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)

School tracking in Luxembourg: the longitudinal impact of student characteristics and school composition

Scientific Conference (2022, December 05)

Research question: The current study aimed to investigate the influence of student and school level factors on school tracking in secondary education. We were especially interested in the association ... [more ▼]

Research question: The current study aimed to investigate the influence of student and school level factors on school tracking in secondary education. We were especially interested in the association between student characteristics and school composition in Grade 3 and school track in Grade 9. Data source: Data were collected as part of the Luxembourg school monitoring programme “Épreuves Standardisées” (ÉpStan; Fischbach et al., 2014). The study cohort include all students enrolled in the Luxembourg public education system in Grade 3 in November 2013 combined with data from the same students in Grade 9 in November 2017-2019 for students following advanced or regular educational pathways, completed with data from November 2020 and 2021 for students that repeated once or twice (N≈3600). Theoretical approach: The study draws upon theoretical frameworks and empirical findings (e.g., Boudon, 1974; Bourdieu, 1984), that have demonstrated students´ socio-demographic characteristics are associated with (dis)advantages for specific groups of students in education systems as well as more recent work focusing on school composition (e.g., Baumert et al., 2006), especially as tracked school systems are known to be prone to social segregation (e.g., Hadjar & Gross, 2016). To date, most research on school segregation in tracked education systems such as Luxembourg has focused on individual student´s characteristics. However, with increasing heterogeneity of student cohorts and known differences in educational opportunities related to the social and ethnic composition of the school’s student body (e.g., Thrupp et al., 2002), the current research extents the existing literature by considering both individual (including prior academic achievement and socio-demographic characteristics) and school level factors (mean academic level and percentage of students from lower socio-economic and migration background) in predicting school track placement. Main findings: Results of a multilevel random effect logistic regression analysis in which we estimated marginal effects on the probability to be placed in the highest, middle or lowest track in Luxembourg show that even after controlling for student´s academic achievement, track placement is affected by the gender and socio-economic background of the student, whereby boys and students from low SES families have less chance to be placed in the highest track. The association with socio-economic background is not only visible on the student level but also on school level, whereby students attending primary schools with a higher percentage of low SES families have less chance to be orientated to the higher track compared to the middle track, regardless of the student´ individual academic performance. [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 ▲]

How sensitive are the evaluations of a school's effectiveness to the selection of covariates in the applied value‑added model?

in Educational Assessment, Evaluation and Accountability (2022)

There is no final consensus regarding which covariates should be used (in addition to prior achievement) when estimating value-added (VA) scores to evaluate a school’s effectiveness. Therefore, we ... [more ▼]

There is no final consensus regarding which covariates should be used (in addition to prior achievement) when estimating value-added (VA) scores to evaluate a school’s effectiveness. Therefore, we examined the sensitivity of evaluations of schools’ effectiveness in math and language achievement to covariate selection in the applied VA model. Four covariate sets were systematically combined, including prior achievement from the same or different domain, sociodemographic and sociocultural background characteristics, and domain-specific achievement motivation. School VA scores were estimated using longitudinal data from the Luxembourg School Monitoring Programme with some 3600 students attending 153 primary schools in Grades 1 and 3. VA scores varied considerably, despite high correlations between VA scores based on the different sets of covariates (.66 < r < 1.00). The explained variance and consistency of school VA scores substantially improved when including prior math and prior language achievement in VA models for math and prior language achievement with sociodemographic and sociocultural background characteristics in VA models for language. These findings suggest that prior achievement in the same subject, the most commonly used covariate to date, may be insufficient to control for between-school differences in student intake when estimating school VA scores. We thus recommend using VA models with caution and applying VA scores for informative purposes rather than as a mean to base accountability decisions upon. [less ▲]

Academic Profile Development: An Investigation of Differentiation Processes Based on Students' Achievement and Grade Level

Scientific Conference (2022, April 21)

Academic achievement profiles affect students’ further development, i.e., by informing educational and professional choices. However, there is a lack of knowledge on the mechanisms behind the development ... [more ▼]

