09. Assessment, Evaluation, Testing and Measurement
Paper
Teacher Turnover and School Composition in Sweden: a Panel Data Approach Using Register Data
Leah Glassow
University of Gothenburg, Sweden
Presenting Author: Glassow, Leah
It is widely accepted that teachers are one of the most important school-level inputs for student academic success. Most educational research focuses on teacher effectiveness in terms of their contribution to student test scores, but there is a growing need to examine the teaching profession as an outcome in itself. A teacher workforce characterized by high turnover rates will not only negatively impact schools via administrative burdens as well as students and their educational futures, but also the teachers themselves via their working conditions and professional satisfaction.
There is a longstanding link between low-SES schools and teacher turnover, but this literature mostly comes out of the USA, with some exceptions. Disproportionate teacher turnover rates often affect lower-SES schools and classrooms in particular (Bacolod, 2007; Bonesrønning, Falch & Strøm, 2005; Hanushek et al., 2004; Feng, 2009; Glassow, 2023), impeding organizational and administrative school functioning, and potentially contributing to longer-term student behaviours such as college attendance and high school completion (Jackson, 2018). Moreover, high turnover rates may be symptomatic of worsening working conditions and professional satisfaction which have been documented in a number of education systems (Ball, 2016; Craig, 2017).
There is therefore a need to document the extent to which teachers mirror socioeconomic demographics of schools and concrete ways in which to democratize access to teacher competence in Sweden. This is a pertinent issue due to the demographic changes occurring in Sweden over the past several decades, the rising school inequality in the country (Karbownik, 2020; Yang Hansen & Gustafsson, 2016). The present study seeks to contribute to this gap in knowledge and examine whether changes in school composition (by family education level or language spoken by the students) results in changes in teacher turnover rates. Using teacher and student register data, the study first examines in a descriptive fashion whether there are growing differences between schools in terms of teacher turnover rates. Next, using a panel data model, the link between changes in student school composition and teacher turnover are explored. Whether or not causal conclusions can be made from such an approach will also be explored in the paper.
Allensworth, Ponisciak and Mazzea (2009) outline several main reasons teachers cite their dissatisfaction with certain schools: principal effectiveness, dysfunctional administration, challenging students, low salary, and limited autonomy which may be due to additional accountability practices. Vagi and Pivarova (2017) consolidate the literature employing theoretical frameworks for teacher mobility and offer person-environment fit theory (Dawis, 1992) as a theory which may encapsulate the myriad of environmental and personal factors which may be relevant for teacher mobility. While the focus of the study is on the role of socioeconomic composition of schools and classrooms in teacher mobility behaviours, person-environment fit theory allows for an accurate estimation of factors which may bias results unless they are under control, or unless proper methodologies are used which account for unobserved heterogeneity. Dawis (2004) highlights that job satisfaction or work stress are the result of successful or mismatched employees, respectively.
Against this background, the main research questions of the study are:
1) Are between school turnover rates growing in Sweden over the past several decades?
2) Do changes in socioeconomic and migration demographics of schools result in higher turnover rates? Specifically, do schools with a higher proportion of students with main languages other than Swedish exhibit a significantly higher proportion of teachers who leave?
3) Does this change depending on teacher qualifications? For example, are more experienced teachers more or less likely to leave as a result of these changes?
Methodology, Methods, Research Instruments or Sources Used
The data come from the teacher and student registry from the Swedish National Agency for Education between the years 2000 and 2013. The information is collected yearly. This registry includes all teachers employed in Swedish schools and not just a sub-sample. It contains information on teachers’ qualifications (education, specialization, experience, certification) as well as their working conditions (workplace, permanent vs. fixed-term status, and workload). The data are matched to the pupil registry for lower and upper secondary schools. Since the teachers cannot be linked to students but only to schools, the analysis concerning the socioeconomic composition is conducted at the school-level.
