Teacher-Student Relationships and Student Outcomes: A Systematic Review of Meta-Analyses and Second-Order Meta-Analysis

E-print/Working paper (2023)

Teacher-student relationships (TSRs) play a vital role in establishing a positive school climate and promoting positive student outcomes. Several meta-analyses have suggested significant associations ... [more ▼]

Teacher-student relationships (TSRs) play a vital role in establishing a positive school climate and promoting positive student outcomes. Several meta-analyses have suggested significant associations between TSRs and, for example, academic achievement, a lack of disruptive behavior, school engagement, peer relationships, motivation, executive functions, and general well-being. However, these meta-analyses have differed substantially in TSR-outcome relationships, moderators, and quality, thus complicating the interpretation of these findings. In this preregistered systematic review of meta-analyses plus original second-order meta-analyses (SOMAs), we aimed to (a) synthesize the meta-analytic evidence on relationships between TSRs and student outcomes, (b) map influential moderators of these relationships, and (c) assess the methodological quality of the meta-analyses. We synthesized over 70 years of educational research in 24 meta-analyses encompassing a total of 116 effect sizes based on more than 2 million prekindergarten and K-12 students. We conducted several three-level SOMAs and found that TSRs had similar strong significant relationships with eight clusters of outcomes: academic achievement, academic emotions, appropriate student behavior, behavior problems, executive functions and self-control, motivation, school belonging and engagement, and student well-being. Age, gender, and informant (student-, peer-, or teacher-assessments) were the most frequently examined moderators in prior research, and our moderator analyses suggested student grade level and social minority status as moderators. We further found large differences in quality between the meta-analyses, and these differences were not associated with the TSR-outcome relationships. These results map the field of TSR research; present their relationships, moderators, and meta-analytic quality; and show how TSRs can contribute to improving outcomes in students via relationship building. Future research should follow meta-analytic open science procedures to improve quality and reproducibility. [less ▲]

The intersectionality of school and student factors in predicting academic achievement

Scientific Conference (2023, August 22)

For several decades, sociological research has studied determinants of educational inequalities, whereby most researches have focused on individual students’ characteristics (e.g., Boudon, 1974; Bourdieu ... [more ▼]

For several decades, sociological research has studied determinants of educational inequalities, whereby most researches have focused on individual students’ characteristics (e.g., Boudon, 1974; Bourdieu, 1984), though others also considered system variables such as school composition and segregation (e.g., Jencks, 1974). However, few studies have addressed the possible interaction of system and student characteristics in relation to student academic outcomes (Gross et al., 2016). Educational inequalities in Luxembourg – with a highly stratified, multilingual education system, further characterised by a large proportion of students with a 1st or 2nd generation migrant status - are related to student characteristics (i.e., socio-economic status and migration status) (e.g., Lenz & Heinz, 2018) as well as schools’ social composition (Martins & Veiga, 2010). The present study aimed to investigate especial the intersectional impact of students´ academic and socio-demographic characteristics, school composition and school tracks on students’ academic performance in Luxembourg. It draws on longitudinal data collected as part of the Luxembourg school monitoring programme “Épreuves Standardisées” (ÉpStan; Fischbach et al., 2014) and included all students enrolled in public education Grade 3 (November 2013) matched with data from the same students in Grade 9 (November 2017-2021) including those repeating once or twice (N≈3600). Results of multilevel mixed effects regression analyses show that both Math and language achievement in Grade 9 is affected by student characteristics (gender, SES, migration background and prior achievement), as well as by the school track and school composition (i.e., percentage of Low SES families in 3rd Grade). In addition, some cross-level interaction effects were found. For example, results show that after controlling for prior performance and other individual characteristics, the gender gap in math achievement is more pronounced in the higher than in the middle school track. These results indicate that not only student and system variables, but also their intersectionality affect student achievement outcomes. More specifically, accounting for socio-demographic student characteristics and prior achievement, our results demonstrate a long-term effect of school composition on students´ educational pathways. Student and system characteristics have a direct effect on academic achievement as well as an indirect effect via school tracking. Furthermore, student and system variables interact such that achievement differences between certain groups of students (e.g., boys) may be exacerbated by system characteristics (i.e., school composition). Results will be discussed in relation to theory as well as their possible implications for tailored policy making. References Boudon, R. (1974). Education, opportunity and social inequality: changing prospects in Western society. Wiley. Bourdieu, P. (1984). Distinction: A social critique of the Judgement of taste (translated by R. Nice). Harvard University Press. Fischbach, A., Ugen, S., & Martin, R. (2014). ÉpStan Technical Report. University of Luxembourg ECCS research unit/LUCET. www.epstan.lu Gross, C., Gottburgsen, A., & Phoenix, A. (2016). Education systems and intersectionality. In A. Hadjar & C. Gross (Eds.), Education systems and inequalities (pp. 51–72). Policy Press. Jencks, C. (1974). Inequality: A re-assessment of the effect of family and schooling in America. Lane. Lenz, T., & Heinz, A. (2018). Das Luxemburgische Schulsystem: Einblicke und Trends. In T. Lentz, I. Baumann, & A. Küpper. (Eds.), Nationaler Bildungsbericht Luxemburg 2018 (pp. 22–34). Université du Luxembourg (LUCET) & SCRIPT. Martins, L., & Veiga, P. (2010). Do inequalities in parents’ education play an important role in PISA students’ mathematics achievement test score disparities? Economics of Education Review, 29(6), 1016–1033. https://doi.org/10.1016/j.econedurev.2010.05.001 [less ▲]

