyfindings/pisa-2012-results-volume-II.pdf
Strello, A., Strietholt, R., & Steinmann, I. (2023). Mind the gap… but which gap? The distinctions between social inequalities in student achievement. Social Indicators Research, 170, 399-425. https://doi.org/10.1007/s11205-023-03196-5
The Relation between Student-Perceived Instructional Quality and Mathematics Confidence and Achievement: A Nordic Outlook using TIMSS 2019 Grade 4 data
Existing research recognizes the significant role of teaching quality in influencing students' academic (mathematics achievement) and affective outcomes (e.g., mathematics confidence) (Hattie, 2009). Teaching quality can both enhance or diminish the impact of student background characteristics on cognitive achievement (Fauth et al., 2014; Hattie, 2009). Observing, quantifying, and accurately measuring differences in teaching quality presents theoretical and methodological challenges, which could potentially introduce bias and affect study validity (Nilsen et al., 2016). This underscores the need for more empirical research on the relationships between teaching quality and learning outcomes, particularly among primary school students where such research is still limited.
This study aims to provide empirical evidence by comparing the relations between student-perceived instructional quality and mathematics achievement and confidence, and examining differences between classrooms in four Nordic countries. The Nordic context is chosen due to the similarities in culture, school systems, and resources among these countries, making it a suitable setting for this comparative analysis (Kavli, 2018). Utilizing data from the 2019 Trends in Mathematics and Science Study (Mullis & Martin, 2017), the study involves 15,839 fourth graders from Denmark, Finland, Norway, and Sweden. It focuses on the relevance of student-perceived instructional quality (Kyriakides & Creemers, 2008) in relation to both cognitive and non-cognitive outcomes, as well as examining variations across classrooms. The concept of instructional quality in this research encompasses two main constructs: classroom management and instructional clarity. Classroom management involves teachers' structural-organizational activities to engage students in learning and establish a conducive learning environment, while instructional clarity pertains to the effectiveness of pedagogical techniques for clear instruction and support (Nilsen & Gustafsson, 2016).
Employing Multilevel Confirmatory Factor Analysis (MCFA) and Multilevel Structural Equation Modeling (MSEM), the study examines the relationships between instructional quality and two outcome variables: mathematics confidence and mathematics achievement.
Considering the cultural and educational similarities across the Nordic countries, alongside their varied results in international large-scale assessments, the study is guided by two research questions:
1. What are the relations between student-perceived instructional quality (classroom management and instructional clarity) and students’ mathematics confidence and achievement in the Nordic context?
2. What are the relations to student background factors?
The results indicate a positive relationship between instructional clarity and mathematics confidence at the student level across all four countries. At the classroom level, mathematics confidence is positively related to instructional clarity. Student background factors demonstrate weaker correlations with mathematics confidence than with mathematics achievement.
References:
Fauth, B., Decristan, J., Rieser, S., Klieme, E., & Büttner, G. (2014). Student ratings of
teaching quality in primary school: Dimensions and prediction of student outcomes.
Learning and instruction, 29, 1-9. https://doi.org/10.1016/j.learninstruc.2013.07.001
Hattie, J. (2009). Visible learning: a synthesis of over 800 meta-analyses relating to
achievement. Routledge.
Kavli, A.-B. (2018). TIMSS and PISA in the Nordic countries In N. C. o. Ministers (Ed.),
Northern Lights on TIMSS and PISA 2018. Nordic Council of Ministers.
https://www.norden.org/en/publication/northern-lights-timss-and-pisa-2018
Kyriakides, L., & Creemers, B. P. M. (2008). Using a multidimensional approach to measure
the impact of classroom-level factors upon student achievement: a study testing the
validity of the dynamic model. School Effectiveness and School Improvement, 19(2),
183-205. https://doi.org/10.1080/09243450802047873
Mullis, I. V. S., & Martin, M. O. E. (2017). TIMSS 2019 Assessment Frameworks Retrieved
from Boston College, TIMSS & PIRLS International Study Center website:
http://timssandpirls.bc.edu/timss2019/frameworks/
Nilsen, & Gustafsson (Eds.). (2016). Teacher Quality, instructional Quality and Student
Outcomes: Relationships Across Countries, Cohorts and Time (Vol. 2). Springer
Open. https://doi.org/10.1007/978-3-319-41252-8.
Nilsen, T., Gustafsson, J.-E., & Blömeke, S. (2016). Conceptual Framework and
Methodology of This Report. In T. Nilsen & J.-E. Gustafsson (Eds.), Teacher Quality,
Instructional Quality and Student Outcomes: Relationships Across Countries, Cohorts
and Time. Springer Open.
Well-being as an Important Asset of Students’ Academic Motivation and Achievement in Slovenia
Recently, the discourse surrounding the role of students’ well-being and its effects on learning motivation and academic achievement has gained more and more attention in the national and international research community and on the stakeholders’ level. Quality teacher-student relationships, social-emotional support from teachers, a sense of belonging at school, and achievement-related anxiety are often highlighted as important aspects of students’ well-being and have been confirmed in various studies (e.g. Barosso et al., 2020; Harding et al., 2019; Kozina, 2020; Shriver & Buffett, 2015) as significant predictors of both academic motivation and achievement. The latest PISA 2022 results for Slovenia show that, compared to their OECD peers, Slovenian 15-year-olds reported significantly below-average levels of all mentioned aspects of well-being. Since Slovenia also witnessed a significant decline in all three literacy domains in PISA 2022, the article fills the research gap in investigating the role of different aspects of students’ well-being in explaining students’ academic motivation and achievement.
For the data analysis, we used the data from the PISA 2022 survey, which in Slovenia includes a representative sample of 6.721 students aged 15. From the 2022 questionnaire, we used separate scales addressing students’ well-being: perceived quality of teacher-student relationships, teacher support in mathematics class, sense of belonging at school, mathematics-related anxiety, and mathematics effort and persistency scale as an indicator of student’s academic motivation. For academic achievement, we used plausible values for all three PISA literacy domains scales. The internal consistency parameters and the multicollinearity between the variables were checked in the Slovenian sample. We used the linear regression procedure to analyse the size effects of different predictors when explaining students’ academic motivation and achievement using the statistical program IEA IDB Analyzer (Version 5.0.23), which, due to two-stage sampling in the PISA study, allows the use of individual students and sample weights.
The results show that all four aspects of students’ well-being were confirmed as significant predictors of students’ academic motivation, with the highest effect sizes for the quality of teacher-student relationships and math-related anxiety. The results also showed that the quality of teacher-student relationships is the most significant predictor of academic achievement on all three PISA literacy scales. Following these findings, it is thus crucial to establish a system for strengthening the social-emotional competencies of Slovenian teachers and students and shift an education strategy to a more holistic approach that supports the strengthening of different aspects of students’ and teachers’ well-being.
References:
Barosso, C., Ganley, C. M., McGraw, A., Geer, E., Hart, S. A., & Daucourt, M. (2020). A meta- analysis of the relation between math anxiety and math achievement. Psychological Bulletin 147(2), 134–168. https://doi.org/10.1037/bul0000307
Harding, S. et al. (2019). Is teachers’ mental health and wellbeing associated with students’ mental health and wellbeing? Journal of Affective Disorders, 242, 180–187. https://doi.org/10.1016/j.jad.2018.08.080
Kozina, A. (Ed.) (2020). Social, emotional and intercultural competencies for inclusive school environments across Europe: Relationships matter. Hamburg: Dr. Kovač.
Shriver, T., & Buffett, J. (2015). The uncommon core. In J. A, Durlak, C. E. Domitrovich, R. P. Weissberg, & T. P. Gullota (Eds.). Handbook of social and emotional learning: Research and practice (pp. 15–16). New York, London: The Guilford Press.
Students’ Future Education Pathways and their Occupational Aspirations
North Macedonia has one of the highest proportions of students failing to demonstrate basic proficiency (Level 2) in all three domains of science, mathematics and reading among PISA-participating countries (52.2% in PISA 2018 testing; 57.4% in PISA-2022). Young citizens of North Macedonia continue to leave education with among the lowest learning outcomes in Europe. On the other hand, between 2018 and 2023, North Macedonia’s public spending on education as a percentage of GDP fell from 2.80% to 2.72%. The share of total government expenditure allocated to education also declined. While poverty rates have fallen in recent decades, low educational performance is limiting the employment and life opportunities of many individuals and impeding national development.
The activity of youth in the labor market of North Macedonia is relatively low (46.7% in the first three quarters of 2022), either compared to the adults or their peers from the EU countries. One in four people over 15 are unemployed, compared to less than one in ten across OECD countries. Low activity of youth illustrates generally low employment probabilities in the country, and the difficulty of school-to-work transition, that can be attributed to (i) unwillingness of employers to bear the costs of on-the-job training of inexperienced youth (ii) skills mismatch between employer’s needs and skills produced by the education system, as well as (iii) the increasing tendency of youth to stay longer in formal education.
Students’ academic performance on the PISA 2022 testing is not aligned with their expectations for further education and career. They hold ambitious expectations of future education, 72% of students expect to complete tertiary degree (34% expect to finish doctoral studies, ISCED 8). Students (83%) reported that they have a clear idea of their future job, and they expect to work in high-skill occupations, such as software developers, medical doctors, managing directors and chief executives.
This research aims to define the factors that can explain this misalignment between education and career expectations within students’ academic performance. More specifically, data show that there are statistically significant differences in students’ education and career expectations when we compare them based on student’s academic achievement in math, science and reading, Index of economic, social, and cultural status, gender, and language of instruction (Macedonian and Albanian). Data from the research is further discussed with students in focus group discussions. Recommendations from the research will be shared with the state representatives responsible for the reforms in secondary education.
References:
No references.
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13:45 - 15:15 | 09 SES 06 A: ICT and Education: Perspectives from ICILS and PIRLS Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Mojca Rozman Session Chair: Wolfram Schulz Symposium |
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09. Assessment, Evaluation, Testing and Measurement
Symposium ICT and Education: Perspectives from ICILS and PIRLS The role of information and communication technology (ICT) has become increasingly integral in shaping how we work, learn, and connect with others. This is especially recognized by UNESCO, who describes ICT as a “social necessity” for ensuring education as a basic human right, particularly in times of crises (UNESCO, 2023). International large-scale assessments (ILSAs) such as those conducted by the International Association for the Evaluation of Educational Achievement (IEA), provide a lens to monitor the evolving role of ICT in education and its connections to student outcomes. Furthermore, ILSAs provide extensive contextual data, enabling comprehensive analyses of various aspects of ICT, such as access to ICT resources, students’ attitudes towards ICT, or teacher preparedness for the use of ICT in the classroom. The goal of this symposium is to demonstrate how different ILSAs can be used to address a wide range of research questions related to ICT in education and to inform research, policy and practice. We focus on two IEA studies: the International Computer and Information Literacy Study (ICILS) and the Progress in International Reading Literacy Study (PIRLS). ICILS aims to respond to the question: how well are students prepared for study, work, and life in a digital world? (Fraillon & Rožman, 2023). It examines eighth-grade students’ computer and information literacy and, as an optional module, students’ computational thinking. The 2023 cycle of ICILS marked 10 years of the study. PIRLS measures fourth-grade students’ reading achievement. Its latest cycle, conducted in 2021, is the only ILSA that successfully collected data during the COVID-19 pandemic, providing a rich data source to inform about the impact of the pandemic on reading achievement (Mullis et al., 2023). Despite these two studies having different research questions and focusing on different content domains, both ICILS and PIRLS provide valuable information on the topic of ICT in education across a diverse range of educational systems. Four symposium papers provide different perspectives on how ICILS and PIRLS can be used to study the role of ICT in education. The first paper gives an overview of IEA studies on ICT in education, to then look at their representation in the academic literature. The main goal is to map the evolution of publications and to describe the type of research that has been conducted. The second paper uses ICILS 2018 data to explore digital applications usage among foreign language teachers. Specifically, it aims to identify different classes of digital application usage as well as factors related to the identified classes. Using PIRLS 2021 data, the third paper examines the implementation of remote learning during the COVID-19 pandemic in the Dinaric region. In particularly, it examines the different response measures implemented as well as the preparedness for digital remote learning. The fourth paper evaluates two question formats used to assess teaching beliefs in the field trial of ICILS 2023. The two formats are compared on multiple criteria of data quality, providing insights into the use of alternative question formats in digital context questionnaires. The presenting authors will focus on the main findings of their studies, highlighting the different ways in which ICILS and PIRLS data can be used. The discussant of the symposium will offer remarks about the presentations, reflecting on the evolving role of ICT in education and how ILSAs can help us study this topic from different thematic and methodological perspectives. References Fraillon, J. & Rožman, M. (2023). International Computer and Information Literacy Study 2023. Assessment Framework. Amsterdam: International Association for the Evaluation of Educational Achievement (IEA). https://www.iea.nl/sites/default/files/2023-12/20231221%20ICILS2023_Assessment_Framework__Final_0.pdf Mullis, I. V. S., von Davier, M., Foy, P., Fishbein, B., Reynolds, K. A., & Wry, E. (2023). PIRLS 2021 International Results in Reading. Boston College, TIMSS & PIRLS International Study Center. https://doi.org/10.6017/lse.tpisc.tr2103.kb5342 UNESCO. (2023). Digital Education: What You Need to Know. https://www.unesco.org/en/digital-education/need-know Presentations of the Symposium The Use of IEA Studies in Research: A Systematic Review of Comped, SITES, and ICILS Related Research
Over the last decades, information and communication technology (ICT) has become an important part of our lives, including education. Already in 1989, the International Association for the Evaluation of Educational Achievement (IEA) was interested in this topic, when it launched its first study about the introduction and use of computers in education (Pelgrum & Plomp, 1993). With over 30 years of different studies about ICT in education, the IEA continues its investigations of how technologies are used in schools and in classrooms and how prepared are students for a digital world through the International Computer and Information Literacy Study (ICILS).
While the international reports of ICILS and its predecessors offer a broad range of information, they only scratch the surface of what can be done with the available data. Additional, and highly relevant, insights come from external publications. Following the reviews of Hopfenbeck et al. (2018) and Lenkeit et al. (2015), the present study is a systematic review of English-language peer-reviewed articles related to three IEA studies about ICT in education: Computers in Education (Comped), the Second Information Technology in Education Study (SITES) and ICILS.
The main goal of this review is to map the evolution of publications based on these studies and to describe the type of research that has been conducted, both in terms of research topics and methodological approaches. Through this, we aim not only to identify crucial literature to be used by any established or newcomer researcher in the field but also to provide guidance on topics for future research. An additional goal is to encourage the use of ICILS in secondary research.
The studies that are included in the review were identified through an electronic search was conducted across five different channels including, for example, multiple electronic databases and target searches in journals focused on international large-scale assessments or on ICT in education. After screening procedures, a total of 91 publications were deemed as relevant for the review. Results map the frequency of publications through years, journals and countries. Further results summarize the major topics studied across within four types of publications identified: descriptive studies, effectiveness studies, critiques or scale evaluations, and case studies.
References:
Hopfenbeck, T. N., Lenkeit, J., Masri, Y. E., Cantrell, K., Ryan, J., & Baird, J.-A. (2018). Lessons Learned from PISA: A Systematic Review of Peer-Reviewed Articles on the Programme for International Student Assessment. Scandinavian Journal of Educational Research, 62(3), 333–353. https://doi.org/10.1080/00313831.2016.1258726
Lenkeit, J., Chan, J., Hopfenbeck, T. N., & Baird, J.-A. (2015). A review of the representation of PIRLS related research in scientific journals. Educational Research Review, 16, 102–115. https://doi.org/10.1016/j.edurev.2015.10.002
Pelgrum, W. J., & Plomp, T. (1993). The IEA study of computers in education: Implementation of an innovation in 21 education systems. Pergamon.
Latent Classes of Digital Application Usage in Foreign Language Teaching in Germany and Related Determinants – Secondary Analyses Based on ICILS
International comparative school performance studies, such as the IEA-Study ICILS (Fraillon & Rožman, 2023), offer insights into educational practices across Europe and the world. The methodological design of the ICILS-Study enables sub-samples to be formed, allowing for the examination of specific groups and the generation of knowledge that could be used to improve school systems. This methodological possibility is used in this contribution to identify classes of digital applications usage by foreign language teachers and related determinants. Previous non-subject-specific studies like Graves and Bowers (2018) were able to identify four media patterns (evaders, assessors, presenters, dexterous). Additionally, factors influencing ICT use, such as teachers' self-efficacy are well studied across subjects (Gerick, Eickelmann & Bos, 2017). However, specific digital application usage classes and their determinants in foreign language teaching remain unexplored, despite possible subject-subcultural influences. This contribution aims to answer two research questions:
1. To what extent can different digital application usage classes be identified for foreign language teachers compared to non-foreign language teachers in Germany?
2. To what extent is there a connection between identified digital application usage classes and determinants of ICT use for both groups?
The study employs ICILS 2018 teacher data from Germany (n=2328; Eickelmann et al., 2019), taking into account data weighting (Tieck & Meinck, 2020).
To answer the first RQ, a latent class analysis is conducted using MPlus8. The class solution is based on statistical information criteria (e.g. smallest BIC; Eshima, 2022). The analysis identifies three usage classes for foreign language (a) and non-foreign language (b) teacher groups: avoiders (a: 80.4%; b: 78.1%), selective users (a: 17.4%; b: 19.7%), and multiple users (a: 2.2%; b: 2.2%) of digital applications.
For the second RQ, a hierarchical regression analysis was conducted using the IDB Analyzer to explore the connections between usage classes and determinants.
The analysis, grounded in theoretical considerations, employs five regression models. Results highlight significant correlations, including foreign language teachers' affiliation with multiple users being linked to positive attitudes towards ICT (Model V; ß=.27, adjusted R²=.15). Correlations vary across usage classes and teacher groups.
The findings contribute to the understanding of the integration of digital applications in language teaching. This provides valuable insights for researchers and policymakers, particularly in Europe. Potential explanations, such as subject-subcultural influence on digital application usage, related determinants and alternative methodological approaches are discussed.
References:
Eickelmann, B., Bos, W., Gerick, J., Goldhammer, F., Schaumburg, H., Schwippert, K. et al. (Hrsg.). (2019). ICILS 2018 #Deutschland. Computer- und informationsbezogene Kompetenzen von Schülerinnen und Schülern im zweiten internationalen Vergleich und Kompetenzen im Bereich Computational Thinking. Münster: Waxmann.
Eshima, N. (2022). An Introduction to Latent Class Analysis. Singapore: Springer Singapore. https://doi.org/10.1007/978-981-19-0972-6
Fraillon, J. & Rožman, M. (2023). International Computer and Information Literacy Study 2023. Assessment Framework. Amsterdam: International Association for the Evaluation of Educational Achievement (IEA). https://www.iea.nl/sites/default/files/2023-12/20231221%20ICILS2023_Assessment_Framework__Final_0.pdf
Gerick, J., Eickelmann, B. & Bos, W. (2017). School-level predictors for the use of ICT in schools and students’ CIL in international comparison. Large-scale Assessments in Education, 5(1), 1–13. DOI: 10.1186/s40536-017-0037-7
Graves, K. E. & Bowers, A. J. (2018). Toward a Typology of Technology-Using Teachers in the ”New Digital Divide”: A Latent Class Analysis of the NCES Fast Response Survey System Teachers’ Use of Educational Technology in U.S. Public Schools, 2009 (FRSS 95). Teachers College Record, (8), 1-42. http://www.tcrecord.org/Content.asp?contentid=22277
Tieck, S. & Meinck, S. (2020). Weights and variance estimation for ICILS 2018. In Mikheeva, E., Meyer, S. (Eds.). IEA International Computer and Information Literacy Study 2018 - User Guide for the International Database. Amsterdam: International Association for Educational Achievement (IEA).
Dinaric Region During the COVID-19 Disruption: Schools’ Response Measures and Digital Preparedness
The COVID-19 pandemic caused severe global challenges for education systems and schooling worldwide, with the Dinaric region being no exception. Although the demand for digital competence among teachers and using digital tools and devices in teaching and learning has been present in the region for over a decade, the existing practices could not fully meet the difficulties associated with the COVID-19 pandemic disruption. Centering on the Dinaric area (i.e., Albania, Croatia, Kosovo, Montenegro, North Macedonia, Serbia and Slovenia) concerning the COVID-19 disruption, the paper sheds light on the disruption and response measures in the region against the demand for remote instruction during COVID-19. It examines the diverse response measures and how they were conveyed to different stakeholders, coupled with prior established practices and ease of access to digital devices and their use in teaching and learning.
Data collected during the PIRLS 2021 cycle from students, teachers, school principals and parents and analyses of the PIRLS 2021 Encyclopedia (Reynolds et al., 2022) are used as primary sources in the analyses. Both national reports and responses from school principals indicate that the level of disruption varied at different times of school operation, prompting different types of responses from the schools, often dependent on school location and overall country response to the pandemic. Results also show certain common patterns across the Dinaric region concerning the systems’ wide range of activities to answer the challenge. National Ministries of Education coordinated technical and overall resource support across the most Dinaric countries. Access to different digital resources and access provided to students and teachers somewhat varied. Internet-based resources dominated distant learning resources. Sharing devices within the class was the leading established practice. In some cases, the availability of smartphones outpowered the presence of one’s own or shared computer (tablet), according to the student reports. Teachers’ professional development across the board was focused more on instruction related to digital literacies than integrating technologies into reading instruction. Parents’ perceptions of whether their child’s learning progress has been adversely affected during the COVID-19 disruption varied between and within countries.
