11. Educational Improvement and Quality Assurance
Paper
Identifying Factors of AcademicFailure to Reverse Underachievement
Malika Iskakova, Saule Serikbaikyzy, Kuandyk Askerbekov
Nazarbayev Intellectual School of chemistry and biology in Kyzylorda, Kazakhstan
Presenting Author: Iskakova, Malika
Topic: IDENTIFYING FACTORS OF ACADEMIC FAILURE TO REVERSE UNDERACHIVEMENT
Research questions:
- what are the factors affecting academic underachievement?
- what are the effective ways of reversing students' academic underachievement?
- what should be considered while providing an individual approach for a student to reverse underachievement?
Purpose: to identify the factors of an academic underachievement, to idenitfy the most effective practices to combat the academic failure of students and to identify the pecularities of a successful individual approach while reveresing academic achievement.
Theoretical Framework:
Recent studies conducted on the theory of motivation were based on the AOM (Achievement Orientation Model) theory introduced by Siegel and McCoach (2003a). The AOM theory is based on Bandura's self-efficacy theory, Weiner’s attribution theory, Eccles’ expectancy-value theory, person-environment fit theory, and Rotter’s locus of control theory (Siegle, McCoach & Roberts, 2017). According to AOM theory, students’ motivation in a combination of all three areas: student’s self-efficacy, goal – valuation, and environmental perception will positively result in student’s task engagement and academic achievement. Seigle et al., (2017) stressed that these three areas can be developed in different levels, but should not be missing at all since it negatively impacts on self-regulation as well as achievement (See Figure 1.).
Self-efficacy addresses a student’s belief to be skillful and capable to complete a task where a student might ask himself “Am I smart enough?” (Siegle, Rubensein & McCoach, 2020). Researchers agree that students with low self – efficacy tend to avoid task accomplishment, therefore, the higher self-efficacy students possess, the stronger task engagement they show (Rubenstein, Siegle, Reis, Mccoach, & Burton, 2012; Siegle et al., 2017; Siegle et al., 2020).
Environmental perception refers to a student's motivation or demotivation as a result of student’s interaction with peers, parents and teachers as well as the expectation from parents and teachers, and the scale of support a student gets from the outside world (Rubenstein et al., 2012). It is assumed that students get false perceptions and find themselves in an unsupportive environment assuming nobody believes in their success, therefore these students often lack or do not develop enough learning skills important to be academically productive (Ritchotte, Matthews & Flowers, 2014).
Methodology, Methods, Research Instruments or Sources UsedThis research adopts a constructivist methodology, wherein the knowledge is constructed through the collaborative interaction between the researcher and the students being studied (Guba & Lincoln, 1994, p. 111). The importance of their input in shaping the findings. Additionally, the researcher plays a fundamental role in facilitating the research process and engaging with the participants, as per the principles of constructivism (Guba & Lincoln, 1994, p. 113).
According to the research on "Identifying Factors of Academic Failure to Reverse Underachievement," a quantitative research method was employed (Author, Year). This involved collecting and analyzing numerical data through surveys, standardized tests, and other measurement tools to understand various factors such as academic performance, study habits, motivation levels, and socio-economic factors (Creswell, Fetters, & Ivankova, 2004). Statistical analysis techniques were then used to examine relationships and patterns within the data, identifying significant factors associated with academic failure and potential strategies for reversing underachievement (Creswell et al., 2004). This quantitative research method provided a systematic and objective approach to exploring the factors influencing academic performance, offering valuable insights into addressing the issue of underachievement (Creswell et al., 2004).
Research desing and sampling: The current research involved 45 participants to collect relevant data by using a survey. The survey was designed to identify factors that contribute to academic failure and explore potential strategies to reverse underachievement. This research design allowed for a systematic and structured approach to collect information from a relatively large sample size. By utilizing a survey, the researchers were able to gather data on various factors that may influence academic performance and analyze the responses to draw meaningful conclusions. The use of a survey as a research tool provided a standardized method for data collection, ensuring consistency and reliability in the findings. The study focused on students from a Nazarbayev Intellectual School in Kyzylorda, specifically targeting low achieveing students there. To gather participants for the study, a purposeful sampling strategy was employed. This approach was chosen because it allowed for the selection of individuals and a research site that would provide the most valuable insights into the central phenomenon being investigated, which in this case was gifted underachievement. This decision was based on the belief that these specific participants and research location would offer the most relevant and informative data for the study, as supported by Creswell (2014).
