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See detailValue-Added Scores Show Limited Stability over Time in Primary School
Emslander, Valentin UL; Levy, Jessica; Scherer, Ronny et al

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 ▲]

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See detailSchool tracking in Luxembourg: the longitudinal impact of student characteristics and school composition
Pit-Ten Cate, Ineke UL; Ottenbacher, Martha UL; Alieva, Aigul et al

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 ▲]

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See detailWhat Primary Schools Are Doing Right: Educational Value-Added in Luxembourg
Emslander, Valentin UL; Levy, Jessica UL; Fischbach, Antoine UL

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 ▲]

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See detailEarly Childhood Education and Care in Luxembourg - Is attendance influenced by immigration background and socioeconomic status?
Kaufmann, Lena Maria UL; Fischbach, Antoine UL; Ottenbacher, Martha UL et al

Poster (2022, November 10)

For decades, researchers have been raising awareness of the issue of educational inequalities in the multilingual Luxemburgish school system. Especially children from families with a migration background ... [more ▼]

For decades, researchers have been raising awareness of the issue of educational inequalities in the multilingual Luxemburgish school system. Especially children from families with a migration background or a lower socio-economic status show large deficits in their language and mathematics competences in comparison to their peers. The same applies to children who do not speak Luxemburgish or German as their first language (Hornung et al., 2021; Sonnleitner et al., 2021). One way to reduce such educational inequalities might be an early and extensive participation in early childhood education and care (ECEC). Indeed, participation in ECEC was found to be positively connected to language and cognitive development in other countries, especially for children from disadvantaged families (Bennett, 2012). However, these children attend ECEC less often (Vandenbroeck & Lazzari, 2014). There are indications that lower parental costs might go hand in hand with a greater attendance of ECEC in general (for a Luxembourgish study, see Bousselin, 2019) and in particular by disadvantaged families (Busse & Gathmann, 2020). The aim of this study is to spotlight the attendance of ECEC in Luxembourg during the implementation of the ECEC reform after 2017 which increased free ECEC hours for all families from 3 to 20 hours a week. We draw on a large dataset of about 35.000 children from the Épreuves Standardisées (ÉpStan, the Luxemburg school monitoring programme) from 2015 to 2021 and investigate which children attend any kind of regulated ECEC service (public, private or family daycare) in which intensity, taking socio-economic and cultural family factors into account. The findings might help to understand in which contexts ECEC attendance should be further encouraged. Implications for future policy decisions are discussed with the goal of further promoting equal educational opportunities for all children. [less ▲]

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See detailTeacher-Student-Relationships and Student Outcomes in Heterogeneous Educational Settings: A Systematic Review of Meta-Analyses
Emslander, Valentin UL; Holzberger, Doris; Fischbach, Antoine UL et al

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 ▲]

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See detailSchool Segregation in Primary and Secondary Education in Luxembourg: Track Placement and Academic Achievement
Pit-Ten Cate, Ineke UL; Hadjar, Andreas UL; Alieva, Aigul et al

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 ▲]

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See detailValidation and Psychometric Analysis of 32 cognitive item models spanning Grades 1 to 7 in the mathematical domain of numbers & operations
Michels, Michael Andreas UL; Hornung, Caroline UL; Gamo, Sylvie UL et al

Scientific Conference (2022, November)

Today’s educational field has a tremendous hunger for valid and psychometrically sound items to reliably track and model students’ learning processes. Educational large-scale assessments, formative ... [more ▼]