Quels impacts des contextes socio-économique et linguistique sur l’apprentissage des mathématiques au Luxembourg ?

Scientific Conference (2023, August 17)

This article presents the results of longitudinal data collection from the Luxembourg national school monitoring (Standardised Tests, ÉpStan) on the mathematical performance of students with a regular or ... [more ▼]

This article presents the results of longitudinal data collection from the Luxembourg national school monitoring (Standardised Tests, ÉpStan) on the mathematical performance of students with a regular or delayed school career from grade 3 to grade 9 according to their linguistic and socio-economic backgrounds. Since Luxembourg has a trilingual education system and a high level of immigration, the extent to which students' linguistic and socio-economic backgrounds influence their educational path will be addressed. The results prove with what Martin and Houssemand had already shown in 2003: multilingualism and the socio-economic background of students, as practiced in Luxembourg, negatively influence the acquisition of mathematical skills. Moreover, this influence increases over the years, which reduces the efficiency and equity of the educational system. In conclusion, recommendations for teaching mathematics in multilingual classrooms will be proposed in order to promote equal opportunities for students attending school in Luxembourg, and to help them develop their skills to the fullest. [less ▲]

European Public School Report 2023: Preliminary results on student population, educational trajectories, mathematics achievement, and stakeholder perceptions

Report (2023)

• Luxembourg is a highly diverse country in terms of the socioeconomic, sociocultural, and linguistic composition of its population. This diversity is reflected in the national education system with an ... [more ▼]