OLS regressions may be biased due to unobserved differences between schools and their association with the model residuals. Educational researchers are increasingly becoming aware of the advantageous of approaches using fixed effects. The analysis is conducted in posit (formerly known as RStudio) using the plm package (Croissant & Milo, 2008). Panel data techniques are employed, which account for time-invariant unobserved heterogeneity associated within the teachers (the subjects). The restriction of variation to within individuals over time account for all factors at the individual level which are constant. The remaining variation is the change in school characteristics over time. The odds of changing schools will be regressed on school characteristics related to parental education and migration composition. The analysis controls for time-varying characteristics at the school-level, such material resources or other factors, such as geographic location. The analysis also considers effect heterogeneity, in terms of whether or not the link between school composition and teacher turnover changes as a function of teacher characteristics. In a final step, the reduction in variation imposed by the fixed effects is investigated by transforming the estimate by the within-unit standard deviation, and within-unit standard deviations are presented for each school.
Conclusions, Expected Outcomes or FindingsThe study expects to find a link between student socioeconomic composition and teacher mobility, whereby schools with higher proportions of students with the right to Swedish language education and lower parental education levels experiencing higher turnover rates. A general positive trend of increasing inequality in teacher turnover between schools is also expected. It is more difficult to speculate about the effects across teacher characteristics, as research is mixed, highlighting the need for this study to shed more light on the issue (Glassow, 2023). The study will provide valuable empirical evidence regarding dimensions of inequality which are often overlooked. First, the fact that the working conditions of teacher may be becoming more unequal across job settings, and second, how this affects school functioning and cohesion from an organizational perspective.
ReferencesAllensworth, E., Ponisciak, S., and Mazzeo, C. (2009). The schools teachers leave: teacher mobility in Chicago public schools. Consortium on Chicago School Research, 1-52.
Bacolod, M. (2007). Who teaches and where they choose to teach: college graduates of the 1990s. Educational Evaluation and Policy Analysis, 29, 155-168.
Ball, S. (2016). Neoliberal education? Confronting the slouching beast. Policy Futures in Education, 14, 1046–1059.
Bonesrønning, H., Falch, T., & Strøm, B. (2005). Teacher sorting, teacher quality, and student composition. European Economic Review, 49, 457-483.
Craig, C. (2017). International teacher attrition: multiperspective views. Teachers and Teaching, 23, 859-862.
Croissant, Y., & Millo, G. (2008). Panel data econometrics in R: The plm package. Journal of Statistical Software, 27, 1–43
Dawis, R. V. (2004). Job satisfaction. In J. C. Thomas (Ed.), Comprehensive handbook of psychological assessment, Vol. 4. Industrial and organizational assessment (pp. 470–481). John Wiley & Sons, Inc..
Feng, L. (2009). Opportunity wages, classroom characteristics, and teacher mobility.
Southern Economic Journal, 75, 1165-1190.
Glassow, L. (2023). Teacher turnover and performance-based school accountability: a global issue? Journal of Education Policy, forthcoming.
Hanushek, E. A., Kain, J. F., & Rivkin, S. G. (2004). Why Public Schools Lose Teachers. The Journal of Human Resources, 39, 326-354.
Jackson, C.K. (2009). Student demographics, teacher sorting, and teacher quality: Evidence from the end of school desegregation. Journal of Labour Economics, 27, 213-256.
Jackson, C.K. (2018). What do test scores miss? The importance of teacher effects on non test score outcomes. Journal of Political Economy, 126, 2072-2107.
Karbownik, K. (2020). The effects of student composition on teacher turnover: evidence from ad admission reform. Economics of Education Review, 75.
Vagi, R., & Pivovarova, M. (2017). "Theorizing teacher mobility": a critical review of
literature. Teachers and Teaching, 23, 781-793.
Yang Hansen, K., and Gustafsson, J.E. (2016). Causes of educational segregation in Sweden –school choice or residential segregation. Educational Research and Evaluation, 22, 23-44.