References:
Reynolds, K.A., Wry, E., Mullis, I.V.S., & von Davier, M. (2022). PIRLS 2021 Encyclopedia: Education Policy and Curriculum in Reading. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: https://pirls2021.org/encyclopedia
Rating or Ranking: Assessing Teaching Beliefs in an International Online Survey Experiment
International large-scale assessments (ILSA) administer context questionnaires to students, teachers, and principals to collect information about school, classroom and learning conditions. These questionnaires usually consist of a series of rating type items which often face issues such as social desirability, self-presentation, and acquiescence bias (e.g., Lelkes and Weiss, 2015; Schaeffer and Dykema, 2020). There are alternatives to rating scales, such as forced choice items, rankings, anchoring vignettes or situational judgement tasks. Alternative item types can address some issues found with rating item types. It was found that ranking reduces the response style, and it improves data quality (Krosnick & Alwin, 1988). Furthermore, computer-based surveys enable administering items or response scales that are difficult to implement on paper. They provide an opportunity to use functions such as sliders, drag-and-drop, or drop-down menus.
In the field trial of the International Computer and Information Literacy Study (ICILS) 2023, Q-sort was introduced as an alternative question type to assess teaching beliefs of secondary school teachers. Q-sort is a technique that was initially developed for clinical interviews, requiring respondents to arrange and rank a series of cards according to their preference. In this paper, we investigate the feasibility of using the Q-sort (ranking) format when collecting data about teaching beliefs in an international survey and explore and compare the quality and usefulness of the data gathered by two question types, ranking and rating.
We use teacher data from 28 countries participating in ICILS 2023 field trial to investigate the effect of the question format using multiple criteria of data quality. The two question types were randomly distributed across the participating teachers within countries. We compare the two versions by the amount of missing data, distribution of responses, item and scale means, and the correlations between the scale scores and teacher characteristics.
For ranking higher proportion of missing values were observed because the cognitive load is higher for the parallel sorting of a total of 18 items than for the rating items that are answered individually. In addition, we observed more variance in the responses from the ranking than in the rating version. The ranking removes the possibility that respondents can agree equally with all statements and can thus reduce acquiescence bias. Although some advantages were found for the ranking format, we could not suggest the implementation of the current version for further data collection because of the high amount of missing data observed.
References:
Krosnick, J. A., & Alwin, D. F. (1988). A test of the form-resistant correlation hypothesis: Ratings, rankings, and the measurement of values. Public Opinion Quarterly, 52 (4), 526–538.
Lelkes, Y., & Weiss, R. (2015). Much ado about acquiescence: The relative validity and reliability of construct-specific and agree–disagree questions. Research & Politics, 2 (3), 053168015604173.
Schaeffer, N. C., & Dykema, J. (2020). Advances in the science of asking questions. Annual Review of Sociology, 46 , 37–60.
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15:45 - 17:15 | 09 SES 07 A JS: Civic and Citizenship Education in Times of Global Challenges Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Elena Papanastasiou Session Chair: Monica Rosén Joint Symposium |
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09. Assessment, Evaluation, Testing and Measurement
Symposium Civic and Citizenship Education in Times of Global Challenges The purpose of this symposium is to discuss the implications of results from the latest implementation of the International Civic and Citizenship Education Study (ICCS), a comparative survey that was conducted in 2022 collecting data from students, teachers, and schools from 24 education systems (see Schulz, Ainley, Fraillon, Losito, Agrusti, Damiani, & Friedman, 2024). The purpose of ICCS is to investigate how young people are prepared to undertake their roles as citizens in a range of countries. ICCS 2022 is the fifth international IEA study in this area and the third cycle of ICCS. It is explicitly linked through common questions to the previous ICCS cycles undertaken in 2009 (Schulz, Ainley, Fraillon, Kerr & Losito, 2010) and 2016 (Schulz, Ainley, Fraillon, Losito, Agrusti, & Friedman, 2018). In addition to providing an opportunity for an evidence-based discussion of the variation in practices and outcomes of civic and citizenship education the symposium will also provide a forum for discussion of methodological issues related to the cross-cultural study of civic and citizenship education. The symposium will primarily focus on the following aspects related to civic and citizenship education:
Educational systems, school and teachers seek to prepare young people to understand the society they live in, to engage with its political and social issues and become actively involved as citizens in later adult life. There is a consensus that formal education influences the extent of adult engagement in society (Pancer, 2015). The third cycle of the IEA International Civic and Citizenship Education Study, ICCS 2022, provides an opportunity to study both the extent and variation of civic knowledge, attitudes, and engagement based on recent data, and it allows to review changes in civic learning outcomes compared to previous cycles. It also provides a basis for a systematic analysis of contextual factors, at different levels of educational systems that influence civic-related learning outcomes. The symposium includes four papers. The first paper is concerned with an analysis of ICCS 2022 data reflecting lower-secondary students views of their political systems and institutions. The second paper focuses on data about how young people use or expected to use digital technologies for civic engagement. The third paper focuses on how schools and teachers deal with aspects of diversity as part of education. The fourth paper discusses the impact of the COVID-19 pandemic on civic and citizenship education outcomes. References Pancer, S. M. (2015). The psychology of citizenship and civic engagement. Oxford: Oxford University Press. Schulz, W., Ainley, J., Fraillon, J., Kerr, D., & Losito, B. (2010). ICCS 2009 International Report. Civic knowledge, attitudes and engagement among lower secondary school students in thirty-eight countries. Amsterdam: IEA. Schulz, W., Ainley, J., Fraillon, J., Losito, B., Agrusti, G., & Friedman, T. (2018). Becoming citizens in a changing world. IEA International Civic and Citizenship Education Study 2016 International Report. Cham: Springer. Schulz, W., Ainley, J., Fraillon, J., Losito, B., Agrusti, G., Damiani, V. Friedman, T. (2024). Education for Citizenship in Times of Global Change. ICCS 2022 International Report. Cham: Springer. Presentations of the Symposium Lower-secondary Students’ Views of their Political Systems
Over the past decade there have been growing concerns regarding a worldwide “democratic recession” (Diamond, 2015, 2021). These have arisen in response to an increases in authoritarian government practices in some countries as well as new political movements that have undermined support for traditional political parties, and, in some cases have challenged the stability of democratic systems (Boogards, 2017; Mair, 2002). These recent developments raise the question to what extent tendencies toward alienation and an understanding of and preference for populist solutions to government are shared by young people (Gidron & Hall, 2019; Henn & Weinstein, 2006), and whether education has the potential of promoting democratic principles to counteract prospects of growing alienation (Estellés & Catellví, 2020; Sant, 2019).
ICCS results from 2009 and 2016 showed considerable support among lower-secondary students for democratic government and equal opportunities across countries (Schulz et al., 2010, 2018). ICCS 2022 addressed additional aspects related to attitudes toward government and the political system as well as perceptions of potential threats to democracy. ICCS data have also shown that students with higher levels of knowledge have lower levels of trust in institutions in countries where systems are generally perceived as more corrupt and less transparent, while in others there is a positive association (Lauglo, 2012; Schulz et al., 2018, 2024).
Based on data from 19 European countries that participated in ICCS 2022, this paper focuses on how students perceive their political systems. The analyses consist of a descriptive review of student perceptions across different countries and include comparisons with results from adult surveys (Eurobarometer, 2023) and considering information about perceived corruption (Transparency International, 2022) as an important context to explain cross-national variation, as well as of multivariate models explaining variation in student perceptions of the political system with student background variables, trust in civic institutions, as well as school-related variables (such as civic knowledge, civic engagement at school).
ICCS 2022 results show that while majorities of students across countries considered democracy as the best form of government, satisfaction with and critical views of the political system varied considerably. While trust in institutions had consistently positive associations with positive appraisals of the political system, positive associations with civic knowledge tended were only observed in some Northern European countries. More knowledgeable students were also more critical of democratic representation than those with lower levels of civic knowledge in countries, where the democratic systems are generally seen less functional.
References:
Boogards, M. (2017). Lessons from Brexit and Trump: populism is what happens when political parties lose control. Zeitschrift für Vergleichende Politikwissenschaft, 11(4), 513–518.
Diamond, L. (2015). Facing up to democratic recession. Journal of Democracy, 26(1), 141–155.
Diamond, L. (2021) Democratic regression in comparative perspective: scope, methods, and causes. Democratization, 28(1), 22-42.
Estellés, M., & Castellví, J. (2020). The educational implications of populism, emotions and digital hate speech: A dialogue with scholars from Canada, Chile, Spain, the UK, and the US. Sustainability, 12(15), 6034.
European Commission (2023). Democracy. Report – Eurobarometer 522. Retrieved at: file:///C:/Users/acerschulzw/Downloads/Democracy_fl_522_report_en.pdf
Gidron N., & Hall, P. A. (2020). Populism as a Problem of Social Integration. Comparative Political Studies, 53(7), 1027-1059.
Henn, M., & Weinstein, M. (2006). Young people and political (in)activism: Why don’t young people vote?. Policy & Politics, 34(3), 517-534.
Lauglo, J. (2013). Do more knowledgeable adolescents have more rationally based civic attitudes? Analysis of 38 countries. Educational Psychology, 33(3), 262–282.
Schulz, W., Ainley, J., Fraillon, J., Losito, B., Agrusti, G., Damiani, V. Friedman, T. (2024). Education for Citizenship in Times of Global Change. ICCS 2022 International Report. Cham: Springer.
Transparency International (2023). Corruption Perceptions Index 2022. Retrieved at: https://www.transparency.org/en/cpi/2022
Students’ Engagement with Digital Technologies
Digital technologies have redefined the ways in which young people can engage in society. Social media and virtual communities are instrumental in connecting individuals and amplifying arguments. This has led to a new era of civic engagement with digital participation as a form of engagement for students, demonstrated by activities such as organizing of climate protests and raising awareness of the plight of a minority group (de Moor et al., 2020; Cho, Byrne, & Pelter, 2020). There is a perception that developments with technology should usher in an era of greater civic engagement (Dubow, Devaux, & Manville, 2017).
The release of the IEA’s International Civic and Citizenship Education Study (ICCS) 2022 (Schulz et al., 2024) provides new data on students’ knowledge of and engagement in civic and citizenship-related topics from 24, predominantly European based, educational systems. Students completed a test of civic knowledge, followed by a questionnaire that included questions about their current and anticipated future level of engagement with technologies for civic engagement.
Previous cycles of the ICCS study reported increased use of digital technologies that did not necessarily lead to an increase in civic engagement (Schulz et al., 2018; Schulz et al., 2010). Preliminary analyses with ICCS 2022 data revealed only a small proportion of students who frequently engage in more active forms of participation. These students were most likely to be interested in civic issues, but also demonstrated lower levels of civic knowledge (see Schulz et al., 2024).
Building on these earlier results, this paper will use data from ICCS 2022 and earlier cycles to explore changes over time in how students use social media to engage in civic activities, their intentions for doing so in the future, their level of trust in social media and their exposure to learning about the reliability of online information. The paper will also examine the characteristics of students who are currently and more likely to participate in future civic engagement activities using digital technologies.
Our preliminary analysis reveals that while digital technologies open new avenues for civic engagement for young people, there is a notable gap in how they effectively harness these tools. This gap underscores the need for integrating digital literacy with civic education to nurture future citizens to become engaged and knowledgeable as technologies become increasingly ingrained in our everyday lives.
References:
Cho, A., Byrne, J., & Pelter, Z. (2020). Digital civic engagement by young people. UNICEF. https://www.unicef.org/globalinsight/media/706/file/UNICEF-Global-Insight-digital-civic-engagement-2020.pdf
de Moor, J., Uba, K., Wahlström, M., Wennerhag, M., & De Vydt, M. (Eds.). (2020). Protest for a future II: Composition, mobilization and motives of the participants in Fridays For Future climate protests on 20-27 September, 2019, in 19 cities around the world. Södertörn University. https://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-40271
Dubow, T., Devaux, A., & Manville, C. (2017). Civic Engagement: How Can Digital Technology Encourage Greater Engagement in Civil Society? RAND Corporation. Retrieved from http://www.jstor.com/stable/resrep17637
Schulz, W., Ainley, J., Fraillon, J., Kerr, D., & Losito, B. (2010). ICCS 2009 International Report. Civic knowledge, attitudes and engagement among lower secondary school students in thirty-eight countries. Amsterdam: IEA.
Schulz, W., Ainley, J., Fraillon, J., Losito, B., Agrusti, G., & Friedman, T. (2018). Becoming citizens in a changing world. IEA International Civic and Citizenship Education Study 2016 International Report. Cham: Springer.
Schulz, W., Ainley, J., Fraillon, J., Losito, B., Agrusti, G., Damiani, V. Friedman, T. (2024). Education for Citizenship in Times of Global Change. ICCS 2022 International Report. Cham: Springer.
Schools’ and Teachers’ Perceptions of Diversity at School
The growing diversity of student populations at the global level has increasingly prompted schools to develop institutional and instructional practices for building multicultural and inclusive learning contexts (Griffith et al., 2016; Banks, 2020), allowing students and school communities to foster positive attitudes toward diversity (Solhaug, 2018). The concept of diversity embraces a wide range of socially ascribed or perceived differences, such as by sex, age, ethnic/social origin, language, religion, nationality, economic condition, or special learning needs (Daniels & Garner, 1999; Council of Europe, 2008). In this scenario, civic and citizenship education plays a key role for the promotion of knowledge and respect for other cultures and the inclusion of diverse groups into society (Schachner et al., 2019).
ICCS 2022 included diversity as one of its focus areas. The study assessed a wide range of issues related to diversity, that concern the affective-behavioural area (e.g. students’ attitudes toward gender equality and equal rights for immigrants) and the contexts of school and classrooms (Schulz et al., 2023).
The paper will present ICCS 2022 results related to how learning environments acknowledge and deal with diversity. After a brief overview of the relevance of the topic of diversity and inclusion within learning objectives, it will analyse data from teacher and school questionnaires concerning teachers’ self-reported preparedness to teach diversity and inclusiveness, their participation in training programs, schools and classroom activities dealing with diversity, and teachers’ opinions regarding the influence of cultural and ethnic differences and of socioeconomic differences on teaching activities.
Findings showed a positive picture of how schools and teacher deal with diversity, however, there were considerable variations across countries. At the school level, activities to promote teaching to young people from diverse backgrounds, to foster tolerance toward diversity, and to support students with special learning needs were reported widely in most ICCS 2022 countries. Majorities among teachers reported to have conducted activities to address diversity in their classrooms and considered diversity as an important resource for education. More than half of them also reported attendance of pre- or in-service training courses on diversity and inclusiveness (Schulz et al., 2024).
Based on these results, the final section of this contribution considers the interplay between democracy and intercultural dialogue (intended in its broader sense, see Council of Europe, 2018) as well as the implications at the school and classroom level for the promoting a democratic and intercultural learning environment for civic and citizenship education.
References:
Banks, J. A. (2020). Diversity, transformative knowledge, and civic education. Routledge. https://www. routledge.com/Diversity-Transformative-Knowledge-and-Civic-Education-Selected-Essays/ Banks/p/book/9780367863197
Council of Europe (2018). Reference Framework of Competences for Democratic Culture. Council of Europe.
https://www.coe.int/en/web/campaign-free-to-speak-safe-to-learn/referenceframework-of-competences-for-democratic-culture
Daniels, H. and Garner, P. (Eds) (1999). Inclusive Education, World Yearbook of Education. Routledge.
Griffith, R. L., Wolfeld, L., Armon, B. K., Rios, J. & Liu, O. L. (2016). Assessing intercultural competence in higher education: Existing research and future directions. ETS Research Report Series, 2016(2), 16-25. https://doi.org/10.1002/ets2.12112
Schachner, M. K. (2019). From equality and inclusion to cultural pluralism – Evolution and effects of cultural diversity perspectives in schools. European Journal of Developmental Psychology, 16(1), 1–17. https://doi.org/ 10.1080/17405629.2017.1326378
Schulz, W., Ainley, J., Fraillon, J., Losito, B., Agrusti, G., Damiani, V. Friedman, T. (2024). Education for Citizenship in Times of Global Change. ICCS 2022 International Report. Cham: Springer.
Schulz, W., Fraillon, J., Losito, B., Agrusti, G., Ainley, J., Damiani, V., & Friedman, T. (2023). IEA International Civic and Citizenship Education Study 2022 Assessment Framework. Cham: Springer.
Solhaug, T. (2018). Democratic Schools – Analytical Perspectives. JSSE, 17 (1), 2-12. DOI 10.4119/UNIBI/jsse-v17-i1-1791
COVID-19 Containment Policies and Grade 8 Student Civic Outcomes
This research examines the effects of COVID-19 containment policies, particularly school closures and lockdowns, on the civic outcomes of eighth-grade students. The effects of school closures on student performance have been studies in an increasing number of studies but to data few studies have studied the effects on the performance in other domains and on socio-economic outcomes (Betthäuser et al., 2023; Di Pietro, 2023). By analysing trend data from the International Civic and Citizenship Education Study (ICCS; Schulz et al., 2024) and the Oxford COVID-19 Government Response Tracker (OxCGRT), the study investigates how these educational disruptions during the pandemic influenced students' civic knowledge, attitudes, and engagement intentions.
The study uses a longitudinal approach, analysing changes in civic outcomes from 2016 to 2022 using data from almost 100.000 students across 15 education systems worldwide. The ICCS data 2016 provides a baseline of students' civic knowledge and engagement intentions prior to the pandemic. In contrast, the OxCGRT data offers a detailed index of governmental responses to COVID-19, including metrics on school closure durations and lockdown strictness.
Significant findings emerge from this analysis. There is a clear negative correlation between the length of school closures and students' civic knowledge scores. Extended periods of school closure correlate with notable declines in students' comprehension of civic concepts and trust in civic institutions. Furthermore, increased average lockdown stringency is associated with heightened intentions among students to participate in protest activities. These patterns indicate a shift in the landscape of civic engagement, potentially leading to more active forms of civic participation in the future.
The research emphasizes the necessity of considering the wider impacts of the COVID-19 pandemic on civic education. It suggests that while prolonged school closures and strict lockdown measures might be essential for public health, they can inadvertently affect the civic development of young people. This situation calls for a reassessment of civic education strategies during crises to ensure the sustainability of high-quality civic learning experiences.
Conclusively, the study adds valuable insights to the discourse on the educational consequences of the pandemic. By providing empirical evidence of the direct connection between COVID-19 containment policies and students' civic outcomes, it underlines the importance of sustaining civic education amidst global challenges
References:
Betthäuser, B. A., Bach-Mortensen, A. M., & Engzell, P. (2023). A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nature Human Behaviour, 7(3), 375–385.
Di Pietro, G. (2023). The impact of Covid-19 on student achievement: Evidence from a recent meta-analysis. Educational Research Review, 39, 100530.
Schulz, W., Ainley, J., Fraillon, J., Losito, B., Agrusti, G., Damiani, V. Friedman, T. (2024). Education for Citizenship in Times of Global Change. ICCS 2022 International Report. Cham: Springer
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Date: Thursday, 29/Aug/2024 | |
9:30 - 11:00 | 09 SES 09 A: Analyzing the Potentials of Digitalization in an Age of Uncertainty Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Ramona Lorenz Session Chair: Rolf Strietholt Symposium |
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09. Assessment, Evaluation, Testing and Measurement
Symposium Analyzing the Potentials of Digitalization in an Age of Uncertainty Digitalization has altered almost all areas of life and has increasingly been established in schools. The expected potentials are manifold with regard to affective variables such as increased motivation to learn, the optimization of learning processes and the improvement of students’ competencies (Voogt et al., 2018). However, research does not consistently show positive correlations between the use of digital media (e.g. different tools, purposes or frequencies) and increased outcomes. In addition, the phase of interruption of regular teaching during the COVID-19 pandemic is noteworthy, as digitalization has maintained teaching processes in many places during this time. Research on disruption of schooling has mainly shown a decline in competencies, and indicated that digitalization has not been able to help prevent a drop in competencies to the same extent in all countries (Di Pietro, 2023; Kennedy & Strietholt, 2023; König & Frey, 2022). Against this background, the papers of the symposium will examine the overarching question of what potential the use of digital media has in the classroom in an international comparison and which factors are relevant for its effectiveness. The first presentation by Simon Skov Fougt and Katja Neubert will address the debates about digitization, especially in the Nordic countries after the latest results of PIRLS and PISA showing a decline in students’ competencies. In these rather digitized countries, the decline in the competencies measured could not be fully compensated for by digitization, which was the expectation on many sides. The paper discusses an explanatory approach based on PIRLS data with a special focus on Denmark amongst the Nordic countries to the question how digitalization can nevertheless be seen as a positively influencing factor on reading literacy in fourth grade during the pandemic. The second contribution by Ramona Lorenz, Ulrich Ludewig and Nele McElvany will broaden the perspective to several European countries. Given inconsistent findings on the effects of digital media depending on the purpose of use and differences according to the socio-economic background of the students, the paper explores the question of how digital schoolwork is related to reading literacy in fourth grade and if differences between countries and between European regions can be found. With data form PIRLS 2021 a multi-group two level model with cross-level interaction shows that no systematic negative relations between digital reading and reading literacy can be found but some positive relations at specific levels or in specific countries appear. Regional differences will be discussed in depth. The third contribution by Fazilat Siddiq and Ronny Scherer consistently provides insights in how digital media should be used in a purposeful way so that digitalization can have the desired effect on students` competencies. Even if education systems worldwide have integrated digitalization, there is still a huge need for teachers to gain a better understanding and professional knowledge for implementing digital media in a reasonable manner. A theory-driven and research-based teaching program was developed to support teachers in enhancing lower secondary students` 21st century skills. By means of thematic analysis and network analysis, learning experiences within this teaching program are examined. Important implications for digitalization in schools complete the contribution. The symposium concludes with a comprehensive discussion and thorough appraisal of the three contributions by Rolf Strietholt. References Di Pietro, G. (2023). The impact of covid-19 on student achievement: Evidence from a recent meta-analysis. Educational Research Review, 100530. Kennedy, A. I., & Strietholt, R. (2023). School Closure Policies and Student Reading Achievement: Evidence Across Countries. Educational Assessment, Evaluation and Accountability, 35, 475–501. König, C., & Frey, A. (2022). The impact of covid-19-related school closures on student achievement¬ – a meta-analysis. Educational Measurement: Issues and Practice, 41(1), 16–22. Voogt, J., Knezek, G., Christensen, R., & Lai, K.-W. (2018). Second Handbook of Information Technology in Primary and Secondary Education. Springer. https://doi.org/10.1007/978-3-319-71054-9 Presentations of the Symposium Digitalization or not?