• Luxembourg is a highly diverse country in terms of the socioeconomic, sociocultural, and linguistic composition of its population. This diversity is reflected in the national education system with an increasing share of students speaking a language other than Luxembourgish and/or German at home. In order to deal more adequately with the increasing language diversity of the student population and to counter educational inequalities that presumably result (at least in part) from a curriculum that places high language expectations on a growing number of students, the Luxembourgish government has broadened the educational offer by introducing European public schools (EPS). These schools follow the European curriculum and allow students to select one main language of instruction among the offered language sections (i.e., German, French, and English). • By combining data from different sources (e.g., administrative student data, expert interviews with stakeholders, achievement scores in mathematics from the Luxembourg School Monitoring Programme “Épreuves Standardisées” - ÉpStan), the present report offers preliminary results on EPS in Luxembourg. They consist of (1) the societal demand for EPS; (2) the composition of the student population in EPS; (3) the perception of EPS by school management teams and parents, and tangible education outcomes in the form of (4) educational trajectories; and (5) academic achievement in mathematics among EPS students compared to their peers in schools following the Luxembourgish curriculum. Described below are the key preliminary findings for each of these aspects: (1) Since 2016, a total of six EPS have opened in different locations across Luxembourg and the amount of students attending EPS has increased considerably at both primary and secondary school level. With the number of applicants surpassing the number of places currently available in EPS, it can be concluded that there appears to be high demand for EPS. (2) With students having a low socioeconomic status (SES) and/or students speaking Portuguese at home taking up the offer of EPS less frequently than high SES students and/or students speaking French or English at home, the student population in EPS differs from the student population in schools following the Luxembourgish curriculum (e.g., nationality, language primarily spoken at home, SES). (3) School management teams and parents report a rather positive perception of EPS, with the extended linguistic offer (i.e., possibility to select a language section) being the main reason why parents select EPS for their child. (4) Looking at the educational trajectories of EPS students, preliminary results offer a tentative indication of EPS students showing less school delay than their peers in school following the Luxembourgish curriculum and high continuity in their educational trajectories (i.e., the vast majority of students remains in EPS instead of changing curriculum). (5) With regard to achievement in mathematics at primary school level, the present report indicates that students in EPS perform better than their peers in schools following the Luxembourgish curriculum. At secondary school level, EPS students perform better than their peers in Enseignement secondaire général - voie d'orientation (ESG) and in Enseignement secondaire général - voie de préparation (ESG-VP), while staying below the performance of Enseignement secondaire classique (ESC) students. Although low SES students or Portuguese speaking students in EPS show better achievement scores than their respective peers in schools following the Luxembourgish curriculum it is not yet possible to draw strong conclusions based on these preliminary findings as these student groups currently take up the EPS offer less frequently than their peers considered as advantaged in the context of schooling. Their number is currently too small to allow more robust and in-depth statistical analyses. • The present report’s findings, especially regarding the tangible educational student outcomes, however, must be considered as tentative due to important methodological limitations. Indeed, the small numbers of students in EPS, particularly so for student groups with specific background characteristics (e.g., low SES students, Portuguese speaking students), do not allow separate analyses based on language section, for example. Thus any identified pattern could be sensitive to the inclusion or exclusion of outliers (e.g., students with particularly high or low ÉpStan scores). In addition, the comprehensive EPS school system at secondary school level (i.e., common track) is compared to the ability-based tracked school system of schools following the Luxembourgish curriculum, which limits the interpretability of secondary school data. Regarding the academic achievement tasks in mathematics, it should be noted that they were developed using education standards of the Luxembourgish curriculum. It is thus possible that achievement was underestimated for EPS students (e.g., assessment of mathematical concepts that have not yet been introduced in EPS). To this date, the ÉpStan administered in EPS only assessed academic achievement in mathematics for which a bigger overlap between curricula is assumed than for language subjects (e.g., German, French). Current psychometric shortcomings (e.g., different timepoints of language introduction within the language section in EPS, task development, comparability of tasks) do not yet allow to assess academic achievement in language subjects. • Considering that the ÉpStan do not currently include a measure that operationalises the learning environment, the present report is unable to draw any conclusions regarding which EPS aspect contributes decisively in explaining the observed differences in educational outcomes. Nevertheless, three potential explanations are presented for further exploration: better linguistic fit in EPS (i.e., students learning to read and write in their native or a related language), structural differences between school offers (e.g., primary and secondary education within one institution, the institutionalized quality assurance and flexibility in teacher recruitment in EPS), and the differences in the composition of the student population (i.e., lower uptake rate of the EPS offer by low SES students and Portuguese speaking students). • The finding that low SES students and Portuguese speaking students take up the EPS offer less frequently than their high SES peers and French or English speaking students, and that the EPS student population differs from the student population in schools following the Luxembourgish curriculum, could potentially result out of three main hurdles: namely (1) the application of selection criteria considering that the demand for EPS is surpassing the number of available places (i.e., the linguistic and/or academic profile of applying students is taken into consideration); (2) lacking system knowledge regarding the characteristics of Luxembourg’s education system among all actors involved in education (which makes it difficult to take informed decisions on a student’s education); and (3) potential organizational challenges that hamper the uptake of the EPS offer (e.g., geographical location of the EPS). • In light of the tentative result that students in EPS show better educational outcomes than many of their peers in schools following the Luxembourgish curriculum, two main implications for educational policy can be deduced. First, the student composition of EPS could be diversified in a targeted manner. This could be achieved, for example, by a) encouraging EPS to target student groups considered as disadvantaged in the context of schooling (e.g., low SES students) more effectively, and by b) fostering an encompassing system knowledge (e.g., characteristics, similarities and differences of the two school offers) among all actors involved in education (e.g., teachers, parents, educational advisors, school psychologists) to allow parents to take an informed decision on their child’s education. A second implication would be to introduce certain characteristics of EPS in schools following the Luxembourgish curriculum (e.g., extending the linguistic offer as in the French literacy acquisition pilot project currently implemented in four C2.1 classes). • By progressively integrating EPS into the well-established Luxembourg School Monitoring Programme, the ÉpStan will allow for a more in-depth analysis of potential educational outcome differences between EPS and schools following the Luxembourgish in the future. With the aim of providing reliable data for evidence-based policy making in the field of education, the results from the ÉpStan could in turn be used for the creation of school offers in which all students can make use of their full academic potential irrespective of their individual background characteristics (e.g., SES, language background). [less ▲]

Early childhood education and care in Luxembourg. Attendance and associations with early learning performance.