Academic achievement profiles affect students’ further development, i.e., by informing educational and professional choices. However, there is a lack of knowledge on the mechanisms behind the development of academic profiles. For research on cognitive ability profiles, specifically differentiation processes, statistical tools have been developed. In the present article, we transfer these methods for differentiation research to academic achievement data. We examine differentiation depending on students’ general level of achievement and grade level in a large Luxembourgish student sample. Students’ achievements in German, French, and Math were assessed within the Luxembourg school monitoring program. We found more balanced academic profiles with increasing achievement level. We further found more balanced profiles with increasing grade level and a positive interaction effect. [less ▲]

The Associations Between Teacher-Student-Relationships and Student Outcomes: A Systematic Review of Meta-Analyses (ReMA-TSR)

Report (2022)

The relationships between students and their teachers can impact students’ learning and development. Characterized by emotional warmth or closeness, positive teacher-student-relationships (TSR) can ... [more ▼]

The relationships between students and their teachers can impact students’ learning and development. Characterized by emotional warmth or closeness, positive teacher-student-relationships (TSR) can improve a variety of student outcomes. Existing meta-analyses suggest strong links between TSR and students’ peer relations, school engagement, academic achievement, emotions, executive functions, general well-being, and reductions in aggressive or disruptive behaviors. However, this evidence base is scattered, and a comprehensive overview of the TSR-outcome associations integrating academic, behavioral, socio-emotional, and general cognitive outcomes is lacking. Further, researchers have been unequivocal about possible moderators, such as how these relationships change with student age as their relationship to family, peers, and teachers change. Considering these research gaps, we aim to systematically review the meta-analytic literature and examine the following two research questions: Research Question 1: To what extent do existing meta-analyses provide evidence supporting significant relations between TSR and children’s academic, behavioral, socioemotional, motivational, and general cognitive outcomes? (Overall relationship) Research Question 2: To what extent do these relationships vary by the characteristics of the meta-analyses, such as student samples, measurement characteristics, and the quality of the meta-analyses? To address these research questions, we conduct a systematic review of existing meta-analyses, integrating the findings of eligible studies. We will include quantitative meta-analyses with preschool or K-12 samples who have no diagnosed disorder or disability. [less ▲]

Subjektives Wohlbefinden in der 5. und 9. Schulklasse: gibt es einen Zusammenhang mit dem Bildungsweg und der schulischen Leistung?

Scientific Conference (2022, March 09)

Der Bildungsauftrag unserer heutigen Wissensgesellschaft vereint ein vielseitiges Spektrum an Kompetenzen, die den Schüler*innen vermittelt werden sollen. Die Lernziele beinhalten nicht nur akademischen ... [more ▼]