PIRLS 2021 and PISA 2022 showed significant declines in Danish students’ reading competence (Fougt et al. 2023; Gissel 2023), as well as we internationally have seen the largest decline ever.
One proven factor here is the covid 19-pandemic (Kennedy & Strietholt 2023); however the waste majority of the public debate in Denmark following PIRLS 2021 and PISA 2022 focuses on digitalization, also at governmental level. The Danish Minister for Culture argued to abandon digitalization in schools, our Prime Minister claimed that smartphones were the biggest threat towards our children, and The Minister for Education gave a public excuse for ‘the digital experiment’ in schools. Several opinions makers and some researchers argue for screen restrictions in education (eg. Ågård, 2021; Rashid et al. 2024).
This paper challenges this standing point with the hypothesis that the waste digitalization of Danish schools and the habituation of teachers and students might have helped to prevent a larger decline in Danish students’ reading competence during the pandemic, as schools relatively easy could transfer to online teaching. The paper mainly draws on PIRLS data and focuses on the comparable Nordic Countries with the following research question: How can digitalization be seen as a positively influencing factor on students reading competence during the pandemic?
In 2022, Denmark was world ranking no. 1 for the third successive time in the latest UN E-government survey on digitalization (UN 2022). PIRLS 2016-2021 data show that Danish schools have been more digitalized for a longer time compared to all other PIRLS participants, also the other Nordic countries. Both in PISA and PIRLS we see a minor decline in students’ reading competence in Denmark as compared to the other Nordic countries, and at the same time, Denmark was by far the most school-closed country within the Nordic countries during the pandemic.
This paper discusses this explanatory approach, and it is also including development in SES and in spoken language at home as other possible explaining factors.
References:
Fougt, S. S., Neubert, K., Kristensen, R. M., Gabrielsson, R., Molbæk, L., & Kjeldsen, C. C. (2023). Danske elevers læsekompetence i 4. klasse: Resultater af PIRLS-undersøgelsen 2021. Aarhus Universitetsforlag
Gissel, S.T. (2023). PISA 2022 LÆSNING. Delrapport. VIVE https://www.uvm.dk/-/media/filer/uvm/int/231204-pisa-2022-laesning-pdf-ua.pdf
Kennedy, A. I., & Strietholt, R. (2023). School Closure Policies and Student Reading Achievement: Evidence Across Countries.
Rashid, I., Bro, K. B. & Brixtofte, M. (2024). Skærmsund. En fire-ugers guide til sundere skærmvaner. Gyldendal
UN (2022). UNITED NATIONS E-GOVERNMENT SURVEY 2022. The Future of Digital Government.
Department of Economic and Social Affairs https://publicadministration.un.org/egovkb/en-us/Reports/UN-E-Government-Survey-2022
What is the Relationship between Digital Schoolwork and Reading Literacy? Findings from PIRLS 2021 for Primary Education in European Countries
Reading literacy is an important foundation for educational achievement, social participation, and professional life. Digitalization is expanding to a considerable extent the reading opportunities that students have in their everyday lives, but also in the school context. The use of digital media is considered to have a wide range of potential benefits for learning. Nevertheless, research does not consistently point out positive effects of digital tools and e.g. has shown that digital reading, at least during leisure time, does not necessarily add to reading comprehension (Altamura et al., 2023). Other studies indicated a negative relation of the amount of daily use of digital devices with reading comprehension that could be compensated by a supportive use by the teachers within digital reading projects (Salmerón et al., 2022). Overall, international large-scale assessments show that in some countries more digital reading time in school is associated with higher reading literacy. In some education systems, however, a negative correlation is found (Lorenz et al., 2023). Furthermore, there is an ongoing discussion about the use of digital media for reading instruction and how it`s use differs between students with different socio-economic backgrounds. This discussion is driven from findings of particularly large learning deficits among children from low socio-economic backgrounds while learning digitally during the COVID-19 pandemic and on country level a larger gap between middle-income countries relative to high-income countries (Betthäuser et al., 2022).
This leads to the question on digitization for school purposes in an international comparison: What relation of digital schoolwork with reading literacy can be found in fourth grade across European countries?
Results indicate an overall positive association of the amount of digital schoolwork (finding and reading information; preparing reports and presentations) and reading literacy at the country level for all considered European countries in PIRLS 2021. Regional differences are that Northern European countries have both a higher level of digital reading for schoolwork and reading literacy, whereas Western European countries have a lower level of digital reading along with lower reading literacy. A multi-group two level model with cross-level interaction revealed effects at the class level, primarily in Eastern and Southern European countries. However, no effects at any level remain statistically significant after controlling for socioeconomic background and spoken language at home (other than test language). Results show no evidence in support of a negative association between digital schoolwork and reading literacy. Inequality between European regions will be discussed.
References:
Altamura, L., Vargas, C., & Salmerón L. (2023). Do new forms of reading pay off? A meta-analysis on the relationship between leisure digital reading habits and text comprehension. Review of Educational Research. https://doi.org/10.3102/00346543231216463
Betthäuser, B.A., Bach-Mortensen, A.M., & Engzell, P. (2022). A systematic review and meta-analysis of the evidence on learning during the covid-19 pandemic. Nature Human Behaviour, 7(3), 375–385.
Lorenz, R., Goldhammer, F. & Glondys, M. (2023). Digitalisierung in der Grundschule [Digitalization in elementary school]. In N. McElvany, R. Lorenz, A. Frey, F. Goldhammer, A. Schilcher & T. C. Stubbe (Hrsg.), IGLU 2021 – Lesekompetenz von Grundschulkindern im internationalen Vergleich und im Trend über 20 Jahre [PIRLS 2021 - Reading literacy of primary school children in an international comparison and trend over 20 years] (S. 197–214). Münster: Waxmann.
Salmerón, L., Vargas, C., Delgado, P., & Baron, N. (2022). Relation between digital tool practices in the language arts classroom and reading comprehension scores. Reading and Writing, 36, 175–194. https://doi.org/10.1007/s11145-022-10295-1
Students’ Reflections and Experiences with a Novel Teaching Program on Computational Thinking and Collaborative Learning - A Design-based Research Study
In recent years, several countries have undergone major curriculum revisions, which has resulted in the inclusion of interdisciplinary competence areas such as digital competence, computational thinking, critical thinking, problem solving and collaborative learning into the compulsory K-12 curricula (Erstad & Siddiq, 2023). Such overarching competence areas are oftentimes labelled 21st century skills (Voogt & Roblin, 2012). Although the intentions in the curriculum are positive, there is currently little research-based knowledge about how such competences can be taught and assessed, and teachers report lack of access to professional development and teaching materials (Erstad & Siddiq, 2022; Kravik et al., 2022). To meet some of these challenges, the TEACH21st-project (Teaching and transfer effects of 21st century skills – collaborative problem solving in digital environments) was initiated in 2019 with the aim to develop teaching materials and practices that are knowledge- (theory-driven) and research-based. More specifically, applying a teacher design team approach (Becuwe et al., 2016) teachers, teacher educators, student teachers and researchers worked together to develop a teaching resource aimed at developing lower secondary students’ computational thinking and collaborative problem-solving competences. This program has been developed, piloted and revised through several iterations.
The final teaching program includes materials (games, charts, tasks etc.) for the teachers and students, and practices (e.g., use of analogue and computer programming in combination to teach computational thinking, how to teach collaborative learning and use it as a pedagogical approach). The program consists of four modules that are built on the principles of: relevance (target learning goals in the curriculum); inclusion (all students should be able to participate independent of their previous knowledge, and provide adaptive teaching); engagement and activity (include engaging and fun tasks, involving physical activity and hands-on assignments); collaborative learning (students need to learn to collaborate and the tasks require positive dependence); and progression (the tasks move towards more advanced levels). Finally, this program has been conducted in 32 classes by their teachers (N=16) after attending a one-day professional development workshop.
In this study, we will examine how the 9th grade students (N = 460) experience learning within this teaching program. The data consist of the students' reflection notes conducted after each of the four modules and observations (N = 24 classes). The data has been analyzed through a combination of thematic analysis (Braun & Clark, 2021) and network analysis (Epskamp et al., 2018). Results and implications will be discussed.
References:
Becuwe, H., Tondeur, J., Pareja, R. N., Thys, J., & Castelein, E. (2016). Teacher design teams as a strategy for professional development: The role of the facilitator. Educational Research and Evaluation, 22(3-4).
Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. London: Sage
Epskamp, S., Maris, G., Waldorp, L. J., & Borsboom, D. (2018). Network psychometrics. In The wiley handbook of psychometric testing (pp. 953–986). Wiley. https://doi.org/10.1002/9781118489772.ch30.
Erstad, O., & Siddiq, F. (2023). Educational assessment of 21st century skills—novel initiatives, yet a lack of systemic transformation, Editor(s): Robert J Tierney, Fazal Rizvi, Kadriye Erkican, International Encyclopedia of Education (Fourth Edition), Elsevier, 2023, Pages 245-255, ISBN 9780128186299, https://doi.org/10.1016/B978-0-12-818630-5.09038-2.
Kravik, R., Berg, T., & Siddiq, F. (2022). Teachers’ understanding of programming and computational thinking in primary education – A critical need for professional development. Acta Didactica Norden. https://doi.org/10.5617/adno.9194
Voogt, J., & Roblin, N.P., (2012). A comparative analysis of international frameworks for 21st century competences: implications for national curriculum policies. J. Curric. Stud. 44 (3), 299–321. https://doi.org/10.1080/00220272.2012.668938
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12:45 - 13:30 | 09 SES 10.5 A: *** Postponed *** NW 09 Network Meeting new time to be confirmed Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Monica Rosén Network Meeting |
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09. Assessment, Evaluation, Testing and Measurement
Paper NW 09 Network Meeting University of Gothenburg, Sweden Presenting Author:Networks hold a meeting during ECER. All interested are welcome. Methodology, Methods, Research Instruments or Sources Used . Conclusions, Expected Outcomes or Findings . References . |
13:45 - 15:15 | 09 SES 11 A: Bridging Gaps and Improving the Future: Transforming Challenges into Opportunities through Large-Scale Assessments Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Monica Rosén Network Keynote |
15:45 - 17:15 | 09 SES 12 A: Examining Leadership, Student Outcomes, and Academic Trajectories Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Gasper Cankar Paper Session |
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09. Assessment, Evaluation, Testing and Measurement
Paper Predicting school failure in Sweden: A longitudinal approach 1University of Gothenburg, Sweden; 2Umeå University Presenting Author:Previous research has identified that cognitive ability and socioeconomic status (SES) indicated by parental education, occupation, or income (Marks, 2013) are the strongest predictors of school outcomes. Cognitive ability is the strongest predictor of school achievement, with correlations around .60-.70 (Gustafsson & Balke, 1993), while SES typically correlates around .30-.40 with school achievement (Sirin, 2005). Longitudinal investigations of the strength of the associations concluded that the influence of SES is declining (Marks, 2013). However, in Sweden the strength of the association between SES and achievement has increased during the last decades (Gustafsson & Yang Hansen, 2018), suggesting that equity of schooling outcomes has deteriorated. Another important factor influencing school outcomes is gender. Girls tend to outperform boys in terms of grades internationally (Dwyer & Johnson, 1997), and this is true for Swedish students as well. Even more concerningly, boys are more at risk of dropping out of school in Sweden (World Bank, 2024). In Sweden, compulsory education ends in the school year 9, while in the optional upper secondary school, there are 18 regular national programs of three years to choose from, six of which are preparatory for higher education such as university, and twelve of which are vocational. While entrance requirements vary between programs, all of them demand students to have passing grades in Swedish/Swedish as a second language, English, an d mathematics from their final year of compulsory schooling. The main question, which can be investigated for all birth cohorts between 1948 and 2004, is the relative importance of cognitive ability, social background, cultural background, and gender as determinants of school failure and general school achievement, and how this varies as a function of school characteristics and societal factors. Methodology, Methods, Research Instruments or Sources Used We define four levels of school failure: premature failure, i.e., no grades or low grades in year 6; early failure, i.e., no grades in year 9; midway failure, i.e., not eligible for upper secondary school, and late failure, i.e., no final grades/exam within three years of finishing upper secondary school. Starting with a basic model including grade point average (GPA) from compulsory school, along with cognitive abilities from grade 6 and background variables, predicting school failure. The differentiation of students into different programs will be dealt with through a dummy variable approach and/or through fitting separate models for different programs or groups of programs. As for the compulsory school model, explanatory variables will be added in the next step, using the same sources of information. Longitudinal data from two sources are used; the GOLD and the UGU databases which partially overlap in that the UGU participants in the seven birth cohorts 1972, 1977, 1982, 1987, 1992, 1998, and 2004 also are included in GOLD. The data allow a large number of cohort comparisons, focusing on curricular and organizational aspects, and on societal changes such as increasing economic inequity and school segregation. Both comprehensive school and upper secondary school will be investigated. Conclusions, Expected Outcomes or Findings The empirical results will be discussed in light of the educational research and political discourse that preceded the reforms, in which both gender and cognitive ability were considered to be of key importance. Along with descriptions of the changes in the school organization and school curricula, this study will contribute to an understanding of the three levels of curriculum (the intended, the implemented, and the achieved curriculum which in interplay with social and home background factors determine children’s opportunity to learn (McDonnell, 1995); and to the changes in the school system that followed with school reforms. References Dwyer, C. A., & Johnson, L. M. (1997). Grades, accomplishments, and correlates. In Gender and fair assessment (pp. 127–156). Lawrence Erlbaum Associates Publishers. Gustafsson, J.-E., & Balke, G. (1993). General and specific abilities as predictors of school achievement. Multivariate Behavioral Research, 28(4), 407–434. https://doi.org/10.1207/s15327906mbr2804_2 Gustafsson, J.-E., & Yang Hansen, K. (2018). Changes in the impact of family education on student educational achievement in Sweden 1988-2014. Scandinavian Journal of Educational Research, 62(5), 719–736. https://doi.org/10.1080/00313831.2017.1306799 Marks, G. N. (2013). Education, social background and cognitive ability: The decline of the social. Routledge. https://www.routledge.com/Education-Social-Background-and-Cognitive-Ability-The-decline-of-the-social/Marks/p/book/9781138923225 McDonnell, L. M. (1995). Opportunity to learn as a research concept and a policy instrument. Educational Evaluation and Policy Analysis, 17(3), 305–322. https://doi.org/10.3102/01623737017003305 Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. World Bank. (2024). Education statistics—All indicators. https://databank.worldbank.org/source/education-statistics-%5e-all-indicators 09. Assessment, Evaluation, Testing and Measurement
Paper Determinants of School Failure in Sweden 1University of Gothenburg, Sweden; 2Umeå University, Sweden Presenting Author:Previous research has identified two main individual-level determinants of school outcomes: cognitive ability and socioeconomic status (SES) indicated by parental education, occupation, or income (Marks, 2013). Cognitive ability is the strongest predictor of school achievement, with correlations around .60-.70 (Gustafsson & Balke, 1993), while SES typically correlates around .30-.40 with school achievement (Sirin, 2005). However, there are substantial country differences in these relationships, and longitudinal investigations of the strength of the associations have also been observed, and Marks (2013) concluded that the influence of SES is declining. On the contrary, for Sweden an increase in the strength of the association between SES and achievement has been observed during the last decades (Gustafsson & Yang Hansen, 2018), suggesting that equity of schooling outcomes has deteriorated. Moreover, gender differences have been observed in Sweden in terms of grades in line with international trends (Dwyer & Johnson, 1997), and boys are more at risk of dropping out of school in Sweden (World Bank, 2024). Recently, much attention has been devoted to personality characteristics as determinants of success and failure in school, such as conscientiousness (Almlund et al., 2011), grit (Duckworth et al., 2007), and growth mindset (Dweck, 2008). Other individual characteristics too have been shown to contribute to school achievement. Prominent examples are self-efficacy (Bandura, 1997), self-concept(Bong & Skaalvik, 2003), and intrinsic and extrinsic motivation (Ryan & Deci, 2000). In Sweden, compulsory education involves school years 1 to 9, while in the optional upper secondary school, there are eighteen regular national programs of three years to choose from, six of which are preparatory for higher education such as university, and twelve of which are vocational. While entrance requirements vary between programs, all of them demand students to have passing grades in Swedish/Swedish as a second language, English, and mathematics from their final year of compulsory schooling. The main aim of this study is to investigate individual and social determinants in the development of school failure using a longitudinal approach for two birth cohorts that have followed the same curriculum, born in 1998 and 2004. Methodology, Methods, Research Instruments or Sources Used We define four levels of school failure: premature failure, i.e., no grades or low grades in year 6; early failure, i.e., no grades in year 9; midway failure, i.e., not eligible for upper secondary school, and late failure, i.e., no final grades/exam within three years of finishing upper secondary school. Information about gender and SES will be used to explain individual variation in cognitive abilities in school year 6. This model will be extended with results on national tests and will be used to predict achievement and school failure in school year 9. Next, explanatory variables derived from the student questionnaires (e.g., self-concept/self-efficacy, achievement goal preferences, motivation, coping, self-reported mental health) and registers (e.g., school relocations, participation in special needs education, mental health problems) will be added to the model to investigate to what extent they affect the risk for school failure. For upper secondary school, a similar approach will be taken, starting with a basic model including grade point average (GPA) from compulsory school, along with cognitive abilities from school year 6 and background variables, predicting school failure in the form of dropout or low grades. The differentiation of students into different programs will be dealt with through a dummy variable approach and/or through fitting separate models for different programs or groups of programs. Conclusions, Expected Outcomes or Findings In combination, the results from the models for compulsory and upper secondary school will provide good coverage of individual and social determinants of school failure. References Almlund, M., Duckworth, A. L., Heckman, J., & Kautz, T. (2011). Personality psychology and economics. In Handbook of the economics of education: Vol. 4 (pp. 1–181). Elsevier. https://econpapers.repec.org/bookchap/eeeeduchp/4-1.htm Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman. Bong, M., & Skaalvik, E. M. (2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15(1), 1–40. https://doi.org/10.1023/A:1021302408382 Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087 Dweck, C. S. (2008). Mindset: The new psychology of success (Ballantine Books trade pbk. ed.). Ballantine Books. Dwyer, C. A., & Johnson, L. M. (1997). Grades, accomplishments, and correlates. In Gender and fair assessment (pp. 127–156). Lawrence Erlbaum Associates Publishers. Gustafsson, J.-E., & Balke, G. (1993). General and specific abilities as predictors of school achievement. Multivariate Behavioral Research, 28(4), 407–434. https://doi.org/10.1207/s15327906mbr2804_2 Gustafsson, J.-E., & Yang Hansen, K. (2018). Changes in the impact of family education on student educational achievement in Sweden 1988-2014. Scandinavian Journal of Educational Research, 62(5), 719–736. https://doi.org/10.1080/00313831.2017.1306799 Marks, G. N. (2013). Education, social background and cognitive ability: The decline of the social. Routledge. https://www.routledge.com/Education-Social-Background-and-Cognitive-Ability-The-decline-of-the-social/Marks/p/book/9781138923225 Ryan & Deci. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020 Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. World Bank. (2024). Education statistics—All indicators. https://databank.worldbank.org/source/education-statistics-%5e-all-indicators 09. Assessment, Evaluation, Testing and Measurement
Paper Do They Achieve What They Aimed For? Trajectories and Achieved School-leaving Certificates of Retained Students. 1Ludwig Maximilian University Munich; 2TU Dortmund / IFS Presenting Author:Nationally and internationally, grade retention is a highly controversial measure to homogenise students with different competence levels. In Germany, some federal states (e.g., Hamburg and Berlin) have already abolished grade retention. In Bavaria and Bremen, however, grade retention rates are above the national average (2.3%, see Statistisches Bundesamt, 2018). In an international comparison, the rate of German pupils who have been retained at least once in the course of their educational career is above the OECD-average (Germany: 19.6%, OECD: 12.2 %; ꭓ² = 29558.56, df=1, p<.001; own calculations). Empirical evidence on the effectiveness of grade retention is still insufficient. International studies (Goos et al., 2021) showed that there are short-term improvements in performance after being retained, but they decrease in the medium and long term. Especially in highly hierarchically structured education systems such as Germany, Belgium, the Netherlands or Switzerland, grade retention is least effective (ibid.). In Germany, only a few reliable studies allow concrete statements about the effectiveness of grade retention: Positive effects of grade retention on students’ performance development were not proven (Ehmke et al., 2017; Fabian, 2020). Beyond this, Fabian (2020) also showed that there was no significant improvement in school grades of retained students. Marsh et al. (2017), however, found an increase in retained students’ math performance after repetition. With regard to the achieved school-leaving qualification, Bellenberg (1999) showed that grade retention is very often associated with school dropout and/or downward change of school track, thus reducing the probability of achieving higher school-leaving qualifications for repeating students. Demski and Liegmann (2014) reported only minor differences between repeaters and promoted students. Given the theoretical assumptions of credentialism (Bills, 2003), DiPrete et al.'s (2017) findings from an international comparison are noteworthy: In Germany, school-leaving certificates are particularly important for future success on the training and labour market. At the same time, objective competencies and obtained certificates are often incongruent (Brändle & Pohlmann, 2021). Empirical findings showed that particularly students with low qualifications successfully enter the training market if they have good grades and high educational aspirations – regardless of their cognitive and social skills (Holtmann et al., 2017). Due to the strong correlation between school-leaving qualifications and success on the training and labour market, the present study investigates whether grade retention has advantages or disadvantages for retained students. Previous research findings suggest that being retained might lead to lower school-leaving qualifications. Methodology, Methods, Research Instruments or Sources Used Data basis for the present analyses was the German National Educational Panel Study (NEPS, starting cohort 3; Blossfeld et al., 2011). The sample was initially representative of Grade 5 students in Germany and consisted of n = 6,491 students. For the present analyses, we excluded students in school tracks where different school-leaving certificates can be obtained (i.e., students from tracks with several educational programmes, comprehensive tracks). Students for whom no information on the attended school track was available were also excluded from analysis. This resulted in an analysis sample of n = 4,371 students, 118 of whom were retained in Grade 7 (2.5%). We treated missing values using multiple imputation (m = 55 ) in R 4.2.2 (R Core Team, 2023) via the package mice (van Buuren & Groothuis-Oudshoorn, 2011), accounting for the clustered data structure and the frequently non-normal data distribution. Based on this analysis sample, we calculated propensity scores using the Rubin Causal Model (Rubin, 1974) and conducted propensity score matching (Rosenbaum & Rubin, 1983) based on objective competencies in reading and math before grade retention as well as information based on key background characteristics provided by students and parents. Grade repeaters were then matched with non-repeaters (full matching, caliper = .10). This allowed us to compare retained students with similar students regarding key background characteristics, but who were regularly promoted. The highest achieved school-leaving certificate, operationalised by CASMIN (König et al, 1988), was then analysed visually. In addition, we created dichotomous dummy variables for (a) a qualification lower than the usual qualification in the respective school type, (b) a qualification appropriate to the school type (i.e., Certificate of Secondary Education at lower tracks [Hauptschule], General Certificate of Secondary Education [GCSE] at intermediate tracks [Realschule], A-level [Abitur] at academic tracks [Gymnasium]) and (c) a qualification higher than the usual qualification at the respective track (i.e., GCSE at lower tracks or A-level after being retained in intermediate tracks). We conducted logistic regressions to analyse the effect of grade repetition on the adequacy of the school-leaving certificate. Results show that a grade retention seems to reduce the chance of achieving a track-equivalent qualification (OR_fit = .92, p = .089). Grade retention had no influence on the chance of achieving a higher qualification than usual in the respective school track. However, grade repetition increased the chance of obtaining a lower qualification (OR_lower = 1.12, p < .001). Conclusions, Expected Outcomes or Findings In summary, we found that students who were retained in Grade 7 were less likely to achieve a school-leaving certificate that is appropriate to their attended school track. Also – in line with findings from Bellenberg (1999) – the risk of achieving a lower school-leaving certificate increased when students were retained. Since there is some evidence that grade retentions do not lead to better grades (Fabian, 2020) – which is one of the most important goals of grade retention – the findings of Holtmann et al. (2017) become even more important: Even with low qualifications, but good grades and high educational aspirations, young adolescents’ successful transition to the training and labour market is more likely. In that regard, as can be assumed based on the findings of the present study, grade retention fails its goal to help students strengthen their academic outcomes and their chances of obtaining a track-adequate school-leaving certificate. Thus, further investigation is needed to analyse whether students with lower school-leaving qualifications than appropriate to the respective attended school track reach this lower qualification at least with better grades. The reason why previous studies (e.g., Demski & Liegmann, 2014) did not find differences in educational attainment for repeaters and non-repeaters might be that in the past decades, “irregular” – i.e., non-linear – trajectories became more common. Thus, the achievement gap between repeaters and non-repeaters after compulsory education might be narrowed by further training. In their study, Demski and Liegmann (2014) used retrospective information of participants, so the effect of further training could be accounted for. However, we could not address these effects with the data of the present study, yet it will hopefully be possible with the ongoing studies of the NEPS in the future. References Bellenberg, G. (1999). Individuelle Schullaufbahnen: eine empirische Untersuchung über Bildungsverläufe von der Einschulung bis zum Abschluss. Weinheim: Juventa. Bills, D. B. (2003). Credentials, signals, and screens: Explaining the relationship between schooling and job assignment. Review of Educational Research, 73(4), 441-469. Blossfeld, H. P., & Von Maurice, J. (2019). Education as a lifelong process (pp. 17-33). Wiesbaden: Springer Fachmedien. Brändle, T., & Pohlmann, B. (2021). Alles nur eine Frage der Kompetenz? Leistungs- und Chancengerechtigkeit bei der Vergabe von Schulabschlüssen und Abschlussnoten. Zeitschrift für Soziologie, 50(1), 58-77. Demski, D., & Liegmann, A. B. (2014). Klassenwiederholungen im Kontext von Schul- und Berufsbiographien. In: A. B. Liegmann, I. Mammes & K. Racherbäumer (eds.). (2014). Facetten von Übergängen im Bildungssystem. Nationale und internationale Ergebnisse empirischer Forschung (pp. 173-189). Münster: Waxmann (2014) DiPrete, T. A., Eller, C. C., Bol, T., & Van de Werfhorst, H. G. (2017). School-to-work linkages in the United States, Germany, and France. American Journal of Sociology, 122(6), 1869-1938. Ehmke, T., Sälzer, C., Pietsch, M., Drechsel, B., & Müller, K. (2017). Kompetenzentwicklung im Schuljahr nach PISA 2012: Effekte von Klassenwiederholungen. Zeitschrift für Erziehungswissenschaft, 2(20), 99-124. Fabian, P. (2020). Leistungskonsolidierung, Leistungssteigerung-oder etwas ganz anderes? Die Effekte einer Klassenwiederholung auf die Leistungsentwicklung. Münster: Waxmann. Goos, M., Pipa, J., & Peixoto, F. (2021). Effectiveness of grade retention: A systematic review and meta-analysis. Educational Research Review, 34, 100401. Holtmann, A. C., Menze, L., & Solga, H. (2017). Persistent disadvantages or new opportunities? The role of agency and structural constraints for low-achieving adolescents’ school-to-work transitions. Journal of Youth and Adolescence, 46, 2091-2113. König, W., Lüttinger, P., & Müller, W. (1988). A comparative analysis of the development and structure of educational systems: Methodological foundations and the construction of a comparative educational scale. Mannheim: Universität Mannheim, Institut für Sozialwissenschaften. Marsh, H. W., Pekrun, R., Parker, P. D., Murayama, K., Guo, J., Dicke, T., & Lichtenfeld, S. (2017). Long-term positive effects of repeating a year in school: Six-year longitudinal study of self-beliefs, anxiety, social relations, school grades, and test scores. Journal of Educational Psychology, 109(3), 425-438. R Core Team (2023). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688-701. |
17:30 - 19:00 | 09 SES 13 A: Exploring Innovative Approaches to Assessment and Feedback Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Tracy Whatmore Paper Session |
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09. Assessment, Evaluation, Testing and Measurement
Paper One Attempt to Measure Collaboration Between Students During Group Work 1University of Belgrade, Faculty of Philosophy, Serbia; 2Institute for Educational Research, Belgrade Presenting Author:Collaborative problem-solving (CPS) skills have become an inevitable part of workforce readiness in contemporary society (Graesser et al., 2018). Numerous studies have shown that CPS is a powerful learning tool that could lead to more creative, efficient and comprehensive solutions than other approaches (Fiore, 2008). Sometimes it is the only possible way to solve complex problems. That is not surprising that the Organisation for Economic Cooperation and Development (OECD, 2019) includes the development of collaboration skills in the education development agenda for 2030. A lot of attempts were made to introduce CPS in everyday educational practice. However, the benefits of the CPS often fail to be achieved (Le et al., 2018). Collaborative problem-solving is usually defined as working together toward a common goal (Hesse et al., 2015). It includes interdependency between group members in joint activity and shared responsibility for the group results. Despite many contributions, there is a lack of instruments for measuring student-specific versions of collaborative processes during group work (Wang et al., 2009). The focus is often on the effect of this type of learning assessed through achievement data (Jansen, 2010) while the quality of the collaborative process is beyond research aims. Usually, self-assessment tools were used for this purpose accompanied by methodological limitation of subjective assessments. In these attempts, students' perceptions and experience with CPS are not distinguished from the quality of collaboration present during group work. Also, collaboration is assessed as an individual skill separate from its nature as a joint activity. This study aims to construct an instrument for assessing the quality of collaboration between students while trying to solve a complex problem. This study is part of the larger project PEERSovers with a focus on designing an evidence-based training program for enhancing high-school students' collaborative skills. The theoretical background for constructing the instrument involves a qualitative systematic literature review of 160 articles published between 2021 and 2022 that investigated differences between productive and unproductive peer collaboration (Baucal et al., 2023). Four aspects of peer interaction were identified as a result of this analysis. The first covers cognitive exchange between group members. Research shows that productive CPS includes argumentative dialogue between team members and constructive evaluation of ideas. Also, the effort is made to move from the personal opinion toward a shared understanding of the problem. Well-known Mercer studies (for example, Mercer et al., 2019; Mercer & Dawes, 2014) pointed out that exploratory talk during group work enhances the co-construction of joint cognitive activity, fosters critical thinking skills and contributes to the overall learning experience in educational settings. The second aspect refers to the emotional aspect of group work manifested through group atmosphere, presence of conflicts and tension, group cohesion, members' sense of belonging, mutual tolerance and empathy. In unproductive groups, members are disrespected and prevented from fully participating. Often the inequality in power is present during group work. Some members dominate in the dialogue space and prevent others from contributing. The third and fourth aspects are dedicated to two domains of group regulation: task activity regulation (time management, coordination of the activity, planning group activity, task-focus approach) and relationship regulation (group norms, sharing responsibility, dividing the assignment, efficient conflict management etc.). An unproductive group is often characterised by lots of off-task behaviour. Usually, few or only one participant takes overall responsibility for group work. We tried to operationalize these four aspects as dimensions of the instrument used for evaluating a CPS.
Methodology, Methods, Research Instruments or Sources Used Sample: Participants were selected from 12 secondary schools in Belgrade (6 vocational and 6 general/gymnasium schools). School counsellors, guided by the students’ preferences, formed triads of male or female students from the same class. The sample included 64 groups of three students (192 participants), of which 37 were girls and 27 were men. All students involved in the research had formal parental consent and their assent. Procedure: Students’ triads participate in CPS sessions trying to solve a single but complex real-life problem. Problem tasks used in this study were related to four community-relevant themes: (1) ecology (2) teen behaviour, (3) media, and (4) education. The assigned task for each group involved generating a written solution to the presented problem, subsequently assessed for its quality. The entire interaction during the CPS process was video-recorded video for subsequent analysis. CPS sessions were conducted on school premises during the regular school day. The average duration of a CPS session was 97 minutes (SD = 30; range = 19-167). Instruments: CPS observational grid (CPS-OG). The quality of collaboration was assessed based on video recordings of CPS sessions. Each session was rated by two independent reviewers using a 22-item observational grid. The grid was designed to capture four dimensions of productive CPS: socio-cognitive (SC - 9 items, 2 reverse-scored; e.g., Group members sought and/or provided explanations for presented ideas and suggestions); socio-emotional (SE - 4 items, 1 reverse-scored; e.g., Group members worked together, as a team); task management (TM - 5 items, 1 reverse-scored; e.g., The group planned its approach to solving the task); relationship management (RM - 5 items, 2 reverse-scored; e.g., Throughout the work, group members purposefully coordinated group and individual activities). Each item is scored on a 5-point Likert scale, ranging from 0 (not at all) to 4 (to a large extent). Data Analyses: Analyses were performed to examine the structural and reliability properties of measures designed specifically for this study. The unidimensionality of CPS-OG subscales was inspected via Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Internal consistency for CPS-OG dimensions was determined by calculating Cronbch’s alpha coefficient. Conclusions, Expected Outcomes or Findings The results confirmed good psychometric characteristics of the CPS observational grid. Exploratory factor analysis (Principal component analysis with Oblimin rotation) resulted in four factors explaining 76% of the total variance. Correlations between factors were moderate with a maximum value of 0.44. The first factor (50% of the variances) mainly included SC variables. The second factor (15 % of the variances) corresponds to the TM dimension. The third (6%) factor represents a mix of the SE and RM variables. It includes statements about negative relationships in the group (present tension, conflicts and isolation of the members). Finally, the fourth factor (5% of the variance) covers the absence of an authoritative leader and good conflict management as aspects of the RM dimension. The correlations between the first factor and the other three are moderate (from -0.33 to 0.44). The correlations between the other factors are low. Confirmatory factor analysis (CFA) confirmed a single-factor solution for all dimensions, except the TM. Item-level intraclass correlation (ICC) for CPS observational grid (CPS-OG) indexes reached excellent values (Cicchetti, 1994), ranging from .75 to .95. Dimension-level ICC values were also excellent: .94 for SC, .90 for SE, .93 for TM, .85 for RM. Internal consistency (Cronbach's alpha) ranges from good to excellent (.921 for SC, .914 for SE, .856 for TM, .791 for RM.) The next research step will include the external validation of the instrument. We will examine the association between the dimensions of the CPS observational grid and the quality of the proposed group solution. The quality of the solution will cover several dimensions: whether the solution is realistic; an assessment of the proposal's creativity; an assessment of the degree to which the proposal is well-argued with various perspectives. References Baucal, A., Jošić, S., Ilić, I. S., Videnović, M., Ivanović, J., & Krstić, K. (2023). What makes peer collaborative problem solving productive or unproductive: A qualitative systematic review. Educational Research Review, 100567. https://doi.org/10.1016/j.edurev.2023.100567 Fiore, S. M., Graesser, A., & Greiff, S. (2018). Collaborative problem solving education for the 21st century workforce. Nature: Human Behavior, 2(6), 367–369. https://doi.org/10.1038/s41562-018-0363-y Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., & Hesse, F. W. (2018). Advancing the science of collaborative problem solving. Psychological science in the public interest, 19(2), 59-92. https://doi.org/10.1177/1529100618808244 Hesse, F., Care, E., Buder, J., Sassenberg, K., & Griffin, P. (2015). A framework for teachable collaborative problem solving skills. Assessment and teaching of 21st-century skills: Methods and approach, 37-56. DOI: 10.1007/978-94-017-9395-7_2 Le, H., Janssen, J., & Wubbels, T. (2018). Collaborative learning practices: teacher and student perceived obstacles to effective student collaboration. Cambridge Journal of Education, 48(1), 103-122. https://doi.org/10.1080/0305764X.2016.1259389 Mercer, N. & Dawes, L (2014). The study of talk between teachers and students, from the 1970s until the 2010s. Oxford Review of Education, 40 (4) (2014), pp. 430-445. https://doi.org/10.1080/03054985.2014.934087 Mercer, N., Hennessy, S., & Warwick, P. (2019). Dialogue, thinking together and digital technology in the classroom: Some educational implications of a continuing line of inquiry. International Journal of Educational Research, 97, 187–199. https://doi.org/10.1016/j.ijer.2017.08.007 OECD. (2019). An OECD Learning Framework 2030 (pp. 23-35). Springer International Publishing. Wang, L., MacCann, C., Zhuang, X., Liu, O. L., & Roberts, R. D. (2009). Assessing teamwork and collaboration in high school students: A multimethod approach. Canadian Journal of School Psychology, 24(2), 108-124. DOI: 10.1177/0829573509335470 09. Assessment, Evaluation, Testing and Measurement
Paper An Exploration of Constructive Verbal Feedback in Secondary School Classrooms NIS Aktobe, Kazakhstan Presenting Author:Verbal feedback is the oral communication between teachers and students that aims to provide constructive guidance on students’ progress, strengths, and areas for improvement, according to numerous educational scholars (Black & Wiliam, 2009; Hattie & Timperley, 2007; Shute, 2008). In secondary school settings providing effective feedback is a key component of a good education. The effectiveness of feedback in education is a widely studied and acknowledged aspect of the learning process (Black & Wiliam, 1998; Hattie, 2009; Karaman, 2021; Wisniewski et al., 2020). As teachers continually work to improve the learning outcomes for their students, the role of feedback, especially verbal feedback that takes place in classrooms everyday, becomes increasingly important. The “Feed Up, Feed Back, Feed Forward” model, introduced by John Hattie and Helen Timperley in their influential 2007 paper, “The Power of Feedback,” presents a cyclical approach comprising three essential stages of effective feedback. These stages encompass setting clear objectives or “feed up,” delivering feedback on present performance, and proposing strategies for enhancement or “feed forward.” This implies that teachers should provide constructive feedback that is descriptive and focused on providing specific, actionable information aimed at helping the recipient improve or enhance their performance, skills, or understanding. Many studies on verbal feedback have been conducted in the field of foreign or second language learning, exploring different types and functions of corrective feedback and their effects on language proficiency (Lyster & Saito, 2010). These studies have shown that providing oral corrective feedback not only helps students improve their accuracy and fluency in speaking, but also enhances their overall language proficiency. Although teachers might have experience or undergo professional development courses, their formative assessment practices could not be always effective. According to certain research findings, teachers’ attitudes about the usage of various forms of oral corrective feedback in the classroom do not necessarily align with their actual practices (Kim & Mostafa, 2021). Further comprehensive research on corrective feedback is necessary to investigate the alignment between teachers’ actual practices and their underlying ideas about feedback (Karimi & Asadnia, 2015). Therefore, this study focuses on the following research question: “To what extent do secondary school teachers provide constructive verbal feedback in classroom?” Methodology, Methods, Research Instruments or Sources Used The study has taken place at Nazarbayev Intellectual school in Aktobe, Kazakhstan, and employed a quantitative research design. The sampling for lesson analysis consisted of 17 teachers representing different subjects, grade levels and teaching experience (from several months to more than ten years). The data was collected through recording videos of the 17 lessons and online survey among participants to understand their attitude on constructive verbal feedback. Ethical considerations have been considered during data collection. All teachers took part in the study voluntarily and agreed their lessons to be recorded. The confidentiality and anonymity of the participants have been ensured. The link to the survey was sent to the corporate emails. 46 teachers participated in an anonymous online survey. The analysis of video recordings was completed according to observation protocol for constructive verbal feedback influenced by observation protocols for formative assessment dimensions by Cisterna and Gotwals (2018). Our protocol consisted of four different levels of constructive feedback practice (1 being the lowest and 4 being the highest). Level 1 indicated the absence of teacher’s verbal feedback, while level 2 implied evaluative feedback where teachers had used very general and ambiguous comments like “Good job”, “Correct” or “That’s not the right answer”. Level 3 verbal feedback was mainly descriptive, focusing on the task completion, however, being not completely constructive and stimulating. The highest level of verbal feedback practice (level 4) was described as purely descriptive and specific with elaborated comments that stimulates students’ learning. Each level received respective score (1-4). Three researchers independently analysed the videos using the lesson observation protocol and the means of their scores was used to evaluate teachers’ overall oral feedback practice. The observation protocol has been designed in cooperation and discussed by all researchers before the lesson analyses to ensure validity and reliability. Conclusions, Expected Outcomes or Findings The analysis of video recordings has revealed that the mean score for teachers’ overall oral feedback practice was 2.6, which indicates that feedback there is room for improvement in providing more detailed and constructive feedback to students. Teachers usually gave more evaluative feedback compared to descriptive one. When giving feedback, they mostly responded with the words “Good”, “good job”, and “correct” as well as conveyed it through gestures. This observation suggests that teachers should focus on enhancing their oral feedback practices by providing more specific and elaborated feedback that would help students understand about the ways to improve their learning. Findings from the survey demonstrate that more than half of the respondents agree that constructive feedback is time-consuming to conduct effectively. The majority of the teachers admitted that they did not take notes of the student’s progress. 43% of the teachers acknowledged that they lacked knowledge of effective feedback providing techniques, whereas the half believed in having sufficient constructive feedback skills. References Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7-74. https://doi.org/10.1080/0969595980050102 Cisterna, D., & Gotwals, A. W. (2018). Enactment of ongoing formative assessment: Challenges and opportunities for professional development and practice. Journal of Science Teacher Education, 29(3), 200-222. Hattie, J. (2009). Visible Learning: A Synthesis of 800+ Meta-Analyses on Achievement. London: Routledge Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487 Karaman, P. (2021). The Effect of Formative Assessment Practices on Student Learning: A Meta-Analysis Study. International Journal of Assessment Tools in Education, 8(4), 801-817. https://doi.org/10.21449/ijate.870300 Karimi, M. N., & Asadnia, F. (2015). EFL Teacher’s Beliefs About Oral Corrective Feedback and their Feedback-providing Practices Across Learners’ Proficiency Levels. Teaching English as a Second Language Quarterly (Formerly Journal of Teaching Language Skills), 34(2), 39-68. Kim, Y., & Mostafa, T. (2021). Teachers’ and Students’ Beliefs and Perspectives about Corrective Feedback. In H. Nassaji & E. Kartchava (Eds.), The Cambridge Handbook of Corrective Feedback in Second Language Learning and Teaching (pp. 561–580). chapter, Cambridge: Cambridge University Press. Lyster, R., & Saito, K. (2010). Oral feedback in classroom SLA: A meta-analysis. Studies in second language acquisition, 32(2), 265-302. Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in psychology, 10, 3087. 09. Assessment, Evaluation, Testing and Measurement
Paper Unpacking Assessment and Feedback: International Student’s experience during postgraduate study 1University of Birmingham, United Kingdom; 2University of Northampton, United Kingdom; 3University of Wolverhampton, United Kingdom Presenting Author:Assessment and feedback are fundamental aspects of student experience in higher education as a measure of progress and achievement, and a central tenant of learning and engagement. Assessment and feedback play a pivotal role in increasing student knowledge and understanding, developing key skills, and promoting motivation and academic advancement. The ever-increasing number of international students in higher education, representing a significant percentage student body, necessitates focused consideration of their experience of assessment and feedback. The research-based paper provides evidence based on the investigation of international students, and presents their voices in relation to the lived experience of assessment and feedback. The research focussed on elements of student experiences with regard to assessment and feedback, addressing the following Research Questions: RQ 1- What do students currently encounter in terms of assessment and feedback? RQ 2- How can we evolve assessment and feedback strategies to enhance the experience for international students? The research utilised an interpretivist paradigm, concentrating on the perspectives of the respondents to develop knowledge of their experience and interpretation of this. Unlike research paradigms which primarily aim to universalise results, interpretivist research focuses on understanding the viewpoints of participants within their specific settings. It acknowledges that these viewpoints and behaviours are dynamic, altering based on temporal and situational factors. This facilitates the contrasting of outcomes across different time frames or locales (Cohen et al., 2017). Higher Education needs to be ever responsive to technological innovation, pedagogical shifts, and the increasing diversity of the student body. The role of assessment and feedback within higher education remains central, acting as both a measure and a driver of student learning and engagement. Dr Katherine Hack, principal adviser in teaching and learning at the Higher Education Academy (HEA), stated that assessments and feedback are two of the most influential tools teachers have to direct and support learning (Advance HE, 2022). Indeed, assessment and feedback are an inherent and significant part of a student’s experience, and the prominence of this is captured annually in surveys such as the National Student Survey (NSS) for undergraduates (NSS) and the Postgraduate Taught Experience Survey (PTES). As such, continually re-evaluating and refining assessment and feedback, to align with the changing educational environment, is essential to keep practices contemporary and responsive to the student body and experience. International students account for a notable percentage of postgraduate students across Higher Education Institutions (HEIs) worldwide, and represent a wide array of cultural and educational backgrounds. The difficulties for students needing to navigate an unfamiliar culture are well documented (Haider, 2018; Xie et al, 2019). Simultaneously, international students must also navigate new assessment and feedback practices as part of the transitional journey to Higher Education Institutions (HEIs), often in a different country. This presents a unique set of challenges, academic and cultural, adding an additional layer of complexity to an already nuanced landscape. HEIs are faced with the task of ensuring that their assessment and feedback practices are inclusive and equitable catering to a diverse student body. The Quality Assurance Agency (QAA) began embedding equality, diversity, and inclusion (EDI) in its subject benchmarks in 2021, in the UK. This was part of a wider commitment to promoting EDI across HEIs, and to ensure that all students have an equal opportunity to succeed. This requires a continued commitment to academic rigour while adapting to the evolving needs and expectations of a diverse student body. This requirement can be applied globally, as HEIs seek to ensure that EDI is integrated within assessment and feedback. The paper seeks to investigate how this can be achieved.