Report (2023)

Luxembourg’s student population is highly diverse in terms of language and family background and shows disparities in learning performances as early as first grade (Cycle 2.1). Achievement gaps might be ... [more ▼]

Luxembourg’s student population is highly diverse in terms of language and family background and shows disparities in learning performances as early as first grade (Cycle 2.1). Achievement gaps might be increased by the high language demands in the traditional Luxembourgish school system. Early Childhood Education and Care (ECEC) including for instance crèche, précoce and Cycle 1, is one of the possible mechanisms to reduce these gaps that is currently discussed by researchers, policy makers, and the broad public. A lot of international literature points towards a positive association of ECEC and child development. However, findings vary widely with characteristics of ECEC, as well as characteristics of children and their families. For this report, we used data from the Luxembourg School Monitoring Programme “ÉpStan” from 2015 to 2021 including students’ learning performances in three domains in Cycle 2.1 – Luxembourgish listening comprehension, early literacy, mathematics – as well as student and parent questionnaire data. Additionally, data from ÉpStan 2022 on German and Luxembourgish listening comprehension and students’ language exposure at home are presented. Who attends which type of ECEC in Luxembourg? We find that the attendance in ECEC is generally high. On average, crèches were attended at a moderate level of intensity and duration. Family background (socioeconomic status, migration background and home language group) interacts in a complex way with attendance in ECEC. For example, children from families with a high socioeconomic status speaking Portuguese or French at home, attended crèche for more hours a week than children from families with a high socioeconomic status speaking Luxembourgish at home. In regard to language exposure in ECEC, Luxembourgish appears to play a dominant role for most children. How are ECEC attendance and family background associated with learning performance in Cycle 2.1? Most importantly, non-formal (crèche) and formal types of ECEC (précoce, Cycle 1) have positive but small to moderate associations with learning performance in the three learning domains. Looking at crèche attendance in more detail, effects of crèche intensities are different for Portuguese speaking and Luxembourgish speaking children – i.e., only Portuguese speaking children benefit from higher intensity attendance in crèche. As can be expected, all children benefit most in their Luxembourgish listening comprehension if they attended a crèche in which Luxembourgish was spoken. Well-known performance disparities in the three learning domains between children of different backgrounds have been confirmed – with advantages for native, Luxembourgish speaking children from higher socioeconomic backgrounds. Is the pattern of differences between children of different home language groups the same in Luxembourgish and German listening comprehension? Children’s performances in German listening comprehension show even larger disparities between home language groups than those in Luxembourgish listening comprehension. This argues against the assumption of a transfer from Luxembourgish to German language skills for all children. Conclusively, this report points towards ECEC as a key adjustable parameter to improve learning development and concludes with the call to collect data on ECEC quality. Structural (e.g., child-caregiver-ratio) and procedural (e.g., characteristics of interaction) aspects of quality should be regulated and systematically evaluated to ensure positive child development and equal opportunities for every child. With more monitoring data on diverse quality aspects and language practices in ECEC, important insights on the effects of new reforms in the educational system could be gained. Additionally, the present results reveal a significant negative relationship between children’s learning performance and a previous allongement de cycle in Cycle 1, calling for a thorough revision of this frequently used procedure. Finally, the continuity between languages in ECEC and the successive schooling is important. This alignment is currently not ensured due to more flexible language policies in ECEC and more rigid language practices in formal schooling. For example, the plurilingual education in ECEC promoting Luxembourgish and French, could build a solid basis for a French literacy acquisition, yet explicit promotion of the current instruction language of reading and writing acquisition, German, in Cycle 2 is still missing. A crucial demand therefore arises to revise the language demands in the curricula and policies – to continuously support ECEC’s plurilingual education in formal schooling (e.g., European and international schools or French literacy acquisition) and to explicitly promote German in ECEC to build a solid basis for literacy acquisition in German. [less ▲]

Lehrer-Schüler-Beziehungen und ihre Korrelate: Ein systematisches Review von Meta-Analysen

Scientific Conference (2023, March 01)

THEORETISCHER HINTERGRUND Gute Beziehungen zur eigenen Lehrerin können sich positiv auf den Erfolg eines Schülers auswirken. Dieser Effekt kann mit Bowlby’s (1982) Bindungstheorie erklärt werden und wird ... [more ▼]