Conclusions, Expected Outcomes or Findings
Through analysis of survey results and systematic procedures, we've identified several key hindrances to educational progress, including inadequate grasp of prior material, preference for certain subjects, excessive extracurricular involvement, and psychological fatigue. These factors notably impact academic performance, particularly among 7th and 8th graders transitioning to new social environments. While some adapt smoothly, others face prolonged adjustment, necessitating tailored interventions.
A case study underscores the complexities of academic struggles, revealing familial and health-related burdens impeding a student's focus and resulting in declining grades, exacerbated by the absence of paternal guidance. Familial dynamics often contribute to suboptimal home environments, perpetuating cycles of underachievement.
Observations highlight prevalent apathy and disinterest, with external motivations, like financial security, dampening academic engagement. Recognizing these complexities, recommendations focus on fostering supportive learning environments through personalized encouragement, critical thinking cultivation, and consistent acknowledgment of achievements. Embracing a culture of learning from mistakes is pivotal to nurturing well-rounded individuals capable of academic success and holistic development.
ReferencesBezrukikh, M. M. (1996). Which children are called slow and why it is difficult for them to study. Arktous.
Glazer, G. D. (2002). Comments on articles by V. A. Sukhomlinsky. In Anthology of humane pedagogy (pp. page numbers if available). Shalva Amonashvili Publishing House.
Lokalova, N. P. (2009). School failure: causes, psychocorrection, psychoprophylaxis.
Lunkov, A. I. (1987). How to help your child at school and at home.
Ritchotte, J. A., Matthews, M. S., & Flowers, C. P. (2014). The validity of the achievement-orientation model for gifted middle school students: An exploratory study. Gifted Child Quarterly, 58(3), 183-198. DOI: 10.1177/0016986214534890
Rubenstein, L. D., Siegle, D., Reis, S. M., Mccoach, D. B., & Burton, M. G. (2012). A complex quest: The development and research of underachievement interventions for gifted students. Psychology in the Schools, 49(7), 678-694. https://doi.org/10.1002/pits.21620
Siegle, D., & McCoach, D. B. (2005). Making a difference: Motivating gifted students who are not achieving. Teaching exceptional children, 38(1), 22-27 https://doi.org/10.1177/004005990503800104
Siegle, D., McCoach, D. B., & Roberts, A. (2017). Why I believe I achieve determines whether I achieve. High Ability Studies, 28(1), 59-72. https://doi.org/10.1080/13598139.2017.1302873
Siegle, D., Rubenstein, L.D., McCoach D. B. (2020). Do you know what I'm thinking? A comparison of teacher and parent perspectives of underachieving gifted students' attitudes. Psychology in the Schools, 1–19. https://doi.org/10.1002/pits.22345
11. Educational Improvement and Quality Assurance
Paper
Teacher Shortages in Rural Communities: Dramatic Increases in Teaching Out-of-Field Across Core Disciplines
Jim Van Overschelde, Minda Lopez, Amy Wiseman
Texas State University, United States of America
Presenting Author: Van Overschelde, Jim
The worldwide teacher shortage has impacted rural communities more than urban and suburban communities (Ingersoll & Tran, 2023). When a qualified teacher is not available to teach a particular course, school principals are forced to assign unqualified people or under-qualified teachers to teach the course. If the teacher is fully trained and qualified to teach (e.g., Math), but is teaching a course outside of their training and qualifications (e.g., English), we say the teacher is teaching English out-of-field (OOF) and teaching Math in-field (du Plessis, 2015; Ingersoll, 1999; 2019). Teaching OOF is not a characteristic of the teacher, but a label that describes the misalignment between the teacher’s qualifications and the course to which they were assigned. If the teacher-of-record has no training and no license to teach, then we say the person is an Unprepared Instructor.
The American federal government changed the education laws in 2015 thereby giving states the right to define teacher qualifications as each saw fit. Prior to 2015, teaching OOF was illegal except under specific and limited conditions. Since 2015, Texas has allowed principals to freely assign teachers to courses for which they have no training, and schools are no longer required to inform parents and guardians that this is happening to their children.