Methodology, Methods, Research Instruments or Sources Used A mixed methods approach was utilised, and two research instruments were devised for the investigation: 1- Online questionnaire 2- Face to face focus groups An online questionnaire served as the initial tool for gathering qualitative data, with the aim of enabling a detailed examination of individual perspectives and an assessment of collective viewpoints within the sample (Clark et al., 2021). Countering the prevalent misconception that qualitative research lacks numerical components, Sandelowski (2001) argued that numeric data can play various roles affecting both the structure of the research and its ultimate categorisation. On this basis some numerical data was drawn upon to contextualise and inform the findings and analysis, and as an indicator of the respondent's experiences. The questionnaire consisted of both fixed-response and open-response items. Fixed-response questions enabled respondents to select options that best suited their specific circumstances, whereas open-response questions offered the opportunity for more detailed personal reflections. The specific questions were informed by preliminary dialogues with international students on postgraduate courses. The questions were aligned with the Research Questions, but also sought to identify and provide opportunities for respondents to include details of their lived experiences. 101 students, undertaking postgraduate study in three universities, responded to the detailed questionnaire, and the data was systematically analysed and key themes identified. Following on from the questionnaire, face-to-face focus groups were then undertaken to gather qualitative data, aimed at a nuanced exploration of individual viewpoints, as well as the aggregated opinions of the participants (Kamberelis and Dimitriadis, 2013). The framework for the focus groups included both pre-defined discussion themes and open-ended questions, allowing respondents to elaborate on their unique perspectives and experiences. Thematic analysis of the focus group transcripts was carried out. The themes were subsequently examined, and cross-referenced against pertinent statistical data and research based findings. The individual viewpoints, perceptions and experiences of the respondents are included in the paper, to ensure that their distinct 'voices' are captured and highlighted. Conclusions, Expected Outcomes or Findings The research-based paper aims to contribute to a growing body of knowledge on assessment and feedback in Higher Education, with a specific focus on international students. Utilising a qualitative approach that incorporates data collected via questionnaire and focus group, the research provides a range of insights regarding how international students experience and perceive assessment and feedback during their postgraduate courses. The research contributes to academic discourse, and offers practical insights for HEIs and academics moving forwards in providing effective provision for an increasingly diverse and global student population. The research contributes to narrowing the research gap identified, and the need for a nuanced understanding of assessment and feedback practices in higher education settings for international students. By offering a multi-faceted view that considers transitional experiences, individual preferences and challenges, and emotional impacts, the research provides a richer, more complex understanding of assessment and feedback. It underscores the need for higher education institutions to adopt a more adaptive, personalised, and emotionally intelligent approach to enhance the student experience of assessment and feedback. The research adds depth and breadth to the existing literature by highlighting key considerations that need to be addressed when working with international students, and places the international student at the forefront. This provides a student voice and perspective that emphasises their particular needs, concerns and challenges. The research provides recommendations and a six phased template that could be utilised in the design and implementation of higher educational assessment and feedback provision for international students, across global HEIs. References Arthur, N. (2017) Supporting international students through strengthening their social resources. Studies in Higher Education, 42(5), 887–894. https://doi.org/10.1080/03075079.2017.1293876 Baughan, P. (2021) Assessment and Feedback in a Post-Pandemic Era: A Time for Learning and Inclusion. Advance HE. Cook, D.A., and Artino, A.R. (2016) Motivation to learn: an overview of contemporary theories. Medical Education. 50(10), 997–1014. Chew, E. (2014) “To listen or to read?” Audio or written assessment feedback for international students in the UK. On the Horizon. 22(2), 127–135. Dawadi, S., Shrestha, S., and Giri, R. A. (2021) Mixed-Methods Research: A Discussion on its Types, Challenges, and Criticisms. Journal of Practical Studies in Education, 2(2), 25-36 DOI: https://doi.org/10.46809/jpse.v2i2.20 Grainger, P. (2020) How do pre-service teacher education students respond to assessment feedback? Assessment & Evaluation in Higher Education. 45(7), 913–925. Haider, M. (2018) Double Consciousness: How Pakistani Graduate Students Navigate Their Contested Identities in American Universities. In Y. Ma & M. A. Garcia-Murillo, eds. Cham: Springer International Publishing, pp. 107–125. Henderson, M., Ryan, T and Phillips, M (2019) The challenges of feedback in higher education, Assessment & Evaluation in Higher Education, 44:8, 1237-1252, DOI: 10.1080/02602938.2019.1599815 Koo, K., and Mathies, C. (2022) New Voices from Intersecting Identities Among International Students Around the World: Transcending Single Stories of Coming and Leaving. Journal of International Students. 12(S2), 1–12. Lomer, S., and Mittelmeier, J. (2023) Mapping the research on pedagogies with international students in the UK: a systematic literature review. Teaching in Higher Education. 28(6), 1243–1263. McCarthy, J. (2015) Evaluating written, audio and video feedback in higher education summative assessment tasks. Issues in Educational Research, 25(2), 153-169. http://www.iier.org.au/iier25/mccarthy.html Oldfield, A., Broadfoot, P., Sutherland, R and Timmis, S (nd) Assessment in a Digital Age. University of Bristol, Graduate School. https://www.bristol.ac.uk/media-library/sites/education/documents/researchreview.pdf Schillings, M., Roebertsen, H., Savelberg, H., Whittingham, J., Dolmans, D. (2020) Peer-to-peer dialogue about teachers’ written feedback enhances students’ understanding on how to improve writing skills. Educational Studies. 46(6), 693–707. Winstone, N.E., Nash, R.A., Parker, M., and Rowntree, J. (2017) Supporting Learners’ Agentic Engagement With Feedback: A Systematic Review and a Taxonomy of Recipient Processes. Educational Psychologist. 52(1), 17–37. Xie, M., Liu, S., Duan, Y., Qin, D.B. (2019) “I Can Feel That People Living Here Don’t Like Chinese Students”: Perceived Discrimination and Chinese International Student Adaptation H. E. Fitzgerald, D. J. Johnson, D. B. Qin, F. A. Villarruel, & J. Norder, eds. , 597–614. |
Date: Friday, 30/Aug/2024 | |
9:30 - 11:00 | 09 SES 14 A: Exploring Factors Influencing Teaching Quality and Student Learning Outcomes Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Charalambos Charalambous Paper Session |
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09. Assessment, Evaluation, Testing and Measurement
Paper Comparing the Predictive Validity of Ratings on Opportunity and Use of Cognitive Activation: Does the Source of Information Matter? University of Cyprus, Cyprus Presenting Author:Teaching quality has been empirically shown to be a key predictor of student learning (Stronge, 2013). In studying teaching quality, teaching effectiveness researchers have for years focused on the opportunities provided to students for learning, as these are crafted through teacher-student and student-student interactions with the content. Yet, following Fend’s (1981) distinction between opportunity and use and more recent work in the German-speaking countries on this issue (cf. Vieluf et al., 2020), teaching effectiveness researchers worldwide have started to more increasingly attend to not only the opportunities created for student learning, but also to how students make use of these opportunities, on the grounds that the former without the latter can only partially explain student learning. Of particular interest in this line of research are the opportunities provided for student cognitive activation, often identified as the potential for cognitive activation, and students’ use of these opportunities, often identified as cognitive activity (Groß-Mlynek et al., 2022; Rieser & Decristan, 2023). This heightened interest in cognitive activation is justified both because of empirical findings corroborating its role for students’ cognitive and affective learning (e.g., Lazarides & Buchholz, 2019), but also due to studies showing cognitively activating teaching to be highly needed worldwide (cf. OECD, 2020). Despite this increased interest, our review of the literature showed that in most extant studies scholarly attention has mostly been directed to the potential for cognitive activation without also exploring students’ cognitive activity. Only three studies concurrently attended to and measured both the opportunity and use for cognitive activation in relation to student learning (Lipowsky et al., 2009; Merk et al., 2021; Rieser & Decristan, 2023). These studies, however, differ not only in reporting mixed findings, but also in their methodological design: whereas the former two employed expert classroom observers’ ratings to capture the potential for cognitive activation and student ratings to capture cognitive activity, the latter utilized student ratings to measure both. Concurrently attending to different sources of information (e.g., expert classroom observers and students) is, however, critical, given scholarly calls (e.g., Fauth et al., 2020) to more systematically examine how the source of information contributes to the predictive validity of the teaching quality measures employed. The scarcity of studies that concurrently attend to the predictive validity of opportunity and use (in cognitive activation); the mixed findings of these studies; and the fact that none of them concurrently used different sources of information to capture the opportunity for cognitive activation—note that the use of opportunities is typically captured only through student ratings—raise two questions: - How does the predictive validity of ratings on the opportunity for cognitive activation compare with that of ratings on the use of cognitive activation? - Does this differ when different sources of information (expert classroom observers vs. students) are employed to capture the opportunity for cognitive activation? Addressing these questions can have important methodological implications for measuring aspects of teaching quality in more optimal ways, but also practical implications for teachers’ formative evaluation. However, to more adequately answer these questions, and especially the second one, attention needs to be paid to ensuring that the measures of the different sources obtained are aligned in the sense of tapping into similar—and if possible identical—aspects of teaching quality. Doing so becomes particularly important, given that our review of the literature showed only a few studies comparing aligned measures of teaching quality from different sources (e.g., van der Scheer et al., 2018)—and even in those cases, not with respect to the issue of their predictive validity. Methodology, Methods, Research Instruments or Sources Used Sample and measures. A sample of 31 elementary school teachers and their sixth-grade students (n=542) participated in the study. For comparability purposes, all participating teachers were observed teaching the same three algebra lessons. Students’ algebra performance before and after these lessons was measured through a validated mathematics test (Authors, 2019). We measured the potential for cognitive activation in two ways: (a) Expert observer ratings: The 93 lessons were coded by three expert raters trained and certified for this purpose; the raters first rated these lessons individually and then met in pairs to discuss and reconcile their scores. For this study, we utilized the raters’ reconciled scores on the Common Core-Aligned Student Practices of the Mathematical Quality of Instruction (cf. Charalambous & Litke, 2018) framework, which capture the opportunities provided to students for cognitive activation through working on challenging tasks, providing explanations, and engaging in reasoning. (b) Student ratings: Drawing on prior work (e.g., Fauth et al., 2014), we used 8 survey items capturing students’ perceptions of how frequently their teacher gave them opportunities to engage in cognitively activating teaching (e.g., through handling different solutions, providing explanations, or working on complex tasks/new content). Student ratings were aggregated to the classroom level to reflect the class’ overall perception of the opportunities provided. Four items were utilized to measure student cognitive activity, drawing on existing scales (e.g., Merk et al., 2021). Unlike for the potential of cognitive activation, we used student ratings at the individual rather than the classroom level, given that they were taken to reflect students’ individual self-perceptions of how they themselves experienced to be cognitively challenged. We also administered a validated survey (Kyriakides et al., 2019) measuring students’ SES, gender, and ethnicity. Finally, we collected information on teachers’ gender, years or experience, and education credentials. Analyses. Two-level (students nested within teachers) multilevel modeling analysis was utilized with students’ performance at the culmination of algebra teaching as the dependent variable. After controlling for student and teacher background characteristics as well as students initial algebra performance, we introduced observer and student ratings on cognitive activation (first in isolation and then in combinations). We ran these analyses twice, first for the ratings as composites, and then for individual items (those that were aligned in content). In comparing the predictive validity of the examined predictors, we considered both their statistical significance and the percentage of the unexplained variance explained. Conclusions, Expected Outcomes or Findings For the composites, both the potential for cognitive activity (opportunity) and cognitive activity (use) were predictive of student learning, regardless of how they were measured. When introduced in isolation to the model, each significantly contributed to student learning. For opportunity, classroom expert ratings explained a much higher percentage of the unexplained variance (4.20% total, all at the teacher level, explaining about 70% of the unexplained variance at that level) compared to that explained by student ratings (1% total, all at the teacher level, explaining 16% of the unexplained variance at that level). Compared to student opportunity ratings, student use ratings explained a slightly higher percentage of the total variance (1.5% total, corresponding to about 7% and 5% of the unexplained variance at the teacher and student level, correspondingly). When all three ratings were introduced, student opportunity ratings were no longer significant. Interestingly, the combination of expert ratings on opportunity and student ratings on use explained the highest percentage of the unexplained variance of all the models considered (5.30% total, explaining 70% and 3% of the unexplained variance at teacher and student level correspondingly). When comparing the aligned survey and MQI items (e.g., providing explanations; working on challenging tasks/new content), we noticed that whereas in all cases, the expert observer ratings had a significant contribution to student learning, student ratings did have such a consistent contribution (and also explained a smaller percentage of the unexplained variance). Collectively, these findings underline the value of concurrently attending to both opportunity and use. They also suggest that classroom observer ratings might have more predictive validity than student ratings when it comes to the opportunities provided to students for cognitive activation. Future replication studies with a different student population on a different subject are, however, needed to test the veracity of these arguments. References Authors (2019). [Blinded for peer-review purposes]. Charalambous, C. Y., & Litke, E. (2018). Studying instructional quality by using a content-specific lens: The case of the Mathematical Quality of Instruction framework. ZDM, 50(3), 445–460. https://doi.org/10.1007/s11858-018-0913-9 Fauth, B., Decristan, J., Rieser, S., Klieme, E., & Büttner, G. (2014). Student ratings of teaching quality in primary school: Dimensions and prediction of student outcomes. Learning and Instruction, 29, 1–9. https://doi.org/10.1016/j.learninstruc.2013.07.001 Fauth, B., Göllner, R., Lenske, G., Praetorius, A.-K. & Wagner, W. (2020). Who sees what? Conceptual considerations on the measurement of teaching quality from different perspectives. Zeitschrift für Pädagogik, 66, 63–80. https://doi.org/10.15496/pub likation-41013 Fend, H. (1981). Theorie der schule. Urban & Schwarzenberg. Groß-Mlynek, L., Graf, T., Harring, M., Gabriel-Busse, K., & Feldhoff, T. (2022). Cognitive activation in a close-up view: Triggers of high cognitive activity in students during group work phases. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.873340 Kyriakides, L., Charalambous, E., Creemers, H. P. M. B., & Dimosthenous, A. (2019). Improving quality and equity in schools in socially disadvantaged areas. Educational Research, 61(3), 274–301. https://doi.org/10.1080/00131881.2019.1642121 Lazarides, R., & Buchholz, J. (2019). Student-perceived teaching quality: How is it related to different achievement emotions in mathematics classrooms? Learning and Instruction, 61, 45–59. https://doi.org/10.1016/j.learninstruc.2019.01.001 Lipowsky, F., Rakoczy, K., Pauli, C., Drollinger-Vetter, B., Klieme, E., & Reusser, K. (2009). Quality of geometry instruction and its short-term impact on students’ understanding of the pythagorean theorem. Learning and Instruction, 19(6), 527–537. https://doi.org/10.1016/j.learninstruc.2008.11.001 Merk, S., Batzel-Kremer, A., Bohl, T., Kleinknecht, M., & Leuders, T. (2021). Nutzung und wirkung eines kognitiv aktivierenden unterrichts bei nicht-gymnasialen schülerinnen und schülern. Unterrichtswissenschaft, 49(3), 467–487. https://doi.org/10.1007/s42010-021-00101-2 OECD. (2020). Global teaching in sights: A video study of teaching. OECD Publishing. https://doi.org/10.1787/20d6f36b-en Rieser, S., & Decristan, J. (2023). Kognitive aktivierung in befragungen von schülerinnen und schülern. Zeitschrift Für Pädagogische Psychologie, 1-15. https://doi.org/10.1024/1010-0652/a000359 Stronge, J. (2013). Effective teachers = student achievement: What the research says. Routledge. van der Scheer, E. A., Bijlsma, H. J. E., & Glas, C. A. W. (2018). Validity and reliability of student perceptions of teaching quality in primary education. School Effectiveness and School Improvement, 30(1), 30–50. https://doi.org/10.1080/09243453.2018.1539015 Vieluf, S., Praetorius, A., Rakoczy, K., Kleinknecht, M., & Pietsch, M. (2020). Angebots-nutzungs-modelle der wirkweise des unterrichts: Ein kritischer vergleich verschiedener modellvarianten. Z. Pädagog. 66, 63–80. https://doi.org/10.25656/01:25864 09. Assessment, Evaluation, Testing and Measurement
Paper Effects of Presentation Order on the Reliability of Classroom Observations of Teaching Quality in Norwegian Mathematics and Science Lessons University of Oslo, Norway Presenting Author:Teaching quality has been researched extensively in the past years with a high number of empirical studies in educational sciences and psychology. To better understand how learning develops in the classroom, scholars are concerned with the reliable and valid measurement of teaching quality. In doing so, Helmke (2012) considers classroom observation as the “gold standard” amongst other ways of capturing teaching quality (e.g., student ratings in large-scale assessment) because of its direct assessment of teaching practices. However, classroom observation also draws on resources and can be prone to many sources of measurement error. Therefore, when performing classroom observation for any purpose (i.e., research, practical, policy) it is important to consider how to allocate (limited) resources such that high score reliability and valid conclusions about teaching quality are ensured. Studies suggests that changing the presentation order of lesson segments could particularly affect score reliability (e.g., Mashburn et al., 2014). For instance, using the generic CLASS-Secondary observation system (Pianta et al., 2008), Mashburn et al. (2014) found that 20-minute lesson segments presented in a random order to raters achieved the best combination of reliability and predictive validity. In the present study, we used a different, hybrid observation system (i.e., comprising both generic and subject-specific aspects of teaching quality, Charalambous & Praetorius, 2018) that was first developed to capture teaching quality in German secondary mathematics classrooms, and that draws on the Three Basic Dimensions of teaching quality (classroom management, student support, and (potential for) cognitive activation, e.g., Klieme et al., 2009). The three basic dimensions have been shown to positively relate to students’ achievement in mathematics classrooms across several studies and various operationalizations (e.g., Baumert et al., 2010; for an overview see Praetorius et al., 2018). Classroom management refers to teachers’ procedures and strategies that enable efficient use of time (time on task), as well as behavioral management (Kounin, 1970). Student support draws on self-determination theory (Deci & Ryan, 1985) and aims at both motivational and emotional support, as well as individualization and differentiation. Cognitive activation, finally, addresses opportunities for "high-order thinking" from a socio-constructivist perspective on teaching and learning (e.g., problem solving, Mayer, 2004). Empirical evidence suggests that generic and subject-specific measures of teaching quality generate moderately correlated, but still unique information about classrooms (Kane & Staiger, 2012). Evaluating this finding, Charalambous and Praetorius (2018) conclude that subject-specific and generic measures together could explain more variance in student learning in mathematics than generic measures alone. Since subject-specificity might be considered a continuum rather than a binary characteristic, they argue that it could be meaningful for scholars to develop hybrid frameworks of teaching quality, which take both perspectives into account (i.e., generic and subject-specific, see also Charalambous & Praetorius, 2018). The purpose of the present study is twofold: First, we aim at investigating the effect of presentation order on score reliability in two subjects. Second, we explore an optimal design for the implementation of our observation system in terms of score reliability. Towards this end, we assigned four trained raters to rate videotaped Norwegian mathematics and science lessons either in sequential 20-minute segments, or two nonsequential 20-minute segments. Methodology, Methods, Research Instruments or Sources Used Data was obtained from schools from the Oslo metropolitan area in Norway, with teachers conveniently participating in the study. In total, 15 classrooms were sampled, and from each classroom one through six lessons are available that were videotaped over the course of several weeks. The length of the lessons varied between 24 and 106 minutes, and they were cut into 20-minute segments for analysis. For the purpose of this study, two segments from every mathematics classroom and two segments from every science classroom were analyzed, and the segments were scored under both study conditions (i.e., sequential and nonsequential). We applied the observation system from the Teacher Education and Development Study–Instruct (TEDS-Instruct, e.g., Schlesinger et al., 2018). Consequently, the framework and corresponding instrument involved four teaching quality dimensions with four to six items each that also used different indicators for mathematics and science classrooms. Raters were trained extensively over the course of one week by studying the rating manual, conducting video observations, and discussing the results with master raters. However, no benchmarks were applied. All raters were student teachers in mathematics and science programs, and they were at least in their fourth year. To analyze the effect of presentation order on score reliability, we designed our study as follows. For each lesson, we randomly assigned one rater to the sequential condition. The rater would then score both segments of this lesson. This condition is referred to as the static condition. At the same time, two different raters were assigned to the nonsequential condition. We had these raters randomly score either the first or the second segment of a lesson. This we refer to as the switching condition. Using this experimental design, raters were balanced across subjects and conditions. Since in this study we only analyzed one lesson for each teacher-subject combination, raters would not score the same teacher or classroom twice within the same condition or subject. However, there was a chance that raters could encounter the same teacher in a different subject. We applied Generalizability theory (GT, Cronbach et al., 1972) to estimate measurement error and reliability in our study. GT was developed specifically for complex measurement situations with many potential sources of error, such as classrooms, lessons, or raters. GT makes use of the linear mixed model to estimate variance components for each measurement facet of interest (G Study). Conclusions, Expected Outcomes or Findings Our results show that, overall, presentation order had little impact on score reliability. In more detail, score reliability was high for science lessons in both conditions, and acceptable for two out of four teaching quality dimensions in mathematics with slightly better results for the static condition. A low share of lesson variance and a relatively high share of within-lesson variation was found for cognitive activation. Correlation analysis and mean comparisons revealed no meaningful differences between conditions. Our results could be depended on the fact that we only sampled one lesson per classroom. Other studies show that particularly subject-specific aspects of teaching quality vary severely over time (e.g., Praetorius et al., 2014). However, we did not encounter similar issues in science classrooms, which suggests that (1) teaching quality in science and mathematics lessons varies on different time scales, (2) the observation system functions differently in mathematics and science lessons, or (3) raters have applied the measure differently between subjects. References Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., . . . Tsai, Y.-M. (2010). Teachers' mathematical knowledge, cognitive activation in the classroom, and sudent progress. American Educational Research Journal, 47(1), 133–180. Charalambous, C., & Praetorius, A.-K. (2018). Studying Instructional Quality in Mathematics through Different Lenses: In Search of Common Ground. ZDM Mathematics Education, 50, 535-553. Cronbach, L. J., Glaser, G. C., Nanda, H., & Rajaratnam, N. (1972). The dependability of behavioral measurements: Theory of generalizability for scores and profiles. John Wiley. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Perspectives in social psychology. Plenum. Helmke, A. (2012). Unterrichtsqualität und Lehrerprofessionalität: Diagnose, Evaluation und Verbesserung des Unterrichts. Klett-Kallmeyer. Kane, T. J., & Staiger, D. O. (2012). Gathering feedback for teaching: Combining high-quality observations with student surveys and achievement gains. Bill & Melinda Gates Foundation. Klieme, E., Lipowsky, F., Rakoczy, K., & Ratzka, N. (2006). Qualitätsdimensionen und Wirksamkeit von Mathematikunterricht: Theoretische Grundlagen und ausgewählte Ergebnisse des Projekts "Pythagoras". In M. Prenzel & L. Allolio-Näcke (Eds.), Untersuchungen zur Bildungsqualität von Schule. Abschlussbericht des DFG-Schwerpunktprogramms (pp. 127-146). Waxmann. Kounin, J. S. (1970). Discipline and group management in classrooms. Holt, Rinehart & Winston. Mashburn, A. J., Meyer, J. P., Allen, J. P., & Pianta, R. C. (2014). The effect of observation length and presentation order on the reliability and validity of an observational measure of teaching quality. Educational and Psychological Measurement, 74(3), 400-422. Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59(1), 14–19. Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System™: Manual K-3. Paul H. Brookes Publishing Co. Praetorius, A.-K., Klieme, E., Herbert, B., & Pinger, P. (2018). Generic dimensions of teaching quality: The German framework of Three Basic Dimensions. ZDM Mathematics Education, 50, 407-426. Schlesinger, L., Jentsch, A., Kaiser, G., König, J., & Blömeke, S. (2018). Subject-specific characteristics of instructional quality in mathematics education. ZDM Mathematics Education, 50, 475-491. Shavelson, R. J., & Webb, N. M. (1991). Generalizability Theory: A Primer. SAGE Publications. 09. Assessment, Evaluation, Testing and Measurement