THEORETISCHER HINTERGRUND Gute Beziehungen zur eigenen Lehrerin können sich positiv auf den Erfolg eines Schülers auswirken. Dieser Effekt kann mit Bowlby’s (1982) Bindungstheorie erklärt werden und wird empirisch immer wieder gestützt (z.B. Hamre & Pianta, 2001). Positive Lehrer-Schüler-Beziehungen zeichnen sich durch emotionale Wärme und Nähe aus; negative Aspekte durch Konflikt und Abhängigkeit. So stehen positive Lehrer-Schüler-Beziehungen nicht nur mit akademischen Leistungen in Verbindung, sondern auch mit einer Vielzahl anderer, wünschenswerter Schülerentwicklungen. Zahlreiche Meta-Analysen deuten auf signifikante Zusammenhänge zwischen Lehrer-Schüler-Beziehungen und schulischem Engagement, guten Beziehungen zu Gleichaltrigen, exekutiven Funktionen, allgemeinem Wohlbefinden und der Verringerung aggressiver oder störender Verhaltensweisen hin (Endedijk et al., 2021; Nurmi, 2012; Roorda et al., 2017; Vandenbroucke et al., 2018). Diese Befunde sind jedoch weit verstreut in der Literatur, sodass Forschungslücken unentdeckt bleiben. Auch unterscheiden sich bisherige Überblicksarbeiten in ihren Methoden und den gefundenen Zusammenhängen zwischen Lehrer-Schüler-Beziehungen und Ergebnisvariablen von Schüler*innen. Darüber hinaus ist die Literatur uneindeutig, welche Moderatoren (z.B. Alter oder Geschlecht) diese Beziehungen beeinflussen. Gleichzeitig variiert die Qualität der Meta-Analysen in diesem Feld merklich, was die Interpretation ihrer Ergebnisse erschweren kann. FRAGESTELLUNG Angesichts dieser Forschungslücken haben wir die meta-analytische Literatur systematisch durchsucht und zusammengefasst (Cooper & Koenka, 2012), um einen Überblick über Korrelate von Lehrer-Schüler-Beziehungen zu schaffen. Hierbei untersuchten wir drei Forschungsfragen 1. Inwieweit hängen akademische, verhaltensbezogene, sozio-emotionale, motivationale und kognitive Schülereigenschaften mit Lehrer-Schüler-Beziehungen in der meta-analytischen Literatur zusammen? 2. Welche Moderatoren beeinflussen diese Zusammenhänge? 3. Welche methodische Qualität haben die einbezogenen Meta-Analysen? METHODE Um diese Forschungsfragen zu beantworten, analysierten wir 24 Meta-Analysen, die rund 130 Effektstärken für über eine Million Schüler*innen umfassten. Nach der Präregistrierung erfolgte eine systematische Literatursuche. Während mehrerer Runden der Überprüfung mithilfe unserer Ein- und Ausschlusskriterien identifizierten wir 24 passende Meta-Analysen. Aus diesen Meta-Analysen extrahierten wir die Effektstärken zum Zusammenhang von Lehrer-Schüler-Beziehungen und akademische, verhaltensbezogene, sozio-emotionale, motivationale und allgemeine kognitive Schülereigenschaften. Für die Forschungsfragen 1 und 2 haben wir die Ergebnisse zusammengefasst und einen narrativen Überblick erarbeitet. Für Forschungsfrage 3 bewerteten wir die Qualität der Meta-Analysen mit Hilfe der AMSTAR-2 Skala (angepasst an korrelative Studien in der Psychologie und Bildungsforschung; Shea et al., 2017). ERGEBNISSE UND IHRE BEDEUTUNG Mit Blick auf die Lehrer-Schüler-Beziehungen werden unterschiedliche Ergebnisvariablen analysiert (Forschungsfrage 1). Die stärksten Zusammenhänge zeigten sich für Konflikt und Abhängigkeit in der Lehrer-Schüler-Beziehung mit Verhaltensproblemen der Schüler*innen (r = .35 bis .57; Nurmi, 2012). Positive Lehrer-Schüler-Beziehungen zeigte die stärkste Verbindung mit der Beteiligung in der Schule (r = .26 bis .34; Roorda et al., 2011), prosozialem, externalisierendem und internalisierendem Verhalten (r = .25; Endedijk et al., 2021) sowie mit Lernmotivation in Kombination mit Beteiligung der Schüler*innen (r = .23; Wang et al., 2020). Alter oder Klassenstufe waren die am häufigsten untersuchten Moderatoren mit teilweise gegenläufigen Befunden (Forschungsfrage 2). Geschlechterunterschiede wurden dagegen seltener festgestellt. Gleichzeitig wurde der Effekt der Informationsquelle häufig untersucht, d.h., ob und auf welche Weise Lehrkräfte, Gleichaltrige oder die Schüler*innen selbst die Lehrer-Schüler-Beziehung bewerteten. Für Forschunsgfrage 3 diskutieren wir die Qualitätsunterschiede der Meta-Analysen. Mit dem systematischen Review von Meta-Analysen fassen wir die Forschungslandschaft zu Korrelaten von Lehrer-Schüler-Beziehungen zusammen und zeigen, in welchem Zusammenhang diese mit Lehrer-Schüler-Beziehungen stehen. Unseren Ergebnissen folgend sollten Lehrkräfte für die Wirkung von Lehrer-Schüler-Beziehungen und deren