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 ▲]

Teacher-Student-Relationships and Student Outcomes in Heterogeneous Educational Settings: A Systematic Review of Meta-Analyses

Scientific Conference (2022, November 09)

Especially in diverse educational settings, positive relationships between students and their teachers can foster students’ learning and help alleviate systematic inequalities. Characterized by emotional ... [more ▼]

Especially in diverse educational settings, positive relationships between students and their teachers can foster students’ learning and help alleviate systematic inequalities. Characterized by emotional warmth or closeness, positive teacher-student relationships (TSR) can improve several student outcomes. For instance, existing meta-analyses suggest significant 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, the evidence on these links is scattered, and a comprehensive overview of the associations with TSR integrating academic, behavioral, socio-emotional, motivational, and general cognitive outcomes is lacking. Furthermore, researchers have been unequivocal about possible moderators, such as how these relationships vary with student age or gender. In light of these research gaps, we systematically reviewed the meta-analytic literature and examined (a) the extent to which academic, behavioral, socio-emotional, motivational, and general cognitive student outcomes are related to TSR in the meta-analytic literature; (b) which moderators influence this association; and (c) the methodological quality of the included meta-analyses. We included meta-analyses with preschool or K-12 samples in our dataset which reported some measure of the relation between TSR and student outcomes. With this dataset, we systematically mapped the evidence on (a) the TSR-outcome relationship; (b) the moderators; and (c) the methodological quality of the meta-analyses. We will present our core findings and discuss future research with this second-order, meta-analytic dataset and the impact of positive TSR in diverse and heterogeneous settings. [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 ▲]

Uncovering everyday dynamics in students’ perceptions of instructional quality with experience sampling

in Learning and Instruction (2022), 81

Why we need systematic reviews and meta-analyses in the testing and assessment literature

in European Journal of Psychological Assessment (2022)

Stability of Value-Added Models: Comparing Classical and Machine Learning Approaches

Scientific Conference (2021, September)

Background: What is the value that teachers or schools add to the evolution of students’ performance? Value-added (VA) modeling aims to answer this question by quantifying the effect of pedagogical ... [more ▼]

Background: What is the value that teachers or schools add to the evolution of students’ performance? Value-added (VA) modeling aims to answer this question by quantifying the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds (e.g., Braun, 2005). A plethora of VA models exist, and several outcome measures are in use to estimate VA scores, yet without consensus on the model specification (Everson, 2017; Levy et al., 2019). Furthermore, it is unclear whether the most frequently used VA models (i.e., multi-level, linear regression, and random forest models) and outcome measures (i.e., language and mathematics achievement) indicate a similar stability of VA scores over time. Objectives: Drawing from the data of a highly diverse and multilingual school setting, where leveling out the influence of students’ backgrounds is of special interest, we aim to (a) clarify the stability of school VA scores over time; (b) shed light on the sensitivity toward different statistical models and outcome variables; and (c) evaluate the practical implications of (in)stable VA scores for individual schools. Method: Utilizing the representative, longitudinal data from the Luxembourg School Monitoring Programme (LUCET, 2021), we examined the stability of school VA scores. We drew on two longitudinal data sets of students who participated in the standardized achievement tests in Grade 1 in 2014 or 2016 and then again in Grade 3 two years later (i.e., 2016 and 2018, respectively), with a total of 5875 students in 146 schools. School VA scores were calculated using classical approaches (i.e., linear regression and multilevel models) and one of the most commonly used machine learning approaches in educational research (i.e., random forests). Results and Discussion: The overall stability over time across the VA models was moderate, with multilevel models showing greater stability than linear regression models and random forests. Stability differed across outcome measures and was higher for VA models with language achievement as an outcome variable as compared to those with mathematics achievement. Practical implications for schools and teachers will be discussed. [less ▲]

Measuring Executive Functions and their Relation to Math Intelligence in Preschool Children: A Meta-Analysis

Scientific Conference (2021, July)

Introduction: Executive functions (inhibition, attention shifting, updating) are linked to math intelligence in school students and adults. This link is particularly important because performance in ... [more ▼]

Introduction: Executive functions (inhibition, attention shifting, updating) are linked to math intelligence in school students and adults. This link is particularly important because performance in school mathematics is predictive of various competencies later in life. While some researchers argue that tests of executive functions and math intelligence measure the same underlying construct, others argue that they measure distinct but correlated constructs. Also, evidence on the differentiation of cognitive skills over time exists. Clarifying the relation between executive functions and math intelligence is, however, challenging, especially because preschoolers cannot fill in commonly used questionnaires that require them to read. As a consequence, researchers have to resort to behavioral, verbal, apparatus-, or computer-based assessments of executive functions. Objectives/Methodology: With this meta-analysis of 29 studies containing 268 effect sizes, we examined the link between executive functions and math intelligence for a total sample of 25,510 preschool children. Specifically, we synthesized the corresponding correlations and sought to clarify which executive function assessments were used for preschool children and how the assessment characteristics may moderate the correlation between executive functions and mathematical skills. Results: Utilizing three-level random-effects meta-analysis, we found a moderate correlation between executive functions and mathematical skills in preschool children, r = 0.35. The type of assessment (behavioral, verbal, apparatus-, or computer-based assessments) did not moderate this relation. Differentiating between the three executive functions revealed average correlations of r = 0.30 between math and inhibition, r = 0.38 between math and attention shifting, and r = 0.36 between math and updating. These analyses will be supplemented by further moderator analyses. Conclusions: Our findings support the significant link between executive functions and mathematical skills in preschoolers—yet, the average correlations do not suggest that both measures are identical. Results will be discussed against the background of deployed assessments and testing environments. [less ▲]