Paper Supporting Teachers to Participate in Lesson Study with Advisors or Facilitators: Searching for Differential Effects on Student Learning Outcomes 1University of Cyprus; 2Cyprus Pedagogical Institute Presenting Author:Lesson Study (LS), a collaborative and inquiry-based model of teacher professional development, has received increased attention in recent years. It involves teachers in small groups identifying an issue in their teaching practice and organising an inquiry to learn more about it. Specifically, teachers jointly plan, teach and reflect on lessons. A rich body of mostly descriptive and qualitative studies suggests that with LS experience teachers may develop pedagogical knowledge, and may be able to identify students’ misconceptions (e.g. Cheung & Wong, 2014; Vrikki, Warwick, Vermunt, Mercer & van Halem, 2017). However, more large-scale controlled studies are needed in order to systematically evaluate the effect of LS (Benedict et al., 2023). In addition, even less evidence exists of the impact of teachers’ participation in LS on their students’ achievements (e.g. Cheung & Wong, 2014; Kager, Mynnott & Vock, 2023). In addition, variations of the LS model include the presence of an external expert (i.e., LS facilitator, knowledgeable other, moderator). The literature identifies many ways that this external expert can support teachers, including enhancing in-depth discussions about students’ thinking, shaping the quality of the teachers’ inquiry, fostering teachers’ discussions by posing questions, encouraging teachers to share their experiences and managing the LS process (e.g. Akiba et al., 2019; Schipper et al., 2017; Bjuland & Helgevold, 2018; De Vrie, Verhoed & Goei, 2016). These responsibilities are not only vaguely described, but their effects have not been studied either. At the same time, research on teacher and school improvement argues for the important role of an advisory and research team that can work closely with teachers and support their attempt to design, implement and evaluate their action plans (e.g., Creemers & Kyriakides, 2012; Scheerens, 2013). This paper addresses both limitations in the literature described above. First, it aims to examine how secondary mathematics teachers’ participation in LS affects their students’ achievement in reasoning. Second, it aims to examine how different types of support offered to LS teacher groups can further enhance students’ achievement. Specifically, it examines the impact of the support of a LS facilitator, who guides teachers through the LS process, fosters their discussions and promotes teacher learning which is expected to affect student learning outcomes. It compares this to the impact on student learning outcomes of the support of an LS advisor who in addition to guiding teachers through the LS process, offers subject advice and his/her own ideas to the teachers. Although having these kinds of support is not uncommon, little is known about the effect of different types of support that teachers may have in implementing LS. Methodology, Methods, Research Instruments or Sources Used A group randomisation study took place in Cyprus during the school year 2022-2023. A total of 42 lower secondary mathematics teachers, who taught Grades 7 to 10 (students aged 11-14), in 13 secondary schools, were randomly allocated to three groups: two experimental and one control group. Teachers in two experimental groups formed small LS teams (2-3 teachers) and implement a specific variation of the LS model, namely Dudley’s (2019) “Research Lesson Study”. This is a three-cycle model, meaning that to complete one LS teachers had to plan three “research” lessons, one teacher teaching them while the others observed, and then to jointly reflect on the lessons. Each LS team completed two LSs during the school year, that is six research lessons. The difference between the two experimental groups was that teachers in one experimental group were supported by a “LS Facilitator”, who coordinated the discussions and helped teachers through the LS process, while teachers in the second experimental group were supported by a “LS Advisor”, who also provided advice on mathematics pedagogy. Teachers of the control group did not participate in any LS. Two classes per teacher were randomly selected to participate in the study, giving a total of 966 student participants. The students completed mathematical reasoning tests at the beginning and at the end of the school year. Specifically, a total of five tests were developed by a group of mathematics educators and expert teachers to assess students’ cognitive learning outcomes in relation to mathematical reasoning. These tests were used as pre-tests and post-tests across the four grades. Prior to the intervention, the construct validity of the five tests was examined. Data were analysed by using the Rasch model and support to the validity of the tests was provided. Student background data (i.e., students’ socioeconomic background and gender) were also collected via a student questionnaire. Conclusions, Expected Outcomes or Findings Using one-way ANOVA, it was found that there was no statistically significant differences on student prior achievement among the three groups at the beginning of the intervention. Inferential analysis revealed no statistically significant differences at .05 level in terms of student background characteristics (i.e., SES and gender). To search for the impact of the intervention on student learning outcomes, multilevel analysis of student achievement in mathematical reasoning was conducted for the data collected at the end of the intervention. The empty model revealed that the teacher level rather than the class level should be considered for this analysis. In Model 1 prior achievement in mathematical reasoning was added as an explanatory variable. Prior achievement was found to have a statistically significant effect on final achievement. In Model 2, student background variables including grade were added as explanatory variable. Finally, two dummy variables (with the control group treated as a reference group) were added to model 2. Only the dummy variable concerned with supporting teachers with an advisor to implement LS was found to have a statistically significant effect at .05 level. Thus, the multilevel analysis revealed that students whose teachers participated in the Advisor group had better results in mathematical reasoning than students whose teachers participated in the Facilitator and the Control groups. Implications of findings for research, policy and practice are discussed. The paper argues about the role of advisor which seems to be crucial for promoting student learning outcomes. Policy makers and school leaders, therefore, should consider options for creating the conditions for in-school models of professional development. Further research is needed to test the generalisability of the findings. References Benedict, A. E., Williams, J., Brownell, M.T., Chapman, L. Sweers, A. & Sohn, H. (2023). Using lesson study to change teacher knowledge and practice: The role of knowledge sources in teacher change. Teaching and Teacher Education, 122. Bjuland, R. & Helgevold, N. (2018). Dialogic processes that enable student teachers’ learning about pupil learning in mentoring conversations in a Lesson Study field practice. Teaching and Teacher Education, 70, 246-254. Creemers, B.P.M. & Kyriakides, L. (2012). Improving Quality in Education: Dynamic Approaches to School Improvement. Routledge. Cheung, W. M., & Wong,W. Y. (2014). Does lesson study work?: A systematic review on the effects of lesson study and learning study on teachers and students. International Journal for Lesson and Learning Studies, 3(2), 137e149. https://doi.org/10.1108/IJLLS-05-2013-0024 De Vries, S., Verhoef, N. & Goei, S. L. (2016). Lesson Study: a practical guide for education. Dudley, P. (2019). Research lesson study: A handbook. https://lessonstudy. co.uk/2015/11/download-a-free-copy-of-the-lesson-study-handbook. Kager, K., Mynott, J. P. & Vock, M. (2023). A conceptual model for teachers’ continuous professional development through lesson study: Capturing inputs, processes, and outcome. International Journal of Educational Research Open. https://doi.org/10.1016/j.ijedro.2023.100272 Scheerens, J. (2013). The use of theory in school effectiveness research revisited. School, Effectiveness and School Improvement, 24, 1–38. Schipper, Τ., Goei, S. L., de Vries, S., & van Veen, K. (2017). Professional growth in adaptive teaching competence as a result of Lesson Study. Teaching and Teacher Education, 68, 289-303. Vrikki, M., Warwick, P., Vermunt, J.D., Mercer, N. & Van Halem, N. (2017). Teacher learning in the context of Lesson Study: A video-based analysis of teacher discussions. Teaching and Teacher Education, 61, 211-224. 09. Assessment, Evaluation, Testing and Measurement
Paper Are Teaching Actions as Observed and Experienced by Students Predicting Romanian Students’ Achievement in TIMSS 2019? University of Bucharest, Romania Presenting Author:In the contemporary era, the important advancements in technology are closely connected with the paramount importance of achievements in mathematics and sciences disciplines. The current societal landscape, characterized by technological progress and the prevalence of a data-driven environment, underscores the increasing importance of mathematical and scientific knowledge. Mathematics is acknowledged as the foundational language supporting all STEM (Science, Technology, Engineering, and Mathematics) disciplines. Numerous stakeholders have underscored the imperative for a nation to enhance the mathematical skills and proficiency of its students (Mujtaba et al., 2014).
In a study analyzing TIMSS 2019 data for Turkey, teacher practices such as relating to daily life and prior knowledge, responding to student needs and encouraging students to participate in the discussion predicted mathematics achievement and explained the one-fifth of the between-schools variance (Sezer & Cakan, 2022). Also, activities such as asking students to complete challenging exercises, which required them to go beyond the instruction, was an important predictor of mathematics achievement and had a positive relationship in 8th-grade (Sezer & Cakan, 2022). In Sweden, an analysis of TIMSS 2019 data showed that teaching activities such as asking to memorize formulas and listening to the teacher were positive predictors of TIMSS 8th-grade mathematics achievement, whereas relating information to daily life was a negative predictor (Eriksson et al., 2019).
The present study aims to investigate the extent to which specific teacher actions rated by students are predicting the Romanian students’ achievement in TIMSS 2019. Results could identify specific actions that could influence student achievement and propose those actions for further research and improvement.
Therefore, the research questions guiding the present study are as follows:
RQ.1 – To what extent do teachers’ actions, as observed and experienced by students, predict 8th-grade Romanian students’ mathematics achievement in TIMSS 2019 after controlling for socio-economic status?
RQ.2 – To what extent do teachers’ actions, as observed and experienced by students, predict 8th-grade Romanian students’ physics achievement in TIMSS 2019 after controlling for socio-economic status?
RQ.3 – To what extent do teachers’ actions, as observed and experienced by students, predict 8th-grade Romanian students’ chemistry achievement in TIMSS 2019 after controlling for socio-economic status?
RQ.4 – To what extent do teachers’ actions, as observed and experienced by students, predict 8th-grade Romanian students’ biology achievement in TIMSS 2019 after controlling for socio-economic status?
RQ.5 – To what extent do teachers’ actions, as observed and experienced by students, predict 8th-grade Romanian students’ earth sciences achievement in TIMSS 2019 after controlling for socio-economic status?
Methodology, Methods, Research Instruments or Sources Used In this transversal study we investigated to what extent Romanian students’ achievement in TIMSS 2019 could be predicted by context factors related to teaching actions as perceived by the students. Socio-economic status was also included in the regression model, because it’s a variable known to have a strong positive relationship with students’ mathematics and science achievement in previous TIMSS cycles. The study sample was established following a random probability sampling process. All the schools in Romania that had the eighth grade in their composition were taken into consideration, each school having an equal chance of being chosen. Following this sampling process, a sample consisting of 199 public schools resulted. From these schools, 4,485 students (14-15 years) participated in the study. Most of the schools participating in the study are located in small towns or villages (40.7%), followed by those in the urban area (26.3%), the suburban area (9.8%), respectively the rural area, with difficult access (7.2%). Data collection was carried out through two methods: administering tests to students in mathematics and sciences and the administration of context questionnaires to students. All test booklets and context questionnaires were applied on the same day. Firstly, the test booklets were applied and then the context questionnaires. During the test period, the students were supervised by a teacher who didn’t have classes with the tested students. The study was performed using TIMSS 2019 data from the official website of TIMSS (International Association for the Evaluation of Educational Achievement, 2021). Student achievement test results for Romanian students and the 8th-grade student questionnaire were used as data sources. From the student questionnaire we extracted the following variables to be investigated as predictors: Working on problems on their own (only in math); Conducting experiments (only in sciences); Teaching actions as observed by students - each item from the composition of the Instructional Clarity scale. Frequency of homework. Socio-economic status, which is a composite measure of number of books in the home, number of home study supports and education level of parents was used as a control variable in the regression analyses. The statistical procedures conducted were descriptive analysis (frequencies and percentages) and multiple simple regression for identifying the predictors of Romanian students’ achievement in TIMSS 2019. Conclusions, Expected Outcomes or Findings The extent in which students work on their own during mathematics classes moderately predicts student achievement in mathematics. Romanian students who work more on their own have on average higher mathematics achievement in TIMSS 2019. At the same time, conducting experiments during science classes is not predicting achievement in any of the science disciplines (i.e., biology, physics, chemistry, earth sciences). From the teaching actions that were rated by students, the level of teachers being supportive in learning is a significant and moderate negative predictor of the students’ achievement in mathematics and biology. Another predictor is the level of teachers linking new lessons to previous acquisitions, predicting student achievement in mathematics, physics and chemistry. The last predictor related to teaching observed by students is the level of teachers being easily understood, which has a significant but relatively low prediction effect on achievement in mathematics, chemistry and biology. The frequency of homework received negatively predicted students’ achievement in biology, chemistry, physics and earth sciences, and did not predict achievement in mathematics at all. For sciences, the more homework they receive for a respective discipline, the lower student achievement in that discipline. TIMSS 2019 results offer a strong basis for decision-making based on scientific evidence to improve educational policies and practices related to teaching and learning mathematics and sciences. Through the proposed research, we hope to come to the aid of teachers with results that will help them to make their teaching methods more efficient in the classroom in order to improve the results of students in mathematics and science, thus making it possible to increase the advanced benchmark. References Eriksson, K., Helenius, O., & Ryve, A. (2019). Using TIMSS items to evaluate the effectiveness of different instructional practices. Instructional Science, 47, 1-18. https://doi.org/10.1007/s11251-018-9473-1 Fitzmaurice, O., O’meara, N., & Johnson, P. (2021). Highlighting the Relevance of Mathematics to Secondary School Students – Why and How. European Journal of STEM Education, 6(1). https://doi.org/10.20897/ejsteme/10895 Griffin, P., & Care, E. (2015). Assessment and teaching of 21st century skills: Methods and approach. Springer. Maass, K., Geiger, V., Ariza, M.R. & Goos, M. (2019). The Role of Mathematics in interdisciplinary STEM education. ZDM Mathematics Education, 51, 869–884. https://doi-org.am.e-nformation.ro/10.1007/s11858-019-01100-5 Mujtaba, T., Sheldrake, R., Reiss, M. J., & Simon, S. (2018). Students’ science attitudes, beliefs, and context: associations with science and chemistry aspirations. International Journal of Science Education, 40(6), 644–667. https://doi.org/10.1080/09500693.2018.1433896 Sezer, E., & Cakan, M. (2022). Role of Teacher Quality and Working Conditions in TIMSS 2019 Mathematics Achievement. Journal of Theoretical Educational Science, 15(2), 395–419. https://doi.org/10.30831/akukeg.971286 TIMSS. (2019). Encyclopedia: Education Policy and Curriculum in Mathematics and Science, Romania. https://timssandpirls.bc.edu/timss2019/encyclopedia/romania.html TIMSS. (2019). Assessment Frameworks. https://timssandpirls.bc.edu/timss2019/frameworks/ |
11:30 - 13:00 | 09 SES 16 A: Investigating Teaching Quality and Student Outcomes Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Joe O'Hara Paper Session |
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09. Assessment, Evaluation, Testing and Measurement
Paper Instructional Practice, Teacher Characteristics and Their Influence on Student Achievement in Science: A Study of TIMSS 2019 in Sweden University of Gothenburg, Sweden Presenting Author:It has long been acknowledged that science skills play a crucial role in fostering economic development (Hanushek & Woessmann, 2012) and technological innovation (Varsakelis, 2006). Therefore, governments around the world are searching for ways to effectively enhance science education. Teachers and their instructional quality play an important role in student achievement and learning (Harris & Sass, 2011). It is also emphasized that teachers are one of the essential factors to enhance student skills and knowledge improvement (Harris & Sass, 2011). However, among several factors associated with students, teaching strategies, school, and home, teacher quality has an important role in student achievement. Methodology, Methods, Research Instruments or Sources Used The present study uses Swedish TIMSS 2019 data focusing on the science domains of biology, chemistry, and physics in the eighth grade. In Sweden, approximately, 4000 8th graders and 200 classes participated in TIMSS 2019, which are taught by an average of 3 science subject-teachers in each class. The student questionnaire variable home educational resources was used as proxy of students’ socioeconomic status (SES). Teachers’ instructional practice, teacher’s years of teaching experience, completed level of formal education, and their major area of study were also selected from the teacher questionnaire data. The choice of the specific variables is justified by previous literature, indicating the influence of the included factors on student achievement. The analysis is carried out simultaneously at student and classroom levels through two-level modelling that is used to investigate the effect of instructional practice, teacher experience, and teacher qualifications on differences in student achievement in biology, chemistry, and physics, which vary because of the provision of home educational resources. The application of multilevel analysis accounts for the potential cluster effects and allows for the investigation of the proposed research questions at the student and classroom levels. The sampling weight was used to make sure that the weighted sample matches the actual sample size in Sweden. The data management was carried out in IBM SPSS Statistics 29 and the models were estimated by Mplus 8.6 (Muthén & Muthén, 1998-2017). All five plausible values were used. The two-level modelling technique was applied in a stepwise manner: • Firstly, a Confirmatory Factor Analysis was carried out to test the validity of the construct instructional practice (IP). • Next, a random slope-only model of the relationship between students’ family SES and their science achievement in each subject was run to test whether the relationship varies across different classrooms to decide upon the choice of the final model. • If the random slope was not statistically significant, the latent variable IP was related to science achievement in a two-level random intercept model, controlling for student and class-level contextual characteristics (teachers’ experience and education, and classroom SES composition). • If the random slope was significant, the compensatory effect of class-level factors on random slope was tested by regressing the random slope on the class-level factors in a two-level random intercept and random slope model. This was to account for the cross-level interaction between students’ family SES and their science achievement. Conclusions, Expected Outcomes or Findings The model fit indices suggested that the measurement model of Instructional practices fit the data well: X2(12) = 258.933, p = 0.00, RMSEA = .073 (90% CI = .065-.081), CFI = .946. SRMR = .033. Factor loading ranged from .45 to .71, indicating the measures of the latent constructs are valid and the measurement model can be established. In the second step, random-slope-only models using the five plausible values for students’ biology, chemistry, and physics achievements and home educational resources were carried out. The results showed a significant variance of the random slope indicating that the relationship between student science achievements (biology and physics) and their home educational resources vary significantly across different classrooms. Consequently, two-level random slope models using the data for biology and physics domains, and a two-level random intercept model using the data for chemistry domain were implemented. Interestingly, the results show that teachers' instructional practice has no significant influence on students’ achievement in biology, chemistry, and physics, when controlling for individual and classroom contextual characteristics. Teachers’ experience has a positively significant influence on biology achievement. However, it has no significant influence on chemistry and physics achievement at the eighth grade. In addition, teacher education and their major area of study had no significant influence on student achievement in the three domains of science. There are no significant relations between teachers’ experience, education, and classroom SES-composition and teachers’ instructional practice based on the Swedish TIMSS data. However, classroom SES-composition is a positively significant predictor of student achievement in all three domains. The results also show that teachers’ experience and their education are significantly correlated. References Act, S. E. (2010). Svensk författningssamling, Skollagen. [The Swedish Code of Statutes. Education Act] 2010: 800. Blömeke, S., Olsen, R. V., & Suhl, U. (2016). Relation of student achievement to the quality of their teachers and instructional quality. Teacher quality, instructional quality and student outcomes, 2, 21-50. Goe, L. (2007). The link between teacher quality and student outcomes: A research synthesis. National comprehensive center for teacher quality. Hanushek, E. A., & Woessmann, L. (2012). Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation. Journal of Economic Growth, 17 (4), 267–321. Harris, D. N., & Sass, T. R. (2011). Teacher training, teacher quality and student achievement. Journal of public economics, 95(7-8), 798-812. Lee, S. W., & Lee, E. A. (2020). Teacher qualification matters: The association between cumulative teacher qualification and students’ educational attainment. International Journal of Educational Development, 77, 102218. Luschei, T.F., Jeong, D.W. (2018). Is teacher sorting a global phenomenon? Cross-national evidence on the nature and correlates of teacher quality opportunity gaps. Educational Researcher. 47 (9), 556–576. Muthén, L. K., & Muthén, B. O. (1998). 1998-2017. MPlus user’s guide. Scheerens, J., & Blömeke, S. (2016). Integrating teacher education effectiveness research into educational effectiveness models. Educational research review, 18, 70-87. Kelly, D. L., Centurino, V. A. S., Martin, M. O., & Mullis, I. V. S. (2020). TIMSS 2019 encyclopedia: Education policy and curriculum in mathematics and science. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: https://timssandpirls. bc. edu/timss2019/encyclopedia.Toraman, Ç. (2019). Effective teacher characteristics. Asian Journal of Instruction, 7(1), 1-14. Varsakelis, N. C. (2006). Education, political institutions and innovative activity: A cross-country empirical investigation. Research Policy, 35(7), 1083–1090. 09. Assessment, Evaluation, Testing and Measurement