Zusammenhängen sensibilisiert werden. Einige Interventionen zur Verbesserung von dieser wichtigen Beziehungen wurden bereits meta-analytisch mit vielversprechende Ergebnissen untersucht (Kincade et al., 2020). Ein nächster Schritt ist nun die experimentelle Überprüfung der gefundenen Korrelate, um positive Lehrer-Schüler-Beziehungen als wirksame Strategie zur Verbesserung von akademischen, verhaltensbezogenen, sozio-emotionalen, motivationalen und kognitiven Schülereigenschaften kausal zu bestätigen. LITERATUR Bowlby, J. (1982). Attachment and loss: Vol. 1. Attachment. (2nd ed., Vol. 1). Basic Books. Cooper, H., & Koenka, A. C. (2012). The overview of reviews: Unique challenges and opportunities when research syntheses are the principal elements of new integrative scholarship. American Psychologist, 67(6), 446–462. https://doi.org/10.1037/a0027119 Decristan, J., Kunter, M., & Fauth, B. (2022). Die Bedeutung individueller Merkmale und konstruktiver Unterstützung der Lehrkraft für die soziale Integration von Schülerinnen und Schülern im Mathematikunterricht der Sekundarstufe. Zeitschrift für Pädagogische Psychologie, 36(1–2), 85–100. https://doi.org/10.1024/1010-0652/a000329 Endedijk, H. M., Breeman, L. D., van Lissa, C. J., Hendrickx, M. M. H. G., den Boer, L., & Mainhard, T. (2021). The Teacher’s Invisible Hand: A Meta-Analysis of the Relevance of Teacher–Student Relationship Quality for Peer Relationships and the Contribution of Student Behavior. Review of Educational Research, 003465432110514. https://doi.org/10.3102/00346543211051428 Givens Rolland, R. (2012). Synthesizing the Evidence on Classroom Goal Structures in Middle and Secondary Schools: A Meta-Analysis and Narrative Review. Review of Educational Research, 82(4), 396–435. https://doi.org/10.3102/0034654312464909 Hamre, B. K., & Pianta, R. C. (2001). Early Teacher-Child Relationships and the Trajectory of Children’s School Outcomes through Eighth Grade. Child Development, 72(2), 625–638. https://doi.org/10.1111/1467-8624.00301 Kincade, L., Cook, C., & Goerdt, A. (2020). Meta-Analysis and Common Practice Elements of Universal Approaches to Improving Student-Teacher Relationships. Review of Educational Research, 90(5), 710–748. https://doi.org/10.3102/0034654320946836 Korpershoek, H., Harms, T., de Boer, H., van Kuijk, M., & Doolaard, S. (2016). A Meta-Analysis of the Effects of Classroom Management Strategies and Classroom Management Programs on Students’ Academic, Behavioral, Emotional, and Motivational Outcomes. Review of Educational Research, 86(3), 643–680. https://doi.org/10.3102/0034654315626799 Lei, H., Cui, Y., & Chiu, M. M. (2016). Affective Teacher—Student Relationships and Students’ Externalizing Behavior Problems: A Meta-Analysis. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01311 Nurmi, J.-E. (2012). Students’ characteristics and teacher–child relationships in instruction: A meta-analysis. Educational Research Review, 7(3), 177–197. https://doi.org/10.1016/j.edurev.2012.03.001 Roorda, D. L., Jak, S., Zee, M., Oort, F. J., & Koomen, H. M. Y. (2017). Affective Teacher–Student Relationships and Students’ Engagement and Achievement: A Meta-Analytic Update and Test of the Mediating Role of Engagement. School Psychology Review, 46(3), 239–261. https://doi.org/10.17105/SPR-2017-0035.V46-3 Roorda, D. L., Koomen, H. M. Y., Spilt, J. L., & Oort, F. J. (2011). The Influence of Affective Teacher–Student Relationships on Students’ School Engagement and Achievement: A Meta-Analytic Approach. Review of Educational Research, 81(4), 493–529. https://doi.org/10.3102/0034654311421793 Shea, B. J., Reeves, B. C., Wells, G., Thuku, M., Hamel, C., Moran, J., Moher, D., Tugwell, P., Welch, V., Kristjansson, E., & Henry, D. A. (2017). AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, j4008. https://doi.org/10.1136/bmj.j4008 Vandenbroucke, L., Spilt, J., Verschueren, K., Piccinin, C., & Baeyens, D. (2018). The Classroom as a Developmental Context for Cognitive Development: A Meta-Analysis on the Importance of Teacher–Student Interactions for Children’s Executive Functions. Review of Educational Research, 88(1), 125–164. https://doi.org/10.3102/0034654317743200 Wang, M.-T., L. Degol, J., Amemiya, J., Parr, A., & Guo, J. (2020). Classroom climate and children’s academic and psychological wellbeing: A systematic review and meta-analysis. Developmental Review, 57, 100912. https://doi.org/10.1016/j.dr.2020.100912 [less ▲]