Teaching OOF is harmful for student learning. Several studies have found that student learn less during a school year when taught OOF compared to similar students taught in-field (Author, 2023; Clotfelter, Ladd & Vigdor, 2010; Lankford, Loeb, & Wyckoff, 2002). Teaching OOF has become an issue of educational equity, because Author (2020) found that particular demographic groups of students were significantly more likely to be taught OOF, including Black students, low-income students, and students living in rural communities.
Rural communities have seen a dramatic increase in the number of Unprepared Instructors, with 72% of new teachers hired in rural schools in 2022-23 being unprepared and unqualified to teach, up from only 18% in 2012-13 (Author, 2024). Our goal for this study was to examine changes in the rates of teachers teaching OOF before versus after the federal legislative changes, and to examine these OOF patterns for core secondary course subjects (e.g., English, Math, Biology).
Methodology, Methods, Research Instruments or Sources UsedFor the purposes of this study, we accessed our copy of the Texas State Longitudinal System (TLDS) that includes data on 5.52 million students annually enrolled in Texas public schools. These data include extensive demographic information, educational serviced received, schools attended, attendance rates, standardized test scores, and the teachers who taught them. The teacher data includes extensive information about teaching licenses held, licensure tests taken, years of experience, and the type of teacher preparation program they completed. The state publishes detailed rules about which teaching licenses are “required” to be held by a teacher in order to teach each course so the rules for in-field versus OOF teaching are explicit.
We drew a sample of 193 million student-course records for 2011-12 through 2018-19 (pre-pandemic) from the TLDS with a focus on students in secondary grades (Grades 7-12). We selected the 18 subjects with the largest student-course enrollment counts during the 2018-19 school year; each count was in excess of 300,000 students per subject per school year. We then examined changes in the OOF teaching rates by locale (e.g., rural, urban) and by school year.
A summary of the statewide descriptive results for 2018-19 include: secondary English is the subject taught most often OOF with over 4 million student-course records taught OOF. Math is second with over 4 million, History is a distant third with under 2.5 million, and Physical Education is fourth with fewer than 2 million. The same patterns hold for rural communities, with the exception that Agriculture is the fourth most common subject taught OOF. The inferential results will be presented too.
Across all 18 course subjects, rural schools had the highest rate of OOF teaching of all geographical locales. The rates of OOF teaching increased from 13.9% in 2011-12 to 23.1% in 2018-19. Approximately 1 in 4 student-courses is now taught OOF. For comparison, major suburban schools increased from 7.7% to 11.6% over the same period. Approximately 1 in 9 student-courses is now taught OOF. In other words, students in rural communities are twice as likely to take classes taught OOF compared to students in suburban communities.
In rural schools, the subjects with the largest increases in OOF teaching rates between 2011-12 and 2018-19 are: Agriculture (506%), Biology (203%), Art (163%), Spanish (114%), Math (83%), and English (79%).
Conclusions, Expected Outcomes or FindingsWe found that students in rural communities are receiving an inferior education relative to students in suburban and urban schools. Students in rural schools have experienced dramatic increases in the number of courses taught by unqualified and under-qualified teachers since federal laws changed. Prior studies found that teaching OOF is harmful to student learning and the current results imply that the quality of education received by rural students has declined over eight years, with the rates of OOF teaching increasing by over 75% in 7 of 18 core subjects, and more than doubling in 4 of the 18 subjects.
We are now exploring ways to increase the pipeline of teachers into rural communities. We are examining where existing rural teachers went to secondary school, and what path they followed to become teachers. Preliminary results indicate the importance of 2-year post-secondary institutions for preparing rural students who go on to teach. We are examining effective exemplar programs in high schools that appear to prepare a large number of students who go on to pursue teaching careers in rural schools.
The descriptive and inferential results will be presented, as well as our findings about positive exemplars for preparing future teachers to work in rural schools.
ReferencesAuthor. (2020).
Author. (2023).
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2010). Teacher credentials and student achievement in high school: A cross subject analysis with student fixed effects. Journal of Human Resources, 45(3), 655–681.