Paper Teachers' Formal Qualifications and Instruction in Grade 4: Effects on Student Achievement in Grade 4 and 6 University of Gothenburg, Sweden Presenting Author:Effective teaching is a multifaceted endeavour influenced by various factors. It extends beyond the mere possession of subject knowledge and teaching experience and is intricately tied to teaching methods (Darling-Hammond, 1997; Hudson et al., 2021; Leino et al., 2022; Shulman, 1987; Wharton-McDonald et al., 1998). International large-scale assessments (ILSA) such as the Trends in Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS) have been widely used to establish links between teachers and their students in mathematics (e.g., Toropova et al., 2019), science (e.g. Fauth et al., 2019) and in reading (e.g., Johansson et al., 2015; Myrberg et al. 2019). However, research on teacher effects has yielded conflicting and inconclusive findings (Blömeke & Olsen, 2019; Coenen et al., 2018; Goe, 2007), partly due to the diverse methodological approaches employed in various studies. A major contributing factor is the lack of comparability and precision in defining teacher competence and teaching quality indicators. Furthermore, significant variations exist among countries in terms of the length, structure, and content of teacher education and instruction, necessitating country-specific analyses with accurate information on these specific features. Investigating teacher effects on student achievement through large-scale data, such as PIRLS assessment data, presents distinct advantages. Firstly, large-scale assessments provide large samples, where whole classes of students are sampled, allowing comprehensive analysis of teacher effects across diverse student populations. Secondly, these assessments offer multiple measures for evaluating teachers and their teaching quality. However, a major challenge with ILSAs when estimating the impact of teachers on student outcomes is that we cannot account for students’ prior achievement. This complicates the task of isolating the direct influence of teachers on student learning outcomes. In the present study, we address this limitation by utilizing the Swedish PIRLS 2016 sample to which additional register information from earlier and later grades was added. This means that we are not only able to account for prior achievement but also investigate long-term teacher effects on student performance. More specifically we are investigating the relationships between teachers’ reading specializations and the short-term and long-term impact of teachers’ reading comprehension practices in grade 4 on student performance in the PIRLS assessment and students’ subject grade in Swedish in sixth grade. We make use of scores from PIRLS, students’ national test results in grade 3 as well as subject grade in Swedish in grade 6. Our research questions are:
Methodology, Methods, Research Instruments or Sources Used The present study utilizes data from the Swedish sample in PIRLS 2016, comprising 4525 students and 214 teachers. Beyond the standard PIRLS assessment information, the Swedish dataset offers noteworthy extensions: information on students’ subject grades and national test scores. This unique feature allows us to access both earlier and later performance data for students. As a result, the current design possesses two features not commonly found in traditional PIRLS design. First, the current design includes students’ prior achievement in third grade, using national test results in Swedish. Second, we can study the effects of teacher characteristics and instruction in both the short and long term. Given that the PIRLS assessment takes place in fourth grade, we can analyze teacher effects in the short term, as students have had their PIRLS teachers for approximately 7-8 months. Additionally, by utilizing achievement data from grade 6, we can address the long-term effects of reading instruction and teacher specialization, considering that students in Sweden typically have had their teacher for 2.5 years in grade 6. As predictors we selected information on teachers’ specialization/s in reading pedagogy during teacher training, information about the time spent on language and reading instruction each week, as well as information about teachers’ classroom reading comprehension activities. As student outcomes, we selected students’ reading achievement in the PIRLS 2016 and Swedish achievement in grade 6. PIRLS 2016 was conducted both on paper and online and we use the scores from the paper-based assessment. Achievement in grade 6 was collected from subject grades, a letter scale ranging from F-A which, however, was converted to a numerical scale ranging from 0-5. Because teacher effects on student achievement can result from initial differences in student achievement rather than teacher competence, we controlled for students’ prior achievement in grade 3. This measure stems from national tests scores which are ranging from 0-18 points. The study employed a hierarchical design, treating students within classrooms as nested units. The study relied on multilevel regression to account for potential cluster effects that are due to the nature of the data (e.g., Hox, 2002). Sampling weights were used to account for the stratification. The main method was Structural Equation Modeling (SEM) with latent variables to investigate the relationships between the predictors and outcomes. We used Confirmatory Factor Analysis to model latent variables of specializations and reading comprehension activities. Data analysis employed SPSS 29 and Mplus version 8 software. Conclusions, Expected Outcomes or Findings Our findings indicate a positive and significant relationship between teachers’ specializations in reading pedagogy and students’ Swedish grades (when controlling for prior achievement, β = .29 (.09), p< .01). This suggests that teachers with a specialization in reading pedagogy significantly influence student achievement, irrespective of students’ initial achievement level. However, this relationship does not extend to the PIRLS assessment results in grade 4. This discrepancy may be attributed to the fact that most students in Sweden have had their new teacher for only 7-8 months when the PIRLS assessment is administered. As a result, it is reasonable to assume that the short-term effects of the teacher may not be evident for achievement in PIRLS. Our initial investigations into the latent variable representing teachers’ reading comprehension activities did not reveal a significant relationship with the outcome variables. For this reason, we conducted further analyses to explore potential nonlinearities between reading comprehension activities and the two student outcomes, both with and without controlling for prior achievement. A significant curvilinear relationship was observed for teachers’ reading comprehension activities on PIRLS achievement and the Swedish grade. This implies that the relationship between reading comprehension activities and achievement was positive to a certain level, then declines. Further investigations of these relationships are needed. Limitations The teacher sample in PIRLS may not fully be representative of the entire teacher population in grade 4, however, the average years of teaching experience in our sample align with those of the total population. Another potential limitation could stem from ceiling effects within the measure of the prior achievement, as these may not adequately differentiate the highest performing students. However, the correlation to PIRLS achievement was relatively high. References Blömeke, S., & Olsen, R. V. (2019). Consistency of results regarding teacher effects across subjects, school levels, outcomes and countries. Teaching and Teacher Education, 77, 170-182. https://doi.org/10.1016/j.tate.2018.09.018 Coenen, J., Cornelisz, I., Groot, W., Maassen van den Brink, H., & Van Klaveren, C. (2018). Teacher characteristics and their effects on student test scores: a systematic review. Journal of economic surveys, 32(3), 848-877. https://doi.org/10.1111/joes.12210 Darling-Hammond, L. (1997). What matters most: 21st-century teaching. The Education digest, 63(3), 4. Fauth, B., Decristan, J., Decker, A.-T., Büttner, G., Hardy, I., Klieme, E., & Kunter, M. (2019). The effects of teacher competence on student outcomes in elementary science education: The mediating role of teaching quality. Teaching and Teacher Education, 86, 102882. https://doi.org/10.1016/j.tate.2019.102882 Goe, L. (2007). The link between teacher quality and student outcomes: A research synthesis. National comprehensive center for teacher quality. Hox, J. (2002). Multilevel Analysis: Techniques and Applications. Taylor and Francis. https://doi.org/10.4324/9781410604118 Hudson, A. K., Moore, K. A., Han, B., Wee Koh, P., Binks-Cantrell, E., & Malatesha Joshi, R. (2021). Elementary Teachers’ Knowledge of Foundational Literacy Skills: A Critical Piece of the Puzzle in the Science of Reading. Reading research quarterly, 56(1), S287-S315. https://doi.org/10.1002/rrq.408 Johansson, S., Myrberg, E., & Rosén, M. (2015). Formal Teacher Competence and its Effect on Pupil Reading Achievement. Scandinavian journal of educational research, 59(5), 564-582. https://doi.org/10.1080/00313831.2014.965787 Leino, K., Nissinen, K., & Sirén, M. (2022). Associations between teacher quality, instructional quality and student reading outcomes in Nordic PIRLS 2016 data. Large-scale Assessments in Education, 10(1), 25-30. https://doi.org/10.1186/s40536-022-00146-4 Myrberg, E., Johansson, S., & Rosén, M. (2019). The Relation between Teacher Specialization and Student Reading Achievement. Scandinavian journal of educational research, 63(5), 744-758. https://doi.org/10.1080/00313831.2018.1434826 Rutkowski, L., Gonzalez, E., Joncas, M., & von Davier, M. (2010). International Large-Scale Assessment Data: Issues in Secondary Analysis and Reporting. Educational researcher, 39(2), 142-151. https://doi.org/10.3102/0013189X10363170 Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard educational review, 57(1), 1-22. https://doi.org/10.17763/haer.57.1.j463w79r56455411 Toropova, A., Johansson, S., & Myrberg, E. (2019). The role of teacher characteristics for student achievement in mathematics and student perceptions of instructional quality. Education enquiry, 10(4), 275-299. https://doi.org/10.1080/20004508.2019.1591844 Wharton-McDonald, R., Pressley, M., & Hampston, J. M. (1998). Literacy Instruction in Nine First-Grade Classrooms: Teacher Characteristics and Student Achievement. The Elementary school journal, 99(2), 101-128. https://doi.org/10.1086/461918 09. Assessment, Evaluation, Testing and Measurement
Paper Instructional Quality as Mediator and Moderator of the SES and Student Achievement Relationship. Insights from Swedish TIMSS 2019 Data University of Gothenburg, Sweden Presenting Author:The relationship between student socioeconomic status (SES) and achievement is apparent in almost every educational system across the world. In the Nordic educational systems, although it may be weaker than other countries, SES is yet one of the strongest predictors of academic achievement (Sirin, 2005). Few studies, such as Myrberg & Rosén (2009), explore the direct and indirect effects of various factors on the relationship between SES and student achievement. Further investigation into these mechanisms is necessary, as SES is mostly used to control for selection bias (Gustafsson, Nilsen, & Hansen, 2018). Given the Nordic educational systems’ aim of increasing equity, an important apsect to investigate is to what degree teacher related factors influence the relationship between student SES and achievement. Previous studies have indicated that teachers account for a significant portion of variance in achievement between classrooms (Darling-Hammond, 2014). Indeed, there is a consensus that teachers are a crucial school factor, and their competence is the foundation of high-quality schools, instruction, and learning (Blömeke, Olsen & Suhl, 2016). Teacher competence (e.g., higher quality instruction, efficient classroom management etc.) positively affects student achievement (Kelcey et al., 2019) and overall may have a positive effect on reducing inequity in education (Wößmann, 2008). Specifically, an essential characteristic for effective teachers lies in their ability to deliver quality instruction, by explaining the content clearly and assessing student understanding of the subject matter (Ferguson, 2012). While instruction quality is related with student motivation, it has been documented to be positively related with student achievement in mathematics (Bergem, Nilsen & Scherer, 2016). This is an important aspect, especially in Sweden, where challenges arise due to the unequal distribution of well-qualified teachers across schools, leading to a widening achievement gap between schools and student groups (Yang Hansen & Gustafsson, 2019). Further, not many studies have investigated the effect that instructional quality has on the relationship between student SES and achievement in Nordic educational systems. Also, a limited number of studies have utilized representative International Large-Scale Assessment (ILSA) data in this context. Thus, the main objective of the present study is to investigate how instructional quality relate to equity in education. Specifically, the study focuses on how student perceptions of instructional quality may mediate or moderate the relationship between SES and eighth grade students’ math achievement in the Swedish educational system. This study is grounded in the Dynamic Model of Educational Effectiveness theory that explores factors influencing student outcomes across all school levels, which can be either equitably or inequitably distributed. The model acknowledges the nested structure of educational systems and the relationships among various factors at different levels. Specifically, the model refers to observable instructional behaviors of teachers in the classroom and includes eight instructional quality dimensions: orientation, structuring, questioning, teaching-modelling, application, time management, creating a learning environment, and classroom assessment (Creemers & Kyriakides, 2013). The research questions that guide the study are:
Methodology, Methods, Research Instruments or Sources Used The study uses cross-sectional secondary questionnaire data, to examine the relationship of SES and students perceived instructional quality with their math achievement. Particularly, it used the Swedish grade 8 data from the TIMSS 2019 cycle with a sample size of N=3996 Swedish students. Teacher instructional quality was measured using questionnaire indicators, such as ‘My teacher is good at explaining mathematics’, ‘My teacher has clear answers to my questions’, ‘My teacher links new lessons to what I already know’ etc. These items were measured on a 4-point Likert scale ranging from “agree a lot” to “disagree a lot”. For measuring student SES, information on the number of books at home and students’ responses regarding their father’s and their mother’s education was used. The number of books at home variable has been identified in several studies to be highly correlated with TIMSS achievement (e.g. Wiberg, 2019). A measure of student mathematics achievement, represented by five plausible values for each student’s math performance on a continuous scale provided by the IEA, was utilized in the analysis through a multiple imputation technique. The method of confirmatory analysis was used to test whether the data fit the measurement models, and then there were built structural models based on an extensive literature review. Multilevel structural equation modelling techniques are employed in the study, as educational systems have an inherently multi-layered structure. Students are nested within classrooms, classrooms within schools, and the schools collectively form a national educational system. When individuals are clustered within natural occuring units (e.g., classrooms, schools, etc.), they share unique components that can affect their school performance. Therefore, multilevel models are essential for decomposing variance into its originating levels (Hox, 2002). The data analyses, which was conducted in SPSS 29 and Mplus 8, incorporated student weights, cluster, and the robust maximum likelihood estimator (MLR), while the model fit was assessed using both local and global fit indices. Conclusions, Expected Outcomes or Findings The preliminary analyses have resulted in well-fitting measurement models for students’ SES and student perceived teachers’ instructional quality latent constructs. The structural models examining the direct and indirect effects of student perceived teachers’ instructional quality on math achievement resulted in an overall good model fit. Model results confirmed that both SES and student perceived instructional quality at student and classroom level significantly relate with math achievement, consistent with prior research. Also, it was found that there is a significant indirect effect of students’ SES to their math achievement through teachers’ instructional quality at the individual level. Further, it was tested the interaction effect of teachers’ instructional quality in a multilevel model. A random slope on the relation between SES and math achievement was specified and teachers’ instructional quality at classroom level was found to have a significant interaction effect on this relationship. While this study centers on teachers’ instruction quality and the connection between student SES and math achievement in Sweden, its results hold significance beyond the Swedish context. Concerns about educational equity and the importance of promoting effective teaching quality are prevalent across every democratic educational system. There is a global movement towards prioritizing equity in education (OECD, 2018), with a consistent emphasis on schooling as a key ‘equalizer’ among individuals of diverse backgrounds, crucial for countries adopting a preventative approach to economic inequality (Hanushek & Woessmann, 2015). Research on how teachers’ instruction quality influences the relationship of student socioeconomic background and academic performance sheds light on the on the pivotal role of teachers in addressing equity issues. Thus, further research is needed to examine how effective teaching contributes in fulfilling schools’ compensatory mission, mitigating the strong correlation between SES and achievement in Sweden and beyond. References Bergem, O. K., Nilsen, T., & Scherer, R. (2016). 7 Undervisningskvalitet i matematikk [7 Teaching quality in mathematics]. In Vi kan lykkes i realfag: Resultater og analyser fra TIMSS 2015, [We can succeed in science: Results and analyzes from TIMSS 2015] (pp. 120-136). Oslo: Universitetsforlaget. Blömeke, S., Olsen, R. V. & Suhl, U. (2016). Relation of Student Achievement to the Quality of Their Teachers and Instructional Quality. In T. Nilsen & J. E. Gustafsson (Eds.), Teacher Quality, Instructional Quality and Student Outcomes (pp. 21-50). Springer. Creemers, B., & Kyriakides, L. (2013). Using the Dynamic Model of Educational Effectiveness to Identify Stages of Effective Teaching: An Introduction to the Special Issue. The Journal of Classroom Interaction, 48(2), 4-10. Darling-Hammond, L. (2014). Strengthening teacher preparation: the holy grail of teacher education. Peabody Journal of Education, 89, 547–561. Ferguson, R.F. (2012). Can student surveys measure teaching quality? Phi Delta Kappa, 94(3), 24–28. Gustafsson, J. E., Nilsen, T., & Hansen, K. Y. (2018). School characteristics moderating the relation between student socio-economic status and mathematics achievement in grade 8. Evidence from 50 countries in TIMSS 2011. Studies in Educational Evaluation, 57, 16-30. Hanushek, E. A., & Woessmann, L. (2015). The knowledge capital of nations: education and the economics of growth. Cambridge: MIT Press. Hox, J. (2002). Multilevel analysis : Techniques and applications. Mahwah, N.J.: Lawrence Erlbaum. Kelcey, B., Hill, H. C., & Chin, M. J. (2019). Teacher mathematical knowledge, instructional quality, and student outcomes: a multilevel quantile mediation analysis. School Effectiveness and School Improvement, 30(4), 398-431. Myrberg, E. & Rosén, M. (2009) Direct and indirect effects of parents´ education on reading achievement among third graders in Sweden. British Journal of Educational Psychology 79, no. 4, pp. 695-711. OECD. (2018). Equity in education: breaking down barriers to social mobility. OECD publishing: Paris. Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. Wiberg, M. (2019). The relationship between TIMSS mathematics achievements, grades and national test scores. Education Inquiry, 10(4), 328–343. Woessmann, L. (2008). Efficiency and equity of European education and training policies. International Tax and Public Finance, 15, 199-230. Yang Hansen, K., & Gustafsson, J.-E. (2019). Identifying the key source of deteriorating educational equity in Sweden between 1998 and 2014 International journal of educational research, 93, 79-90. |
14:15 - 15:45 | 09 SES 17 A: Understanding the Impact of COVID-19 on Student Well-being and Academic Performance Location: Room 013 in ΧΩΔ 02 (Common Teaching Facilities [CTF02]) [Ground Floor] Session Chair: Sarah Howie Paper Session |
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09. Assessment, Evaluation, Testing and Measurement