09. Assessment, Evaluation, Testing and Measurement
Paper
Teachers' Job Satisfaction: Understanding the Links Between Teacher Characteristics, Sense of Workload and Job Satisfaction
Mari Lindström, Stefan Johansson, Linda Borger
Gothenburg University, Sweden
Presenting Author: Lindström, Mari
Whether or not teacher training equips teachers with the professional knowledge and competence they need to deliver high-quality teaching has been an important area of debate in recent decades (Darling-Hammond, 2016). Research has shown that teachers develop their knowledge, competence, and skills through teacher education and subject-specific specializations during teacher training (Coenen et al., 2018; Hill et al., 2019) as well as through years of teaching experience (Coenen et al., 2018) and professional development (Hill et al., 2019). There is evidence too that teachers play a key role in influencing student learning and achievement (e.g., Coenen et al.). However, despite the best of formal qualifications the conditions for the working environment and teachers’ job satisfaction can affect how teachers exercise their competence in classrooms (Collie et al., 2012). Indeed, teachers’ professional competence is recognized as a multi-dimensional construct consisting of a broad range of cognitive and affective aspects of teacher characteristics that interact with teacher work (Blömeke, 2017). For this reason, the importance of the working environment and working conditions cannot be overlooked as it has been shown in previous research that teachers’ workload affects teacher job satisfaction (Toropova et al., 2021). Teachers’ job satisfaction, in turn, is suggested to influence teacher instruction and the learning support offered to students (Klusmann et al., 2008). In addition, teachers’ working environment, in terms of greater classroom autonomy and fewer disciplinary problems (Nguyen et al., 2020) as well as the attractiveness of the teaching profession are suggested to be factors influencing whether teachers remain in the profession or not (Viac & Fraser, 2020). Furthermore, research also shows that school socio-economic status (SES) is associated with teachers’ working conditions and well-being. Teachers working in schools with a lower socio-economic status report not only higher mental workload but also poorer well-being (Virtanen et al., 2007). Considering that working conditions and job satisfaction are associated with important teacher and student outcomes more studies that examine relationships within this area are needed.
Many European countries struggle with changes in recruitment to the teaching profession, declining status of the teaching profession, and increasing teacher turnover (eg., Skaalvik & Skaalvik, 2011). In Sweden, these issues are perhaps particularly pertinent (Alatalo et al., 2021; Holmlund et al., 2020) since Sweden also faces increasing school segregation and increasing achievement gaps (Yang Hansen & Gustafsson, 2016). Against this background, the present study aims to investigate factors related to teachers’ workload and job satisfaction in Swedish compulsory schools. Our theoretical point of departure is based on Blömeke’s (2017) modelling of teachers’ professional competence. Teacher competence is modelled as a multi-dimensional construct where all teacher resources play together to deal with the demands and challenges of the classroom. We investigate the relationships between different teacher characteristics and working conditions and teachers’ sense of job satisfaction. We hypothesize that teachers with more experience and a subject-specific specialization in mathematics have higher job satisfaction. More time in the profession may have helped teachers to find coping strategies but not only that, more specialized teachers are likely to work with the subject and grade they are trained for. This in turn could reduce the workload and increase the sense of job satisfaction. Moreover, we hypothesize that schools’ socio-economic composition is associated with teachers’ sense of workload and job satisfaction. More specifically, our research questions are:
1.) To what extent are teachers’ characteristics related to their working conditions and job satisfaction?
2.) Do teachers’ sense of workload and job satisfaction vary depending on students’ socio-economic background?
Methodology, Methods, Research Instruments or Sources UsedThe current study is based on data from Sweden’s participation in the most recent Trends in International Mathematics and Science Study (TIMSS 2019). TIMSS is organized by the International Association for the Evaluation of Educational Achievement (IEA) and assesses fourth-grade and eighth-grade students’ mathematics and science achievement on a 4-year cycle. The data was retrieved from the official website of TIMSS (http://timssandpirls.bc.edu) and we took advantage of the data from the background questionnaires to the fourth-grade teachers.