Today’s educational field has a tremendous hunger for valid and psychometrically sound items to reliably track and model students’ learning processes. Educational large-scale assessments, formative classroom assessment, and lately, digital learning platforms require a constant stream of high-quality, and unbiased items. However, traditional development of test items ties up a significant amount of time from subject matter experts, pedagogues and psychometricians and might not be suited anymore to nowadays demands. Salvation is sought in automatic item generation (AIG) which provides the possibility of generating multiple items within a short period of time based on the development of cognitively sound item templates by using algorithms (Gierl & Haladyna, 2013; Gierl et al., 2015). The present study psychometrically analyses 35 cognitive item models that were developed by a team of national subject matter experts and psychometricians and then used for algorithmically producing items for the mathematical domain of numbers & shapes for Grades 1, 3, 5, and 7 of the Luxembourgish school system. Each item model was administered in 6 experimentally varied versions to investigate the impact of a) the context the mathematical problem was presented in, and b) problem characteristics which cognitive psychology identified to influence the problem solving process. Based on samples from Grade 1 (n = 5963), Grade 3 (n = 5527), Grade 5 (n = 5291), and Grade 7 (n = 3018) collected within the annual Épreuves standardisées, this design allows for evaluating whether psychometric characteristics of produced items per model are a) stable, b) can be predicted by problem characteristics, and c) are unbiased towards subgroups of students (known to be disadvantaged in the Luxembourgish school system). After item calibration using the 1-PL model, each cognitive model was analyzed in-depth by descriptive comparisons of resulting IRT parameters, and the estimation of manipulated problem characteristics’ impact on item difficulty by using the linear logistic test model (LLTM, Fischer, 1972). Results are truly promising and show negligible effects of different problem contexts on item difficulty and reasonably stable effects of altered problem characteristics. Thus, the majority of developed cognitive models could be used to generate a huge number of items (> 10.000.000) for the domain of numbers & operations with known psychometric properties without the need for expensive field-trials. We end with discussing lessons learned from item difficulty prediction per model and highlighting differences between the Grades. References: Fischer, G. H. (1973). The linear logistic test model as an instrument in educational research. Acta Psychologica, 36, 359-374. Gierl, M. J., & Haladyna, T. M. (Eds.). (2013). Automatic item generation: Theory and practice. New York, NY: Routledge. Gierl, M. J., Lai, H., Hogan, J., & Matovinovic, D. (2015). A Method for Generating Educational Test Items That Are Aligned to the Common Core State Standards. Journal of Applied Testing Technology, 16(1), 1–18. [less ▲]

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See detailHow sensitive are the evaluations of a school's effectiveness to the selection of covariates in the applied value‑added model?
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

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 ▲]

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See detailAcademic Profile Development: An Investigation of Differentiation Processes Based on Students' Achievement and Grade Level
Breit, Moritz; Brunner, Martin; Fischbach, Antoine UL et al

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 ▲]

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See detailCreating positive learning experiences with technology: A field study on the effects of user experience for digital concept mapping
Rohles, Björn UL; Backes, Susanne UL; Fischbach, Antoine UL et al

in Heliyon (2022), 8(4),

Learning and assessment are increasingly mediated by digital technologies. Thus, learners’ experiences with these digital technologies are growing in importance, as they might affect learning and ... [more ▼]

Learning and assessment are increasingly mediated by digital technologies. Thus, learners’ experiences with these digital technologies are growing in importance, as they might affect learning and assessment. The present paper explores the impact of user experience on digital concept mapping. It builds on user experience theory to explain variance in the intention to use digital concept mapping tools and in concept map-based assessment scores. Furthermore, it identifies fulfillment of psychological needs as an important driver of positive experiences. In a field study in three schools and a university (N = 71), we tested two concept mapping prototypes on computers and tablets. We found that user experience is a significant factor explaining variance in intention to use. User experience also explained variance in three out of four concept mapping scores on tablets, potentially related to the lower pragmatic quality of the tablet prototypes. Fulfillment of psychological needs strongly affected perceptions of different qualities of user experience with digital concept mapping. These results indicate that user experience needs to be considered in digital concept mapping to provide a positive and successful environment for learning and assessment. Finally, we discuss implications for designers of digital learning and assessment tools. [less ▲]

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See detailThe Associations Between Teacher-Student-Relationships and Student Outcomes: A Systematic Review of Meta-Analyses (ReMA-TSR)
Emslander, Valentin UL; Holzberger, Doris; Fischbach, Antoine UL et al