Paper Do COVID-19 Infections Have Effects on Cognitive Abilities of Primary School Students? Results of a Representative Study in Burgenland, Austria 1University College of Teacher Education Burgenland, Austria; 2University of Oldenburg, Germany; 3Ludwig-Maximilians University München, Germany Presenting Author:The impact of the COVID-19 pandemic on children's and adolescents' development is a topic that has been intensively studied in recent educational research. However, the focus is often on the consequences that school closures and class cancellations had for students (e.g., Betthäuser, Bach-Mortensen, & Engzell, 2023; Patrinos, Vegas, & Carter-Rau, 2022). The consequences that COVID-19 infections can have on affected children and adolescents have been described primarily in clinical studies. Although younger individuals are less likely to have symptomatic infections or severe infections, they may experience symptomatic consequences that are persistent even after recovery (Behnood et al., 2022; Lopez-Leon et al., 2022). The severity of persistent consequences has been linked to the severity of symptomatology during illness (e.g., Radtke, Ulyte, Puhan, & Kriemle, 2021). For adults, such associations have already been empirically demonstrated regarding cognitive impairments (Hampshire et al., 2021). Although many clinical studies examined the consequences of COVID-19 infections in children, there is a lack of studies presenting results that are representative of specific subpopulations and that allow comparisons of groups of children that have already recovered from COVID-19 with groups that have not yet been infected. The present study addresses these research desiderata and examines the question of whether primary school children who have recovered from COVID-19 show disadvantages in terms of their cognitive abilities. Methodology, Methods, Research Instruments or Sources Used 1,761 second- and third-grade students (49.9% girls, 50.1% boys) in the federal state of Burgenland, Austria, who had parental consent to participate were examined in June 2022 (32.3% of the population of students in the school year 2021/22, from 106 (63.1%) of the 168 elementary schools in Burgenland). In addition, students’ parents or legal guardians were surveyed (n=1,438). The key independent variable was whether the children had been infected with COVID-19 at the time of the survey. According to parents and students, this was the case for n=1,253 students, whereas n=508 students had not been infected by that time. In addition, we assessed whether the infection was symptomatic or asymptomatic, which symptoms occurred, and whether a physician was consulted due to the COVID-19 infection. In addition, characteristics of students' individual and family background were surveyed (including gender, language spoken at home, native language, parents' country of birth, parental education, etc.). We used standardized instruments of the federal state of Burgenland to weight the gathered data based on state statistics. As dependent variable, cognitive ability was assessed using the Cognitive Abilities Test (KFT 1-3; Heller & Geisler, 1983) (test duration: 60 minutes), which consists of four subtests: language comprehension, relation recognition, inductive reasoning and numerical thinking. For analysis, four groups were distinguished: children who had not been infected at the time of the survey (control group, n=502) and three recovery groups (RG): asymptomatically infected children (RG1, n=251), symptomatically infected children (RG2, n=850), and symptomatically infected children who had seen a medical doctor because of the illness (RG3, n=131). The doctor's visit is considered as an indicator of a situation that gave the parents reasons for concern. According to parents, 78 percent of recovered children had been infected with COVID-19 within the five months prior to data collection. The data of the three recovery groups were compared pairwise with those of the control group. Since small, but significant differences were found between the groups regarding immigrant background, native language and language spoken at home, an analysis of covariance was conducted controlling for these variables. Missing values were treated as Missing at Random and were multiply imputed (MICE, Buuren & Groothuis-Oudshoorn, 2011; CART Breiman, et al, 1984). Data were weighted using iterative proportional fitting (IPF; Deming & Stephan, 1940; Lomax & Norman, 2019) based on representative statistics from the federal state of Burgenland. All statistical tests were conducted with an error probability of p<.05. Conclusions, Expected Outcomes or Findings Regarding cognitive abilities, RG1 and RG3 showed a significantly lower test performance in numerical reasoning than the control group (RG1: F(1,748)=7.42**, p=.007, partial Eta²=.010; RG3: F(1,627)=9.18**, p=.003, partial Eta²=.014). Moreover, RG3 also performed significantly lower in language comprehension than the control group (F(1,627)=11.26***, p<.001, partial Eta²=.018). For relation recognition and inductive reasoning, RG3 performed, in tendency, lower than the control group (F(1,627)=3.57, p=.059, partial Eta²=.006; F(1,637)=3.19, p=.075, partial Eta²=.005). Our findings suggest negative cognitive effects of COVID-19 infections for two of the recovery groups distinguished in the present study. For the recovery group of symptomatically infected children who underwent medical treatment, the findings point more strongly into this direction. The identified effects are of small size. However, given the low prevalence of longer-lasting symptoms after the infection among children (Lopez-Leon et al., 2022), these effects may imply severe consequences for the cognitive functioning of the respective children. Further analyses using propensity score matching are planned to validate our findings obtained by covariance analysis. Beyond this, it has to be considered that the effects reported here emerged at a time when, for most children, the infection happened only a few weeks or months before the survey. The extent to which these effects persist is another important question. Therefore, our sample was re-assessed in June 2023 using the same test instrument to assess students' cognitive abilities. The results of this follow-up study will be available by spring 2024 and will be included in our paper. The findings will be discussed with reference to the medical research literature as a consequence of the impaired central functions (memory, attention) and with regard to consequences for targeted educational support of children after their COVID-19 infections. References Behnood, S. A., Shafran, R., Bennett, S. D., Zhang, A. X. D., O’Mahoney, L. L., Stephenson, T. J., . . . Swann, O. V. (2022). Persistent symptoms following SARS-CoV-2 infection amongst children and young people: A meta-analysis of controlled and uncontrolled studies. Journal of Infection, 84(2), 158–170. https://doi.org/10.1016/j.jinf.2021.11.011 Betthäuser, B. A., Bach-Mortensen, A. M., & Engzell, P. (2023). A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nature Human Behaviour, 7(3), 375–385. https://doi.org/10.1038/s41562-022-01506-4 Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). CART: Classification and Regression Trees. Belmont, CA: Wadsworth. Buuren, S. van, & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03 Deming, W. E., & Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics, 11(4), 427–444. https://doi.org/10.1214/aoms/1177731829 Hampshire, A., Trender, W., Chamberlain, S. R., Jolly, A. E., Grant, J. E., Patrick, F., . . . Mehta, M. A. (2021). Cognitive deficits in people who have recovered from COVID-19. EClinicalMedicine, 39, 101044. https://doi.org/10.1016/j.eclinm.2021.101044 Heller, K., & Geisler, H. J. (1983). Kognitiver Fähigkeitstest (Grundschulform). KFT 1–3. Weinheim: Beltz. Lomax, N., & Norman, P. (2016). Estimating population attribute values in a table: “Get me started in” Iterative Proportional Fitting. The Professional Geographer, 68(3), 451–461. https://doi.org/10.1080/00330124.2015.1099449 Lopez-Leon, S., Wegman-Ostrosky, T., Ayuzo del Valle, N. C., Perelman, C., Sepulveda, R., Rebolledo, P. A., . . . Villapol, S. (2022). Long-COVID in children and adolescents: A systematic review and meta-analyses. Scientific Reports, 12(1), 9950. https://doi.org/10.1038/s41598-022-13495-5 Patrinos, H. A., Vegas, E., & Carter-Rau, R. (2022). An analysis of COVID-19 student learning loss. The World Bank. https://doi.org/10.1596/1813-9450-10033 Radtke, T., Ulyte, A., Puhan, M. A., & Kriemler, S. (2021). Long-term symptoms after SARS-CoV-2 infection in children and adolescents. JAMA, 326(9), 869–871. https://doi.org/10.1001/jama.2021.11880 09. Assessment, Evaluation, Testing and Measurement
Paper School Environments Pre- and Post- Pandemic: Exploring the Irish Context Using TIMSS and PIRLS Data Educational Research Centre, Ireland Presenting Author:The influence of the school environment on pupils’ educational outcomes has long been established (Kutsyuruba et al., 2015; Mullis et al., 2013). Having a safe, structured and encouraging learning environment is associated with higher achievement and improved wellbeing (Cohen et al., 2009; Mullis et al., 2019; Thapa et al., 2013). Therefore, research on the school environment is important as it can have practical implications for educational policy. International large-scale assessments such as the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS), which are based on nationally representative samples of pupils at the target grade at the time of the assessment, allow researchers to examine aspects of the school environment from different perspectives. Factors such as school climate and school safety and discipline can be examined in both studies. The study cycles that are of particular focus in this paper are TIMSS 2019 and PIRLS 2021. These cycles can be seen as bookending the 2019/20 and 2020/21 academic years, during which extended periods of nationwide school closures occurred in Ireland as a result of the COVID-19 pandemic. These closures resulted in disruption to in-person teaching and learning and a transition to remote learning, which could potentially have impacted the school environment in the longer term. Due to the unprecedented disruption in education that occurred between the administrations of TIMSS 2019 and PIRLS 2021, these data, stemming from school principals, class teachers, pupils, and parents/guardians, present a key opportunity to examine whether school environments in Ireland differed substantially between these time points. While we cannot infer causation when comparing cross-sectional datasets such as these, the nationally representative findings may help us to better understand the school landscape in the wake of the nationwide closures. Methodology, Methods, Research Instruments or Sources Used This analysis uses data from two studies: TIMSS 2019 and PIRLS 2021. Each study involved a representative sample of pupils in Ireland for the year the study was conducted, with 4,582 pupils in 150 schools taking part in TIMSS 2019 and 4,663 pupils in 148 schools taking part in PIRLS 2021. For TIMSS, pupils in Grade 4 were assessed on mathematics and science, while for PIRLS, pupils at the start of Grade 5 were assessed on reading literacy. In PIRLS 2021, the decision was made in Ireland (and 13 other countries) to move from spring to autumn testing because of the nationwide closures in the academic year 2020/21; therefore, pupils who participated in PIRLS in 2021 were approximately six months older than those who participated in TIMSS 2019. Context questionnaires were completed by participating pupils, their parents/guardians, school principals, and class teachers. Data on questionnaire items relating to school climate and school safety and discipline that were common to both the TIMSS 2019 and PIRLS 2021 assessments, along with pupils’ home resources for learning (as a proxy for socioeconomic status) and achievement, were analysed. School climate indices included parents’ perceptions of their child’s school, schools’ emphasis on academic success, teacher job satisfaction, and pupils’ sense of belonging at school. School safety and discipline indices included school discipline, school safety and order, and bullying. The analysis was conducted in three phases using the International Association for the Evaluation of Educational Achievement (IEA) International Database Analyzer (IDB Analyzer) (IEA, 2023). Initially, individual items comprising each index were examined. Secondly, the relationship of the indices with achievement was examined (mathematics and science for TIMSS and reading for PIRLS) with a follow-up analysis that also took pupils’ home resources for learning into account. Finally, hierarchical linear regression models were constructed to examine the extent to which the indices of interest explained achievement in each subject. In each instance, two models were tested: first, a model with only the school environment indices, and second, a model that included both the school environment indices and the home resources for learning index. The use of the IEA IDB Analyzer allowed for the adjustment of regression estimates for sampling error due to the clustered sampling design of TIMSS and PIRLS via the use of the replicate weights. Conclusions, Expected Outcomes or Findings Results point to a picture of overall stability in the school environments in Ireland between TIMSS 2019 and PIRLS 2021. In terms of school climate, the proportion of parents who were very satisfied with their child’s school remained high, ranging from 77% in 2019 to 80% in 2021. Fewer pupils in 2021 had teachers who reported that their school placed a very high or high emphasis on academic success, but, these differences were slight. Also, teacher job satisfaction was largely stable between 2019 and 2021. For example, at index level, over half of pupils were taught by teachers who reported being very satisfied in both studies, while the proportion whose teachers were less than satisfied remained small (10% in 2019 and 8% in 2021).There was a small decrease in the proportion of pupils whose teachers reported being often content with their profession as a teacher and those whose teachers very often found their work full of meaning and purpose. School safety and discipline was also relatively unchanged in the bullying and the safe and orderly school indices. In the regression models, more frequent bullying was associated with lower achievement even after home resources for learning were accounted for. Higher sense of school belonging was associated with higher achievement in all subjects when only school environment indices were included. However, in 2019 it was not significant after home resources for learning were accounted for, whereas it remained significant after they were accounted for in 2021. This may suggest an increased importance of school belonging for other student outcomes post-pandemic, which should be monitored and examined further. Overall, the stability observed in relation to the school environment pre- and post-pandemic may be viewed as positive considering the significant disruption and challenges brought on by the pandemic and associated school closures in Ireland. References Cohen, J., McCabe, E. M., Michelli, N. M., & Pickeral, T. (2009). School climate: Research, policy, practice, and teacher education. Teachers College Record, 111(1), 180–213. https://doi.org/10.1177/016146810911100108 IEA. (2023). Help manual for the IEA IDB Analyzer (Version 5.0). https://www.iea.nl Kutsyuruba, B., Klinger, D. A., & Hussain, A. (2015). Relationships among school climate, school safety, and student achievement and well-being: A review of the literature. Review of Education, 3(2), 103–135. https://doi.org/10.1002/rev3.3043 Mullis, I. V. S., Martin, M. O., & Foy, P. (2013). The impact of reading ability on TIMSS mathematics and science achievement at the fourth grade: An analysis by item reading demands. In M. O. Martin & I. V. S. Mullis (Eds.), TIMSS and PIRLS 2011: Relationships among reading, mathematics, and science achievement at the fourth grade—Implications for early learning (pp. 67–108). TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College, and International Association for the Evaluation of Educational Achievement (IEA). Mullis, I. V. S., & Martin, M. O. (Eds.). (2019). PIRLS 2021 assessment frameworks. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College, and International Association for the Evaluation of Educational Achievement (IEA). Thapa, A., Cohen, J., Guffey, S., & Higgins-D’Alessandro, A. (2013). A review of school climate research. Review of Educational Research, 83(3), 357–385. https://doi.org/10.3102/0034654313483907 09. Assessment, Evaluation, Testing and Measurement
Paper Children At Risk: Association Between PIRLS Reading Achievement and Student Well-Being in Finland University of Jyväskylä, Finland Presenting Author:Being able to read can be seen as the foundation of a functioning democracy enabling learning, equal participation in society, and a condition for a healthy and successful life (European Commission, 2023; EDUFI, 2021). Reading performance is closely linked with other areas of academic performance, and there is a strong association between student well-being in school and reading performance (European Commission, 2023). Moreover, the danger of failing to meet academic or social expectations or to complete school with a basic level of academic proficiency has been termed “at-risk” (e.g. Novosel et al., 2012). In Finland, the trends in students’ academic well-being (e.g. Helenius & Kivimäki, 2023; Read et al., 2022) and learning performance (e.g. Mullis et al., 2023; OECD, 2023) have been descending in the last decade. For example, Grade 4 students’ performance in reading has decreased from 2011 to 2021 as evidenced by the Progress in International Reading Literacy Study (PIRLS) (Mullis et al., 2023). The performance in reading declined by two points from 2011 to 2016 and by 17 points from 2016 to 2021. When examining the international reading benchmarks, the percentage of advanced achievers has dropped from 18% to 14% during this period. Meanwhile, the percentage of students at the low or below benchmark has doubled from 8% to 16%. We define these students as “students at risk”. They are in danger of not achieving adequate reading proficiency which is crucial for their learning success or failure in subsequent school years. As for student well-being, the latest School Health Promotion Study (Helenius & Kivimäki, 2023) shows that more than one third of girls and one in five boys felt that their health was average or poor in Finland. The study also reports that experiences of physical threats and bullying have recently increased. Furthermore, school burnout has increased for a long time, especially among girls (Read et al., 2022). Student well-being in school can be considered as a condition that enables positive learning outcomes but also as an outcome of successful learning and students’ satisfaction with their school experiences (Morinaj & Hascher, 2022). Student well-being in school consists of positive attitudes to school, enjoyment in school, positive academic self-concept, the absence of worries, physical complaints, and social problems in school, which can be used as indicators of well-being (Hascher, 2003). In PIRLS, student well-being is indirectly measured by several indicators – such as school belonging, academic self-concept, experience of bullying, and absenteeism (Reynolds et al., 2024). PIRLS 2021 data was collected during the COVID-19 pandemic, but then there were no school closures in Finland. However, one year before these students’ schooling was disrupted, and they spent eight weeks in distance learning. Lerkkanen et al. (2022) showed that the Finnish students’ development in reading was slower from Grade 2 to 4 in the COVID sample compared to the pre-COVID sample. Previous research has detected the association between student well-being and learning performance but also the need for further examining this relation and the role of other factors associated with reading achievement, e.g. socioeconomic background (e.g. Bücker et al., 2018; Nilsen et al., 2022). For example, Manu et al. (2023) focused on the role of gender and parental education, and Torppa et al. (2022) the effects of the home literacy environment on the development of Finnish children’s reading comprehension. In this study, we ask the following research questions, using the PIRLS reading assessment data from 2011 to 2021: 1) How has students’ well-being in school changed, if any, from 2011 to 2021? 2) How do students’ socioeconomic background and well-being factors predict the risk of low academic achievement in reading? Methodology, Methods, Research Instruments or Sources Used The present study is based on the three cycles of curriculum-based PIRLS assessment in Finland. The data includes the 4th graders who participated in PIRLS 2011 (N = 4,640), PIRLS 2016 (N = 4,896), and PIRLS 2021 (N = 7,018). In this study, we use school climate and safety, students’ attitudes, and absenteeism as indicators of well-being. School climate and safety include the scales of Students’ Sense of School Belonging (3 items) and Bullying (6 items). Students’ attitudes include the scales of Students Like Reading (5 items) and Students Confident in Reading (7 items). These four-point scales are from PIRLS student questionnaires. From each scale, we selected those items that were the same in all three cycles of PIRLS assessment. Absenteeism was asked of students (in years 2016 and 2021, not asked in 2011) by a single item reporting how often they are absent from school. As an indicator of student’s socioeconomic background, we used Home Socioeconomic Status and Home Resources for Learning scales, and Parents’ Educational Level separately. The data was analysed by using various statistical methods. To answer the second research question, binary logistic regression analysis was applied. The low achievement benchmark (cut point 474) was used as a binary response. Students’ socioeconomic background and well-being factors were used as explanatory variables. This analysis was conducted separately for each of the three PIRLS data sets. Five plausible values representing students’ proficiency in reading (see von Davier et al., 2023) were used in the analyses. A two-stage sampling design used in the PIRLS assessment (von Davier et al., 2023) was considered in the analyses. Conclusions, Expected Outcomes or Findings Overall, the Finnish 4th grade students’ well-being was relatively good. Examination of the trends of means showed that there are some changes in students’ well-being from 2011 to 2021. After 2011, students’ sense of school belonging increased, and bullying first decreased from 2011 to 2016 but increased again from 2016 to 2021. From 2011 to 2021, both students liking reading and confidence in reading decreased. The preliminary results of logistic regression showed that there were significant associations between bullying, student confident in reading, student socioeconomic background, parents’ educational level, absenteeism, gender, and low achievement in reading. In all three cycles of PIRLS (2011, 2016, and 2021), the predictive factors for the risk of low academic achievement in reading were the students’ low degree of confidence in their own reading ability, lower socioeconomic background, parents’ low educational level (in 2021 even below higher education), and gender (boy). In PIRLS 2016 and 2021 datasets, the frequency of absences from school (once a week) also predicts the risk of low academic achievement in reading. Being subjected to bullying about weekly was a risk factor in PIRLS 2021 dataset. When identifying at-risk students in reading, the results suggest that family background, especially the educational background of parents, has become more important, as has bullying. In Finland, however, about 5% of the students experienced bullying about weekly. In addition, the students’ confidence in their own reading ability seems to be a strong predictor of reading achievement. Furthermore, the gender gap in reading achievement has remained rather large favouring girls for a long time in Finland. It also seems that the factors predicting the risk of low academic achievement in reading are linked to each other. This study supports earlier research on the meaning of students’ well-being and socioeconomic background to learning. References Bücker, S., et al. (2018). Subjective well-being and academic achievement: A meta-analysis. Journal of Research in Personality, 74, 83–94. EDUFI. (2023). National Literacy Strategy 2030: Finland - the most multiliterate country in the world in 2030. Finnish National Agency for Education. https://www.oph.fi/sites/default/files/documents/National_literacy_strategy_2030.pdf European Commission. (2023). Children’s reading competence and well-being in the EU – An EU comparative analysis of the PIRLS results. https://data.europa.eu/doi/10.2766/820665 Hascher, T. (2003). Well-being in school – why students need social support. In P. Mayring & C. von Rhöneck (Eds.), Learning emotions – the influence of affective factors on classroom learning (pp. 127–142). Bern u.a Lang. Helenius, J., & Kivimäki, H. (2023). Well-being of children and young people – School Health Promotion study 2023. Finnish Institute for Health and Welfare, Statistical Report 50/2023. https://urn.fi/URN:NBN:fi-fe20230913124233 Lerkkanen, M.-K., et al. (2022). Reading and math skills development among Finnish primary school children before and after COVID-19 school closure. Reading and Writing, 36, 263–288. Manu, M., et al. (2023). Reading development from kindergarten to age 18: The role of gender and parental education. Reading Research Quarterly, 58(4), 505-538. Morinaj, J., & Hascher, T. (2022). On the relationship between student well-being and academic achievement: A longitudinal study among secondary school students in Switzerland. Zeitschrift für Psychologie, 230(3), 201–214. Mullis, I. V. S., et al. (2023). PIRLS 2021 International Results in Reading. Boston College, TIMSS & PIRLS International Study Center. https://doi.org/10.6017/lse.tpisc.tr2103.kb5342 Nilsen, T., Kaarstein, H., & Lehre, A. C. (2022). Trend analyses of TIMSS 2015 and 2019: school factors related to declining performance in mathematics. Large-scale Assessments in Education, 10(1), 1–19. Novosel, L., et al. (2012). At-risk learners. In N. M. Seel (Ed.), Encyclopedia of the science of learning (pp. 348–350). Springer. OECD. (2023). PISA 2022 Results (Volume I): The State of Learning and Equity in Education, PISA, OECD Publishing, Paris. https://doi.org/10.1787/53f23881-en Read, S., Hietajärvi, L. & Salmela-Aro, K. (2022). School burnout trends and sociodemographic factors in Finland 2006–2019. Social Psychiatry and Psychiatric Epidemiology, 57, 1659–1669. Reynolds, K.A., et al. (2024). Aspects of student well-being and reading achievement in PIRLS 2021 (PIRLS Insights). Boston College, TIMSS & PIRLS International Study Center. Torppa, M., et al. (2022). Long-term effects of the home literacy environment on reading development: Familial risk for dyslexia as a moderator. Journal of Experimental Child Psychology, 215, Article 105314. von Davier, M., et al. (Eds.). (2023). Methods and Procedures: PIRLS 2021 Technical Report. Boston College, TIMSS & PIRLS International Study Center. https://pirls2021.org/methods |
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