Du Plessis, A. (2015). Effective education: Conceptualising the meaning of out-of-field teaching practices for teachers, teacher quality and school leaders. International Journal of Educational Research. 72, 89-102. doi: 10.1016/j.ijer.2015.05.005
Ingersoll, R. M. (2019). Measuring out-of-field teaching. In L. Hobbs & G. Törner (Eds.), Examining the phenomenon of ‘teaching out-of-field’: International perspectives on teaching as a non-specialist (pp. 21–52). Springer. https://doi.org/10.1007/978-981-13-3366-8_2
Ingersoll, R. M., & Tran, H. (2023). Teacher shortages and turnover in rural schools in the US: An organizational analysis. Educational Administration Quarterly, 59(2), 396-431. https://doi.org/10.1177/0013161X231159922
Lankford, H., Loeb, S., & Wyckoff, J. (2002). Teacher sorting and the plight of urban schools: A descriptive analysis. Educational Evaluation and Policy Analysis, 24(1), 37–62.
11. Educational Improvement and Quality Assurance
Paper
Quality Assurance with Learning Analytics in Secondary Education: Insights from Flanders and Ireland
Margot Joris1, Jerich Faddar1, Valérie Thomas1, Martin Brown2, Joe O'Hara2, Gerry McNamara2
1Vrije Universiteit Brussel, Belgium; 2Dublin City University
Presenting Author: Faddar, Jerich;
Brown, Martin
Since the introduction and proliferation of learning management systems in K-12 schools, as part of the digital transformation in education, a huge amount of data (learning analytics) has become generally available for (re)designing and evaluating education, and for evidence-informed quality assurance (Brown & Malin, 2022). This quality assurance (QA) has become increasingly decentralized in many European countries, making schools responsible and accountable for their own quality. Schools have therefore been developing their own procedures and exploring their responsibilities in the context of QA, but the use of learning analytics (LA) data regarding learning processes often remains un(der)explored.
Up till now, LA have primarily proven their potential for QA in the context of higher education. In secondary schools, they are currently mainly used at the micro level, where they are being used by (individual) teachers to identify and tailor to learners’ specific needs. However, the potential use of LA at the school (management) level, or the ways in which schools or school teams can make optimal use of these data, remains an open and under-explored question (Ifenthaler, 2021) for researchers, practitioners and policy makers alike (Gašević et al. 2016). Moreover, there is a lack of capacity to work with these data for strategic planning and quality development in schools. Teaching staff, school leaders and middle managers are often wondering how to start off with learning analytics data in this regard, and often invoke questions from an ethical and privacy perspective. Next to these general questions, there is also the issue of competencies. Even if school staff would know how these data could inform them, there is a general lack of competencies and know-how on how to get started (Ifenthaler, Mah & Yin-Kim Yau, 2019).
The QUALAS (Quality Assurance with Learning Analytics in Schools) project therefore aims to build on the available knowledge on QA and LA to identify possibilities for enhancing the capacity of educational professionals in secondary schools in Flanders (Belgium), Ireland, Italy and Spain to make appropriate use of learning analytics for quality assurance. In order to achieve these aims, we first want to identify how LA and QA are currently being coupled and put into practice in secondary education in these different jurisdictions. Additionally, we investigate how the approaches in two of these jurisdictions (Flanders and Ireland) draw on and relate to policy and initiatives of QA and LA at a European level.
We start from European conceptualisations of quality assurance, including definitions provided by the European Commission (2018). We further rely on the distinctions drawn between external and internal evaluation, and between government-based and market-based accountability in education (Eurydice, 2015). Concerning LA, we draw on a publication by the European Commission’s Joint Research Centre on the use of learning analytics and its action list for educational stakeholders (Ferguson et al., 2016), and on the European Union’s Digital Education Action Plan. These definitions and conceptualisations are subsequently compared to national (or regional) policy texts and other grey literature concerning QA and LA, in order to answer the following research questions:
- How is QA defined and linked to LA in the different jurisdictions?
- How do schools deal with learning analytics data? What are LA used for?
- What strategies for capacity building in the use of LA (for QA) in schools exist/are successful?
- What learning management systems are most common/popular in secondary schools and why?
Methodology, Methods, Research Instruments or Sources UsedIn this contribution, we present our findings from a grey literature analysis conducted in the educational jurisdictions of Flanders and Ireland, and situate these in relation to the European policy and research documents sketched above.