To answer our research questions, we selected information about teachers’ sense of current workload, and information about teachers’ sense of job satisfaction indicated by items concerning their sense of being a teacher. Further, we selected information of teachers’ teaching experience and subject orientation during teacher training. TIMSS provides a detailed specification of the differences in subject specializations, and we used this information to categorize a variable that indicated higher and lower degrees of specialization for teaching mathematics in grade four. From students, we retrieved information about their socio-economic background measured by the number of books at home. However, further elaboration on the socio-economic background will be carried out with variables from both student and caregivers’ questionnaire answers.
TIMSS has a hierarchical design with students nested in classrooms/teachers and for this reason, 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. Through means of confirmatory factor analysis (CFA), we modelled latent variables for teachers’ workload and job satisfaction. The latter was used as an outcome variable in a multilevel structural equation model to investigate the relationships to workload, teaching experience, and subject specialization/s. By means of student background information, we constructed a variable that aimed to capture school segregation and was used in the analysis to measure differences in teacher workload and job satisfaction between classrooms/schools. The next step is to include a similar analysis for grade 8 to compare how the results differ between grades. These analyses are to be carried out during spring. The main programs for data analysis were SPSS 29 and Mplus version 8 software.
Conclusions, Expected Outcomes or FindingsThe initial results demonstrate that teachers’ sense of less workload has a significant relationship to teachers’ sense of better job satisfaction in grade 4 (b= .33 (.10), p= .001). The workload indicators (e.g., too much material to cover, too many hours, too many administrative tasks, and the need for more time to prepare and more time to assist students), indicated large variability among teachers, and the results suggest that more experienced teachers experience a higher level of workload (b= -.27 (.08), p= .001). However, no significant relationship between experience and job satisfaction was found. Having a specialization aimed at mathematics and science teaching, in turn, has a significant positive relationship with teachers’ sense of less workload (b= .26 (.08), p= .01), but no significant relationship with job satisfaction. When adding school SES as a control variable into the model, the relationships between experience and specialization and workload change only slightly, suggesting that the socio-economic status of the school does not decrease/increase the relationships to any greater extent. The results indicate that the relationship between workload and job satisfaction is the same for teachers regardless of the school’s SES. In a next step, we aim to shed light on differences across grades by means of data from 8th grade. We expect to see some differences due to the different working conditions for teachers in grade 4 and 8 teachers. For example, in Sweden, teachers in grade 8 assign grades as opposed to teachers in grade 4 and this might be one factor that increases teacher workload.
There are several limitations to this study. First, causal relationships examined in the study cannot be supported due to the cross-sectional study design. Another limitation is that the study is threatened by single-source bias by the self-reported questionnaire answers of teachers.
ReferencesAlatalo, T., Hansson, Å., & Johansson, S. (2021). Teachers' academic achievement: evidence from Swedish longitudinal register data. European journal of teacher education, ahead-of-print(ahead-of-print), 1-21. https://doi.org/10.1080/02619768.2021.1962281
Blömeke, S. (2017). Modelling teachers' professional competence as a multi-dimensional construct. In Pedagogical Knowledge and the Changing Nature of the Teaching Profession (p. 119-135). OECD Publishing. https://doi.org/10.1787/9789264270695-7-en
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
Collie, R. J., Shapka, J. D., & Perry, N. E. (2012). School Climate and Social-Emotional Learning: Predicting Teacher Stress, Job Satisfaction, and Teaching Efficacy. Journal of Educational Psychology, 104(4), 1189-1204. https://doi.org/10.1037/a0029356
Darling-Hammond, L. (2016). Research on Teaching and Teacher Education and Its Influences on Policy and Practice. Educational researcher, 45(2), 83-91. https://doi.org/10.3102/0013189X16639597
Hill, H. C., Charalambous, C. Y., & Chin, M. J. (2019). Teacher Characteristics and Student Learning in Mathematics: A Comprehensive Assessment. Educational policy (Los Altos, Calif.), 33(7), 1103-1134. https://doi.org/10.1177/0895904818755468
Holmlund, H., Sjögren, A., & Öckert, B. (2020). Jämlikhet i möjligheter och utfall i den svenska skolan (Rapport 2020:7), [Equality in opportunities and outcomes in the Swedish school]. Institutet för Arbetsmarknads- och Utbildningspolitisk Utvärdering.