The formation of academic self-concept and interest in primary school: Examining the generalized internal/external frame of reference model with first- and third-grade children

in Contemporary Educational Psychology (2023)

Value-Added Scores Show Limited Stability over Time in Primary School

in PLoS ONE (2022), 17(12), 0279255

Value-added (VA) models are used for accountability purposes and quantify the value a teacher or a school adds to their students’ achievement. If VA scores lack stability over time and vary across outcome ... [more ▼]

Value-added (VA) models are used for accountability purposes and quantify the value a teacher or a school adds to their students’ achievement. If VA scores lack stability over time and vary across outcome domains (e.g., mathematics and language learning), their use for high-stakes decision making is in question and could have detrimental real-life implications: teachers could lose their jobs, or a school might receive less funding. However, school-level stability over time and variation across domains have rarely been studied together. In the present study, we examined the stability of VA scores over time for mathematics and lan- guage learning, drawing on representative, large-scale, and longitudinal data from two cohorts of standardized achievement tests in Luxembourg (N = 7,016 students in 151 schools). We found that only 34–38% of the schools showed stable VA scores over time with moderate rank correlations of VA scores from 2017 to 2019 of r = .34 for mathematics and r = .37 for language learning. Although they showed insufficient stability over time for high-stakes decision making, school VA scores could be employed to identify teaching or school practices that are genuinely effective—especially in heterogeneous student populations. [less ▲]

What Primary Schools Are Doing Right: Educational Value-Added in Luxembourg

Poster (2022, November 10)

In such a diverse context as Luxembourg, educational inequalities can arise from diverse languages spoken at home, a migration background, or a family’s socioeconomic status. This diversity leads to ... [more ▼]