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 ▲]

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See detailSystematic Identification of High "Value-Added" in Educational Contexts (SIVA)
Emslander, Valentin UL; Levy, Jessica; Fischbach, Antoine UL

Report (2022)

The aim of the SIVA project is to investigate differences between schools with stable high value-added (VA) scores to those with low or medium VA scores to learn about their effective pedagogical ... [more ▼]

The aim of the SIVA project is to investigate differences between schools with stable high value-added (VA) scores to those with low or medium VA scores to learn about their effective pedagogical strategies. We attempt to achieve this goal through classroom observations and questionnaires for students in grade 2, their parents, their teachers, as well as school presidents. More specifically, with the present study we want to learn from target schools with stable positive VA scores – a statistical method usually used to estimate schools' effectiveness. We will use VA modelling constructively to compare those schools identified as highly effective (i.e., with high VA scores) to schools with medium or low VA scores on variables such as pedagogical strategies, student background, and school climate. To this end, a mixed-methods design based on questionnaires, observations, and results from the Luxembourg School Monitoring Programme ÉpStan (LUCET, 2021) will be applied. The content of the investigation is based on a synthesis of models of school learning and quality, focusing on aspects such as school organization or classroom management (e.g., Hattie, 2008; Helmke et al., 2008; Klieme et al., 2001) and is extended by 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, and the diverse student population). With the aim to obtain a preferably broad picture, students, parents, teachers, school presidents and regional directors will be investigated. While parents, teachers, school presidents and regional directors can—as adults—fill out questionnaires individually, obtaining the opinion from children at such a young age can be challenging. The SIVA project tackles this issue by choosing item formats that are appealing and understandable for young children (see, e.g.,Lehnert, 2019), as well as by including classroom observations conducted by neutral educational experts (please, find both the questionnaires and observation sheets in the attachments). [less ▲]

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See detailSubjektives Wohlbefinden in der 5. und 9. Schulklasse: gibt es einen Zusammenhang mit dem Bildungsweg und der schulischen Leistung?
Pit-Ten Cate, Ineke UL; Esch, Pascale UL; Keller, Ulrich UL et al

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 ▲]

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See detailAre Value-Added Scores Stable Enough for High-Stakes Decisions?
Emslander, Valentin UL; Levy, Jessica UL; Scherer, Ronny et al

Scientific Conference (2022, March)

Theoretical Background: Can we quantify the effectiveness of a teacher or a school with a single number? Researchers in the field of value-added (VA) models may argue just that (e.g., Chetty et al., 2014 ... [more ▼]