Hox, J. (2002). Multilevel Analysis: Techniques and Applications. Taylor and Francis. https://doi.org/10.4324/9781410604118
Klusmann, U., Kunter, M., Trautwein, U., Lüdtke, O., & Baumert, J. (2008). Teachers' Occupational Well-Being and Quality of Instruction: The Important Role of Self-Regulatory Patterns. Journal of Educational Psychology, 100(3), 702-715. https://doi.org/10.1037/0022-0663.100.3.702
Nguyen, T. D., Pham, L. D., Crouch, M., & Springer, M. G. (2020). The correlates of teacher turnover: An updated and expanded Meta-analysis of the literature. Educational research review, 31, 100355. https://doi.org/10.1016/j.edurev.2020.100355
Skaalvik, E. M., & Skaalvik, S. (2011). Teacher job satisfaction and motivation to leave the teaching profession: Relations with school context, feeling of belonging, and emotional exhaustion. Teaching and Teacher Education, 27(6), 1029-1038. https://doi.org/10.1016/j.tate.2011.04.001
Toropova, A., Myrberg, E., & Johansson, S. (2021). Teacher job satisfaction: the importance of school working conditions and teacher characteristics. Educational review, 73(1), 71-97. https://doi.org/10.1080/00131911.2019.1705247
Virtanen, M., Kivimäki, M., Elovainio, M., Linna, A., Pentti, J., & Vahtera, J. (2007). Neighbourhood socioeconomic status, health and working conditions of school teachers. Journal of epidemiology and community health (1979), 61(4), 326-330. https://doi.org/10.1136/jech.2006.052878
Yang Hansen, K., & Gustafsson, J.-E. (2016). Causes of educational segregation in Sweden - school choice or residential segregation. Educational research and evaluation, 22(1-2), 23-44. https://doi.org/10.1080/13803611.2016.1178589
09. Assessment, Evaluation, Testing and Measurement
Paper
Teacher Beliefs on the Nature of Mathematics: Do These Affect Students’ Motivation and Enjoyment of Mathematics Across Different European Countries
Xin Liu1, Jelena Radišić1, Kajsa Yang Hansen2,3, Nils Buchholtz4, Hege Kaarstein1
1University of Oslo; 2University West; 3University of Gothenburg; 4University of Hamburg
Presenting Author: Liu, Xin
Mathematics competence plays a crucial role in solving problems, developing analytical skills, and providing the essential foundation to build knowledge in understanding the content of other school subjects. However, international large-scale assessment (ILSA) studies have pointed to significant cross-country variation in students’ mathematics competency levels and their motivation to learn mathematics (Mullis et al., 2020).
Students’ motivation is seen as the driving force behind their learning of mathematics over time (Wigfield et al., 2016). This is coupled with more recent ideas on the need to support strong mathematics self-efficacy (Parker et al., 2014) and positive academic emotions. Expectancy-value theory points out that achievement-related choices are motivated by a combination of students’ expectations for success and task value in particular domains (Eccles & Wigfield, 2020). Control-value theory focuses on the emotions experienced while students are involved in an achievement activity, such as the succeeding or failing emotions that arise as an outcome of an achievement activity (Pekrun et al., 2017). Indeed, empirical evidence speaks in favour of a significant relationship between teachers holding beliefs about their students’ learning, teaching a particular subject or its nature, and student motivation and their academic achievement (Muis & Foy, 2010). Given the influence of teachers’ beliefs, research has found that students report gender-stereotyped teacher ability expectations, particularly in domains of mathematics (e.g., Dickhauser & Meyer, 2006; Lazarides & Watt, 2015). Meanwhile, motivation differs in specific-domain and genders, such as mathematics learning motivation (Eccles & Wigfield, 2020).