In such a diverse context as Luxembourg, educational inequalities can arise from diverse languages spoken at home, a migration background, or a family’s socioeconomic status. This diversity leads to different preconditions for learning math and languages (e.g. the language of instruction) and thus shapes the school careers of students (Hadjar & Backes, 2021). The aim of the project Systematic Identification of High Value-Added in Educational Contexts (SIVA) was to answer the questions (1) what highly effective schools are doing “right” or differently and (2) what other schools can learn from them in alleviating inequalities. In collaboration with the Observatoire National de la Qualité Scolaire, we investigated the differences of schools with stable high value-added (VA) scores to those with stable medium or low VA scores from multiple perspectives. VA is a statistical regression method usually used to fairly estimate schools’ effectiveness considering diverse student backgrounds. First, we identified 16 schools which had a stable high, medium, or low VA scores over two years. Second, we collected data on their pedagogical strategies, student background, and school climate through questionnaires and classroom observations. Third, we matched our data to results from the Luxembourg School Monitoring Programme ÉpStan (LUCET, 2021). We selected the variables based on learning models focusing on aspects such as school organization or classroom management (e.g., Hattie, 2008; Helmke et al., 2008; Klieme et al., 2001). We further investigated specificities about the Luxembourgish school system, which are not represented in international school learning models (such as the division into two-year learning cycles, the multilingual school setting, or the diverse student population). We will discuss the SIVA-project, its goals, and its data collection leading to data from observations in 49 classroom and questionnaires with over 500 second graders, their parents, their teachers, as well as school presidents and regional directors. Literature Hadjar, A., & Backes, S. (2021). Bildungsungleichheiten am Übergang in die Sekundarschule in Luxemburg. https://doi.org/10.48746/BB2021LU-DE-21A Hattie, J. (2008). Visible Learning: A synthesis of over 800 meta-analyses relating to achievement (0 ed.). Routledge. https://doi.org/10.4324/9780203887332 Helmke, A., Rindermann, H., & Schrader, F.-W. (2008). Wirkfaktoren akademischer Leistungen in Schule und Hochschule [Determinants of academic achievement in school and university]. In M. Schneider & M. Hasselhorn (Eds.), Handbuch der pädagogischen Psychologie (Vol. 10, pp. 145–155). Hogrefe. Klieme, E., Schümer, G., & Knoll, S. (2001). Mathematikunterricht in der Sekundarstufe I: “Aufgabenkultur” und Unterrichtsgestaltung. TIMSS - Impulse für Schule und Unterricht, 43–57. LUCET. (2021). Épreuves Standardisées (ÉpStan). https://epstan.lu [less ▲]

School Segregation in Primary and Secondary Education in Luxembourg: Track Placement and Academic Achievement

Scientific Conference (2022, November 09)

Known as a highly stratified education system with early tracking (similar to Dutch, German, Austrian, and German-speaking Swiss systems), Luxembourg features additional properties that add to its ... [more ▼]

Known as a highly stratified education system with early tracking (similar to Dutch, German, Austrian, and German-speaking Swiss systems), Luxembourg features additional properties that add to its complexity in the educational realm (Backes & Hadjar, 2017). It is a simultaneously multilingual system that also has the largest share of students born outside of Luxembourg or parents born abroad. While most migrants come from within Europe, they frequently come from either a particularly high or low socio-economic background. It has been scientifically established that the educational inequalities in Luxembourg are driven mostly by social origin and immigration/language background. Gender is another critical dimension of disadvantage; for example, boys are less motivated to obtain higher education than girls (Hadjar, Scharf, & Hascher, 2021). In addition, gender often intersects with other factors such as immigrant background in shaping disadvantages. However, evidence shows that – beyond individual background characteristics – schools’ social composition also perpetuates inequalities in student achievement (Martins & Veiga, 2010). Therefore, we focus on the role of school-level segregation on student’s academic outcomes over time using data of a longitudinal cohort from the School Monitoring Programme (Éprueve Standardisée (ÉpStan)) with 5097 students in Grade 3 observed in 2013 and later in Grade 9 observed in 2019 (regular pathways) and 2020 and 2021 (irregular pathways, i.e., class repetitions). School segregation is an aggregate measure of the proportion of students who belong to low socio-economic background and the proportion of students born abroad and/or do not speak instruction language at home. Our contribution aims to provide insights into the following questions: 1. Does school-level segregation in primary education (G3) predict student’s track placement in secondary education? 2. Does school-level segregation in primary education (G3) predict student’s math and German achievement in secondary education (G9)? 3. How strongly are achievement outcomes in G9 correlated with within- and between-track segregation in G9? The findings will serve as a complementary base for tailored policy making with respect to the long-term impact of school composition for teaching and learning, especially within a tracked school system. References Becker, S., & Hadjar, A. (2017). Educational trajectories through secondary education in Luxembourg: How does permeability affect educational inequalities? Schweizerische Zeitschrift Für Bildungswissenschaften, 39(3), 437–460. https://doi.org/10.25656/01:16659 Hadjar, A., Scharf, J., & Hascher, T. (2021). Who aspires to higher education? Axes of inequality, values of education and higher education aspirations in secondary schools in Luxembourg and the Swiss Canton of Bern. European Journal of Education, 56(1), 9–26. https://doi.org/10.1111/ejed.12435 Martins, L., & Veiga, P. (2010). Do inequalities in parents’ education play an important role in PISA students’ mathematics achievement test score disparities? Economics of Education Review, 29(6), 1016–1033. https://doi.org/10.1016/j.econedurev.2010.05.001 [less ▲]