This grey literature review was conducted as part of the first phase of the Erasmus + ka cooperation project QUALAS, a cooperation between Vrije Universiteit Brussel (Belgium), Dublin City University (Ireland), Universidad de Valladolid (Spain) and Instituto nazionale per la valutazione del sistema educativo di instruzione e di formazione (Italy). This first phase consisted of a rapid narrative systematic review of the existing literature on LA and its connection(s) to quality assurance in secondary education and schools. Grey literature is generally defined as: “that which is produced on all levels of government, academics, business and industry in print and electronic formats, but which is not controlled by commercial publishers.” (Paez, 2017). In our case(s), it includes: (practice-oriented) academic publications and vulgarizing texts, government reports and policy documents, and documents of LA and learning management system providers. This grey literature was incorporated based on the belief that it can make important contributions to a systematic review, because it can provide resources and data that cannot be found within commercially published (academic) literature and can thus help avoid potential (publication) bias (Paez, 2017).
Given our focus on national education system contexts, our (grey) search strategy did not include consulting (international) grey literature databases. However, conference proceedings stemming from the results from our systematic search in scientific databases (Scopus, ScienceDirect, Web of Science and EBSCOhost) with a focus on the European context or the respective jurisdictions of Flanders, Ireland, Italy and Spain were included as grey literature items. Additionally, we conducted web searches looking for specific documents, reports and other publications on LA and QA in these jurisdictions, which were conducted in the jurisdictions’ respective languages.
All four partner institutions conducted the analysis of grey literature found for their own jurisdictions. A template was provided by the project coordinator to ensure the reliability and validity of the analyses. The analysis protocol focused on:
• Definition(s) of QA
• Types and functions of LA
• Level(s) of use of LA
• Data use and LA within QA
• Bibliometric info (year, type of publication, authors, target audience, etc.)
Conclusions, Expected Outcomes or FindingsIncluding grey literature in our systematic literature review on learning analytics for quality assurance in secondary education in four European countries, and in relation to policies and conceptualisations at a European level, provided us with significant insights additional to those provided by the systematic (scientific) literature review. In this contribution, we focus on our findings in the Flemish and Irish jurisdictions, and link these to the European level.
First, we will sketch the main findings for both the Flemish and Irish educational contexts: their approaches to QA, current use of LA in secondary education, and existing links between QA and LA in both jurisdictions. We then identify and discuss four main themes, arising from a comparison between the Flemish and Irish contexts based on the grey literature found in both jurisdictions and their relations to the European context:
1) LA and its relation to QA as expressions of digital optimism and the push for (post-covid) educational digitalisation for national and European recovery and resilience: tackling societal challenges through (digital) education and LA
2) LA for QA: focus on personalisation and differentiation in education
3) Digital education as covering two dimensions (REF): the educational use of digital technologies and devices (including LA) and the digital competence and professional development of educators
4) Reluctancy and fears connected to LA and QA, or educational push-back: the threat of hollowing out education and the teaching profession
ReferencesBrown, C. & Malin, J.R. (eds). (2022). The Emerald Handbook of Evidence-Informed Practice in Education. Emerald Publishing Limited.
European Commission. (2018). Quality assurance for school development. Guiding principles for policy development on quality assurance in school education. Retrieved from: https://education.ec.europa.eu/sites/default/files/document-library-docs/2018-wgs2-quality-assurance-school_en.pdf
Eurydice. (2015). Assuring Quality in Education: Policies and Approaches to School Evaluation in Europe. Retrieved from: https://op.europa.eu/en/publication-detail/-/publication/4a2443a7-7bac-11e5-9fae-01aa75ed71a1/language-en
Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T. & Vuorikari, R. (2016). Research Evidence on the Use of Learning Analytics - Implications for Education Policy. In Vuorikari, R. & Castaño Muñoz, J. (Eds.). Joint Research Centre Science for Policy Report. doi:10.2791/955210.
Gašević, D., Dawson, S. and Pardo, A. (2016). “How do we start? State and directions of learning analytics adoption”. International Council for Open and Distance Education.
Ifenthaler, D. (2021). Learning analytics for school and system management. In OECD Digital Education Outlook 2021. Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. OECD Library. 161.
Ifenthaler, D., Mah, D-K. & Yau, J.Y. (2019). Utilizing Learning Analytics to Support Study Success. Springer Cham. DOI 10.1007/978-3-319-64792-0
Paez, A. (2017). Gray literature: An important resource in systematic reviews. Journal of Evidence-Based Medicine,10(3), 153-240.
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