Notwithstanding, the positive relationship between motivation and achievement in mathematics has been confirmed (e.g. Garon-Carrier et al., 2016), yet different theoretical perspectives have led to diverse ways of capturing motivation, and thus different strengths and directions of the relationship (Pipa et al., 2017). Our review of the literature found that, while many studies have measured and incorporated motivation, the nature of the relationship between teacher beliefs and motivation, for example, whether gender mediates this relationship, remains unclear. This is particularly important in the context of motivation and its development, given motivation is also seen as an essential outcome of learning. The present study is designed to investigate the relationship between teachers’ beliefs of the nature of mathematics and different aspects of students’ motivation following the Expectancy-value (Eccles & Wigfield, 2020) and enjoyment of mathematics (Pekrun et al., 2017), focusing on gender differences in motivational patterns. Building upon the conceptual framework and research objective, we focus on the following research questions (a) Do teachers’ beliefs about the nature and learning of math affect students’ motivation and enjoyment, taking into account students’ math achievement and classroom composition? (b) Are these mechanisms different between boys and girls?
Methodology, Methods, Research Instruments or Sources UsedData were collected from 3rd and 4th-grade mathematics teachers and their students across six European countries (i.e., Norway, Finland, Sweden, Portugal, Estonia, and Serbia). The scale used to capture teachers’ beliefs on the nature of mathematics was adapted from the Teacher Education and Development Study in Mathematics (TEDS-M; Laschke & Blömeke, 2014). Students’ answers were collected with the Expectancy-Value Scale (Peixoto et al., 2022), subscales of intrinsic value, utility, and perceived competence, while enjoyment was captured with a subscale from the Achievement Emotions Questionnaire-Elementary School (AEQ-ES; Lichtenfeld et al., 2012). Math achievement was measured by a test covering major curricular topics developed using established TIMSS items (Approval IEA-22-022). A joint math competence scale was established across grades due to overlapping items in the grade-specific tests. Mplus was used for statistical analyses (Muthén & Muthén,1998-2017). Missing data were handled using FIML. In all analyses, we used the robust maximum likelihood estimator (MLR). Confirmatory factor analysis (CFA) was applied to examine the measurement properties of latent constructs and test measurement invariance across six countries. We specified two-level random slope structural equation models, using the classroom as the between-level. The model assumed the within-classrooms estimate of the slope and intercept for the regression of students’ motivation and enjoyment on gender (0=girl, 1=boy) as random coefficients. Therefore, the model estimated the mean and the variance of the slopes and the intercepts. A separate analysis was conducted for all six educational systems. We refer to the estimated coefficients of the moderators as Slope_ Enjoy, Slope_ Intrinsic, Slope_ PC, and Slope_Utility. If the mean of the slope is significant, it will imply that the effect of gender did not vary between classrooms but differs within the classroom. The significant variance of slope shows that the effect of gender varies between classrooms.
In the next step, we examined whether the student motivation and enjoyment – gender slope can be explained by classroom teacher beliefs about the nature of mathematics (i.e.., mathematics as a set of rules or as a process of inquiry), taking into account student mathematics achievement and classroom composition. This is, therefore, an investigation of cross-level interaction, i.e., if classroom teacher beliefs about the nature of mathematics moderate the within-classroom relationship between gender and student motivation and enjoyment of mathematics learning. The models also included the regression of classroom mathematics achievement on the classroom composition (i.e., % low SES students and % students with behavioural problems).