Theoretical Background: Can we quantify the effectiveness of a teacher or a school with a single number? Researchers in the field of value-added (VA) models may argue just that (e.g., Chetty et al., 2014; Kane et al., 2013). VA models are widely used for accountability purposes in education and quantify the value a teacher or a school adds to their students’ achievement. For this purpose, these models predict achievement over time and attempt to control for factors that cannot be influenced by schools or teachers (i.e., sociodemographic & sociocultural background). Following this logic, what is left must be due to teacher or school differences (see, e.g., Braun, 2005). To utilize VA models for high-stakes decision-making (e.g., teachers’ tenure, the allocation of funding), these models would need to be highly stable over time. School-level stability over time, however, has hardly been researched at all and the resulting findings are mixed, with some studies indicating high stability of school VA scores over time (Ferrão, 2012; Thomas et al., 2007) and others reporting a lack of stability (e.g., Gorard et al., 2013; Perry, 2016). Furthermore, as there is no consensus on which variables to use as independent or dependent variables in VA models (Everson, 2017; Levy et al., 2019), the stability of VA could vary between different outcome measures (e.g., language or mathematics). If VA models lack stability over time and across outcome measures, their use as the primary information for high-stakes decision-making is in question, and the inferences drawn from them could be compromised. Questions: With these uncertainties in mind, we examine the stability of school VA model scores over time and investigate the differences between language and mathematics achievement as outcome variables. Additionally, we demonstrate the real-life implications of (in)stable VA scores for single schools and point out an alternative, more constructive use of school VA models in educational research. Method: To study the stability of VA scores on school level over time and across outcomes, we drew on a sample of 146 primary schools, using representative longitudinal data from the standardized achievement tests of the Luxembourg School Monitoring Programme (LUCET, 2021). These schools included a heterogeneous and multilingual sample of 7016 students. To determine the stability of VA scores in the subject of mathematics and in languages over time, we based our analysis on two longitudinal datasets (from 2015 to 2017 and from 2017 to 2019, respectively) and generated two VA scores per dataset, one for language and one for mathematics achievement. We further analyzed how many schools displayed stable VA scores in the respective outcomes over two years, and compared the rank correlations of VA scores between language and mathematics achievement as an outcome variable. Results and Their Significance: Only 34-38 % of the schools showed stable VA scores from grade 1 to 3 with moderate rank correlations of r = .37 with language and r = .34 with mathematics achievement. We therefore discourage using VA models as the only information for high-stakes educational decisions. Nonetheless, we argue that VA models could be employed to find genuinely effective teaching or school practices—especially in heterogeneous student populations, such as Luxembourg, in which educational disparities are an important topic already in primary school (Hoffmann et al., 2018). Consequently, we contrast the school climate and instructional quality, which might be a driver of the differences between schools with stable high vs. low VA scores. Literature Braun, H. (2005). Using student progress to evaluate teachers: A primer on value-added models. Educational Testing Service. Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers I: Evaluating bias in teacher value-added estimates. American Economic Review, 104(9), 2593–2632. https://doi.org/10.1257/aer.104.9.2593 Everson, K. C. (2017). Value-added modeling and educational accountability: Are we answering the real questions? Review of Educational Research, 87(1), 35–70. https://doi.org/10.3102/0034654316637199 Ferrão, M. E. (2012). On the stability of value added indicators. Quality & Quantity, 46(2), 627–637. https://doi.org/10.1007/s11135-010-9417-6 Gorard, S., Hordosy, R., & Siddiqui, N. (2013). How unstable are “school effects” assessed by a value-added technique? International Education Studies, 6(1), 1–9. https://doi.org/10.5539/ies.v6n1p1 Kane, T. J., McCaffrey, D. F., Miller, T., & Staiger, D. O. (2013). Have We Identified Effective Teachers? Validating Measures of Effective Teaching Using Random Assignment. Research Paper. MET Project. Bill & Melinda Gates Foundation. https://files.eric.ed.gov/fulltext/ED540959.pdf Levy, J., Brunner, M., Keller, U., & Fischbach, A. (2019). Methodological issues in value-added modeling: An international review from 26 countries. Educational Assessment, Evaluation and Accountability, 31(3), 257–287. https://doi.org/10.1007/s11092-019-09303-w LUCET. (2021). Épreuves Standardisées (ÉpStan). https://epstan.lu Perry, T. (2016). English value-added measures: Examining the limitations of school performance measurement. British Educational Research Journal, 42(6), 1056–1080. https://doi.org/10.1002/berj.3247 Thomas, S., Peng, W. J., & Gray, J. (2007). Modelling patterns of improvement over time: Value added trends in English secondary school performance across ten cohorts. Oxford Review of Education, 33(3), 261–295. https://doi.org/10.1080/03054980701366116 [less ▲]

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See detailNeed for Cognition and its relation to academic achievement in different learning environments
Colling, Joanne UL; Wollschläger, Rachel UL; Keller, Ulrich UL et al

in Learning and Individual Differences (2022), 93

The present study investigates how Need for Cognition (NFC), an individual's tendency to engage in and enjoy thinking, relates to academic achievement in 9th grade students (N = 3.355) attending different ... [more ▼]