Dimensional comparisons in the formation of domain-specific achievement goals

in Motivation Science (2021), 7(3), 306318

Measuring Executive Functions and their Relations to Mathematical Skills in Preschool Children: A Meta-Analysis

Scientific Conference (2020, July)

Introduction: Executive functions (inhibition, attention shifting, working memory) are linked to mathematical skills in school students and adults. This link is particularly important because performance ... [more ▼]

Introduction: Executive functions (inhibition, attention shifting, working memory) are linked to mathematical skills in school students and adults. This link is particularly important because performance in school mathematics is predictive of various competencies later in life. While some researchers argue that tests of executive functions and mathematical skills measure the same underlying construct, others argue that they measure distinct but correlated constructs. Also, evidence on the differentiation of cognitive skills over time exists. Clarifying the relation between executive functions and mathematical skills is, however, challenging, especially because preschoolers cannot fill in commonly used questionnaires that require them to read. As a consequence, researchers have to resort to behavioral, verbal, apparatus-, or computer-based assessments of executive functions. Objectives/Methodology: With this meta-analysis of 26 studies containing 238 effect sizes, we examined the link between executive functions and early mathematical skills for a total sample of 24,256 preschool children. Specifically, we synthesized the corresponding correlations and sought to clarify which executive function assessments were used for preschool children and how the assessment characteristics may moderate the correlation between executive functions and mathematical skills. Results: Utilizing three-level random-effects meta-analysis, we found a moderate correlation between executive functions and mathematical skills in preschool children, r = 0.35. The type of assessment (behavioral, verbal, apparatus-, or computer-based assessments) did not moderate this relation. Differentiating between the three executive functions revealed average correlations of r = 0.31 between math and inhibition, r = 0.38 between math and attention shifting, and r = 0.36 between math and updating. These analyses will be supplemented by further moderator analyses. Conclusions: Our findings support the significant link between executive functions and mathematical skills in preschoolers—yet, the average correlations do not suggest that both measures are identical. Results will be discussed against the background of deployed assessments and testing environments. [less ▲]

Measures of Executive Functions and Mathematical Skills are Distinct Even at a Young Age: A Meta-Analysis with Preschool Children

Scientific Conference (2020)

Measures of executive functions (inhibition, attention shifting, working memory) are linked to measures of mathematical skills in school students and adults. However, the magnitude of this relation in ... [more ▼]

Measures of executive functions (inhibition, attention shifting, working memory) are linked to measures of mathematical skills in school students and adults. However, the magnitude of this relation in preschool children is unclear. Following the literature on the differentiation of cognitive skills over time, some researchers suggest that tests of executive functions and mathematical skills measure the same underlying construct, while others suggest that they measure correlated but distinct constructs. This dispute does not only tap the question of how the constructs can be understood but also the question of cost and test efficiency (i.e., assessments of single vs. multiple constructs). Clarifying the relation between measures of the two constructs can be especially challenging because preschoolers cannot fill in commonly used questionnaires that require them to read. Thus, researchers have to resort to behavioral, verbal, apparatus-, or computer-based assessments of executive functions. As a result, executive functions may vary in their relation to mathematical skills as a consequence of their measurement. We examined the link between executive functions and early mathematical skills measures, conducting a meta-analysis of 26 studies containing 238 effect sizes for a total sample of 24,256 preschool children. Specifically, we synthesized the corresponding correlations and aimed to clarify which executive function assessments were used for preschool children and how assessment characteristics may moderate the correlation between executive functions and mathematical skills. Three-level random-effects meta-analysis revealed a small to moderate average correlation between executive functions and mathematical skills measures of preschool children, r = 0.35. The type of assessment (behavioral, verbal, apparatus-, or computer-based assessments) did not moderate this relation. Investigating the three executive functions separately, we found average correlations of r = 0.31 between mathematical skills and inhibition, r = 0.38 between mathematical skills and attention shifting, and r = 0.36 between mathematical skills and updating. These analyses will be supplemented by further moderator and sensitivity analyses. These findings emphasize the significant link between executive functions and mathematical skills measures in preschoolers—hereby, supporting that the measures of both constructs are distinct. In addition, under-researched areas around the assessment of executive functions and mathematical abilities will be discussed. [less ▲]