Conclusions, Expected Outcomes or FindingsMetric invariance across countries and grades was confirmed for motivation dimensions (i.e., intrinsic value, utility, and perceived competence), enjoyment and teacher beliefs about the nature of mathematics (mathematics as a set of rules or as a process of inquiry). The estimate of the variance and mean of the Slope tended to be small, and, in most cases, they were non-significant. The variance of Slope_Intrinsic is significant in five countries (excl. Finland), and Slope_ Enjoy is significant in four countries (excl. Estonia and Serbia). The variance Slope_PC is significant in Portugal. Norway and Sweden have a significant variance of Slope_Utility. Correlations between estimates from the negative significant slope regressions were only found in Portugal for Slope_PC on Inquiry and Slope_PC on Rules. The results showed that the effect of inquiry and rules on students’ perceived competence was gender-specific and higher for girls in Portugal. If teachers' beliefs on the nature of mathematics were stronger, girls reported higher perceived competence related to mathematics. The mean of Slope_PC was significant in all six countries. This pattern may reflect gender differences within the classroom, and girls perceiving themselves to be less competent in mastering mathematics.
Observations from the student-level models indicate that students’ intrinsic value and perceived competence positively relate to their enjoyment of math in all six countries. The positive relations between utility and enjoyment were confirmed in Finland, Norway, Serbia, and Sweden. At the classroom level, boys were more externally motivated (i.e. higher utility value) to learn mathematics in classrooms composed of students from socioeconomically disadvantaged families in Norway. Girls’ intrinsic value was higher in Norwegian and Swedish classrooms saturated by more students with behavioural problems.
ReferencesDickhauser, O., & Meyer, W. (2006). Gender differences in young children’s math ability attributions. Psychology Science, 48(1), 3–16.
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61.
Garon-Carrier, G., Boivin, M., Guay, F., Kovas, Y., Dionne, G., et al. (2016)., Intrinsic Motivation and Achievement in Mathematics in Elementary School: A Longitudinal Investigation of Their Association. Child Development, 87, 165–175.
Laschke, C., & Blömeke, S. (2014). Teacher Education and Development Study: Learning to Teach Mathematics (TEDS-M 2008). Dokumentation der Erhebungsinstrumente. Waxmann Verlag.
Lazarides, R., Rubach, C., & Ittel, A. (2017). Adolescents’ Perceptions of Socializers’ Beliefs, Career-Related Conversations, and Motivation in Mathematics. Developmental Psychology, 53(3), 525-539.
Lichtenfeld, S., Pekrun, R., Stupnisky, R.H., Reiss, K., & Murayama, K. (2012). Measuring students’ emotions in the early years: The Achievement Emotions Questionnaire-Elementary School (AEQ-ES). Learning and Individual Differences 22, 190-201.
Muis, K. R., & Foy, M. J. (2010). The effects of teachers’ beliefs on elementary students’ beliefs, motivation, and achievement in mathematics. In L. D. Bendixen & F. C. Feucht (Eds.), Personal epistemology in the classroom: Theory, research, and implications for practice (pp. 435–469). Cambridge University Press.
Mullis, I. V. S., Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. (2020). TIMSS 2019 International Results in Mathematics and Science.Boston College, TIMSS & PIRLS International Study Center.
Muthén, L. K., & Muthén, B. (2017). Mplus user’s guide: Statistical analysis with latent variables. Wiley.
Parker, P. D., Marsh, H. W., Ciarrochi, J., Marshall, S., & Abduljabbar, A. S. (2014). Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. Educational Psychology, 34(1), 29-48.
Peixoto, F., Radišić, J., Krstić, K., Hansen, K. Y., Laine, A., Baucal, A., Sõrmus, M., & Mata, L.(2022). Contribution to the Validation of the Expectancy-Value Scale for Primary School Students. Journal of Psychoeducational Assessment, https://doi.org/10.1177/07342829221144868
Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child Development, 88(5), 1653-1670.
Pipa, J., Peixoto, F., Mata, L., Monteiro, V., & Sanches, C. (2017). The Goal Orientations Scale (GOS): Validation for Portuguese students. European Journal of Developmental Psychology, 14(4), 477-488.
Wigfield, A., Tonks, S., & Klauda, S. L. (2016). Expectancy-value theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook on motivation in school (2nd ed., pp. 55–76). Routledge.
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