The present study investigates how Need for Cognition (NFC), an individual's tendency to engage in and enjoy thinking, relates to academic achievement in 9th grade students (N = 3.355) attending different school tracks to understand whether school track moderates this relation when controlling for student background variables. Using structural regression analyses, our findings revealed small and significant positive relations between NFC and academic achievement in German, French and Math. Relations were strongest in the highest and weakest in the lowest track. No significant track difference between the highest and the intermediary track could be identified; significant differences of small effect size between the intermediary and the lowest track were found in favor of the intermediary track in the relation between NFC and academic achievement in German and Math. These findings underpin the importance of NFC in academic settings, while highlighting that the relation between NFC and achievement varies with the characteristics of different learning environments. [less ▲]

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See detailAcademic Achievement and Subjective Well-being: A Representative Cross-sectional Study
Wollschläger, Rachel UL; Esch, Pascale UL; Keller, Ulrich UL et al

in Heinen, Andreas; Samuel, Robin; Vögele, Claus (Eds.) et al Wohlbefinden und Gesundheit im Jugendalter (2022)

Formal education is a very important, time-intensive, and highly consequential aspect of adolescents’ everyday life. School as well as education can influence adolescents’ well-being in both the short ... [more ▼]

Formal education is a very important, time-intensive, and highly consequential aspect of adolescents’ everyday life. School as well as education can influence adolescents’ well-being in both the short- and long-term. In return, adolescents’ well-being in- and outside school may affect their educational achievement. The objective of the present study is to investigate how self-reported dimensions of adolescents’ subjective well-being (SWB) in an educational context (i.e., academic self-concept, school anxiety, social and emotional inclusion) relate to educational pathways (regular vs. irregular school transitions; attendance of more vs. less prestigious secondary school tracks) and standardized assessment scores in key academic areas (i.e., mathematics and languages). Drawing on representative data emerging from the Luxembourg School Monitoring Programme “Épreuves Standardisées” (academic year 2018/2019), the relationship between academic achievement and students’ self-reported well-being was analysed cross-sectionally for the entire student cohorts of 5th and 9th graders. Result indicated that grades and educational pathways affect SWB, whereby in general lower ratings of SWB were observed in older students, students that experienced grade retention and students in lower secondary school tracks. Furthermore, ratings of SWB explained a significant proportion of variance in academic achievement in bot Grade 5 and Grade 9. These findings highlight the importance of student´ SWB in education. SWB may not only affect academic achievement, but also impact motivation and engagement and hence long-term educational success. Implications of the findings for research and educational debate are discussed. [less ▲]

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See detailDeveloping and Validating a Short-Form Questionnaire for the Assessment of Seven Facets of Conscientiousness in Large-Scale Assessments
Franzen, Patrick UL; Arens, A. Katrin; Greiff, Samuel UL et al

in Journal of Personality Assessment (2022), 104(6), 759-773

Conscientiousness is the most important personality predictor of academic achievement. It consists of several lower order facets with differential relations to academic achievement. There is currently no ... [more ▼]

Conscientiousness is the most important personality predictor of academic achievement. It consists of several lower order facets with differential relations to academic achievement. There is currently no short instrument assessing facets of conscientiousness in the educational context. Therefore, in the present multi-study report, we develop and validate a short-form questionnaire for the assessment of seven Conscientiousness facets, namely Industriousness, Perfectionism, Tidiness, Procrastination Refrainment, Control, Caution, and Task Planning. To this end, we examined multiple representative samples totaling N = 14,604 Grade 9 and 10 students from Luxembourg. The questionnaire was developed by adapting and shortening an existing scale using an exhaustive search algorithm. The algorithm was specified to select the best item combination based on model fit, reliability, and measurement invariance across the German and French language versions. The resulting instrument showed the expected factorial structure. The relations of the facets with personality constructs and academic achievement were in line with theoretical assumptions. Reliability was acceptable for all facets. Measurement invariance across language versions, gender, immigration status and cohort was established. We conclude that the presented questionnaire provides a short measurement of seven facets of Conscientiousness with valid and reliable scores. [less ▲]

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