Der Bildungsauftrag unserer heutigen Wissensgesellschaft vereint ein vielseitiges Spektrum an Kompetenzen, die den Schüler*innen vermittelt werden sollen. Die Lernziele beinhalten nicht nur akademischen Erfolg, sondern auch schulisches Wohlbefinden. In der Bildungsforschung haben affektive und sozio-emotionale Faktoren sowie deren Einfluss auf das Erreichen von Lernzielen über die letzten Jahrzehnte an Interesse gewonnen (s.a. Hascher et al., 2018). Subjektives Wohlbefinden (SWB) ist ein komplexes, multidimensionales Phänomen, welches emotionale, soziale und kognitive Facetten umfasst (Hascher & Edlinger, 2009). Das SWB wird als Grundlage für erfolgreiches Lernen betrachtet (Hascher & Hagenauer, 2011), wobei der Zusammenhang je nach Entwicklungsstadium der Schüler*innen variieren kann. Ergebnisse einer Metaanalyse (Bücker et al., 2018) zeigten eine statistisch signifikante mittlere Effektstärke für den Zusammenhang zwischen SWB und Leistung, wobei diese Ergebnisse über verschiedene Ebenen soziodemografischer Merkmale, SWB-Domäne und Indikatoren der Leistung hinweg stabil waren. Außerdem zeigten Gutman und Voraus (2012) in einer längsschnittlichen Studie mit einer Kohorte von Schüler*innen zwischen 7 und 13 Jahren, schwache bis mittlere Korrelationen zwischen unterschiedlichen Dimensionen des Wohlbefindens und aktueller sowie späterer akademischer Leistung. In dieser Studie haben wir den Zusammenhang zwischen verschiedenen Dimensionen des SWB und standardisierten Kompetenztestergebnissen zu verschiedenen Zeitpunkten (5. und 9. Schulkasse) untersucht. Ein erstes Ziel bestand darin, die Unterschiede des Wohlbefindens in Bezug auf das Entwicklungsstadium zu untersuchen, wobei wir auch den Einfluss von Klassenwiederholung und Schulzweig betrachteten. Ein weiteres Ziel der Studie bestand darin, den Zusammenhang zwischen SWB und Leistung unter Berücksichtigung sozio-demografischer Variablen zu ermitteln. Die Ergebnisse basieren auf den Daten der gesamte Kohorte von Fünft- und Neuntklässler*innen (N=5159 bzw. N=6279), die im Rahmen des nationalen Schulmonitoring (Luxembourg School Monitoring Programm „Épreuves Standardisées“; Martin et al., 2015) im November 2018 in Luxemburg erhoben wurden. Im Rahmen dieser Erhebung wurden sowohl standardisierte Schulleistungstests als auch ein Fragebogen zu soziodemographischen und sozio-emotionalen Aspekten durchgeführt. Vier Domäne des SWB wurden erfasst: Selbstkonzept, Schulangst, soziale- sowie emotionale Inklusion. Die standardisierten Leistungstests umfassten Leseverstehen in Deutsch und Französisch sowie Mathematik. Zusätzlich wurden über einen Schüler- oder Elternfragebogen weitere sozio-demographische Merkmale erfasst. Der Zusammenhang zwischen SWB und Entwicklungsstadium (Schulklasse) unter Einbeziehung von Klassenwiederholung und Schulzweig wurde mittels zwei mixed model Analysen überprüft. Die Ergebnisse zeigten, dass Schüler*innen in der 5. Klasse höhere Werte von SWB angaben als Schüler*innen in der 9. Klasse, F(8,121164)=180.61, p<.001. Zusätzlich wurde das SWB negativ beeinflusst durch Klassenwiederholung, F(8, 63989)=17.75, p<.001. Neuntklässler*innen in anspruchsvolleren Schulzweigen gaben höhere Werte von SWB an als Schüler*innen in niedrigeren Schulzweigen, F(2,40219)=15.71, p<001. Die Schulleistung wurde über eine schrittweise Regression vorhergesagt: zunächst wurden sozio-demographische Hintergrundvariable (Geschlecht, Migrationshintergrund, HISEI der Eltern) dem Model hinzugefügt und, in einem zweiten Schritt, Indikatoren des SWB. Die Ergebnisse zeigten, dass in der 5. Klasse 13% und in der 9. Klasse 19% der Varianz in der Schulleistung durch soziodemografische Variablen vorausgesagt werden kann. Sowohl für Fünft- als auch für Neuntklässler*innen, erklärten die Dimensionen des SWB zusätzliche 6% bzw. 4% der Varianz. Die Ergebnisse dieser Studie zeigten, dass Entwicklungsstadium, Klassenwiederholung und Schulzweig einen Einfluß auf das SWB der Schüler*innen haben. Darüber hinaus zeigten die Ergebnisse, dass das SWB über soziodemografische Merkmale hinaus zur Erklärung der schulischen Leistung beiträgt. In Anbetracht ihrer Ergebnisse, kann diese Studie auch die Diskussion um Klassenwiederholung als pädagogische Intervention und um die Praxis der Aufgliederung von Schüler*innen nach Leistungsniveau innerhalb und zwischen Schulformen bereichern. Während sich die meisten Studien zu den Effekten dieser Interventionen auf die schulische Leistung konzentrierten, zeigt die vorliegende Studie, dass diese Maßnahmen auch das SWB betreffen. Weitere (längsschnittliche) Studien könnten darauf eingehen, inwieweit es letztendlich zu einem kumulativen Effekt auf die schulische Leistung kommen kann oder ob und inwiefern das SWB den Zusammenhang zwischen diesen Faktoren und der schulischen Leistung beeinflussen kann. [less ▲]