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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

E-print/Working paper (2022)

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 language 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 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 detailThe generalized internal/external frame of reference model with academic self-concepts, interests, and anxieties in students from different language backgrounds
van der Westhuizen, Lindie UL; Arens, A. Katrin; Greiff, Samuel UL et al

in Contemporary Educational Psychology (2022)

Student motivation and affect play an important role in successful language learning. To investigate the formation of language learning motivation and affect, this study extended the generalized internal ... [more ▼]

Student motivation and affect play an important role in successful language learning. To investigate the formation of language learning motivation and affect, this study extended the generalized internal/external frame of reference (GI/E) model framework to multiple languages (German and French, along with math) and multiple motivational-affective outcomes (academic self-concept, interest, and anxiety). We examined whether social and dimensional comparisons play similar roles in the formation of students’ self-concepts, interests, and anxieties concerning different languages and whether dimensional comparisons result in contrast or assimilation effects. Moreover, we tested the generalizability of the GI/E model assumptions across students with different language backgrounds. Using a data set comprising virtually all ninth-grade students (N=6275; 48.0% female) from Luxembourg’s multilingual educational system, our findings indicated (1) clear contrast effects in the formation of self-concept and interest in math, German, and French, and (2) a combination of contrast, assimilation, and no effects in the formation of anxiety in math, German, and French. Using a subsample of 5837 students with valid language information (48.0% female), invariance tests demonstrated that the GI/E achievement–outcome relations operated equivalently across students from different home language backgrounds. [less ▲]

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See detailCompétences en littératie numérique et pensée computationnelle des élèves de huitième année – Principales conclusions d’ICILS 2018
Boualam, Rachid UL; Lomos, Catalina; Fischbach, Antoine UL

in LUCET; SCRIPT (Eds.) Rapport national sur l’éducation au Luxembourg 2021 (2021)

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