Befunde aus dem nationalen Bildungsmonitoring ÉpStan vor dem Hintergrund der COVID-19 Pandemie (Supplement)

in LUCET; SCRIPT (Eds.) Nationaler Bildungsbericht Luxemburg 2021 (2021)

Neue längsschnittliche Befunde aus dem nationalen Bildungsmonitoring ÉpStan in der 3. und 9. Klasse: Schlechtere Ergebnisse und wirkungslose Klassenwiederholungen

in LUCET; SCRIPT (Eds.) Nationaler Bildungsbericht Luxemburg 2021 (2021)

Résultats du monitoring scolaire national ÉpStan dans le contexte de la pandémie de COVID-19

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

Need for Cognition and its relation to Self-Control and Action Orientation in Secondary School Students in Luxembourg

Scientific Conference (2021, November)

Long-Term Effects of Retention in Grade 8 in Luxembourg

Scientific Conference (2021, September 10)

Meta-analyses (Hattie, 2009; Jimerson, 2001) have suggested that grade retention rarely has positive effects and more often negative effects on students’ performance and psycho-emotional well-being. The ... [more ▼]

Meta-analyses (Hattie, 2009; Jimerson, 2001) have suggested that grade retention rarely has positive effects and more often negative effects on students’ performance and psycho-emotional well-being. The occurrence of negative effects may be due to the absence of new learning experiences (Pagani, Tremblay, Vitaro, Boulerice & McDuff, 2001). However, in the short term, positive effects of grade retention are quite likely to occur (Klapproth, Schaltz, Brunner, Keller, Fischbach, Ugen & Martin, 2016). In Luxembourg, more than half of the students repeat at least one grade within their entire school career (Klapproth & Schaltz, 2015). Since grade retention is applied so frequently, the aim of the current study was to examine long-term effects of grade retention, and particularly retention in grade 8. The data used in this study were drawn from 2,835 Luxembourgish students who completed primary education (grade 6) and began secondary education (grade 7) in the 2008-2009 school year. We conducted propensity-score matching to select retained and promoted students with comparable characteristics. We used the “same age-cohort, same grade, different times of measurement” approach for comparisons (Klapproth et al., 2016). The dependent variables were the school marks in the main subjects (German, French, and mathematics) in grades 10, 11, and 12, which can vary between 0 and 60 (with higher values indicating better achievement, and values below 30 indicating insufficient achievement). Our results showed that grade 8 repeaters obtain significantly lower school marks in grades 10 to 12 as compared to matched non-repeaters, with most negative effects appearing for mathematics and French (as opposed to German) and with negative effects strengthening significantly with time. These results seem to confirm results of previous meta-analyses on longer-term effects of grade retention, seemingly suggesting that grade retention is no effective means to tackle low student achievement. [less ▲]

Tackling educational inequalities using school effectiveness measures

Scientific Conference (2020, November 11)

Is there a bilingual advantage in mathematics?

Scientific Conference (2020, November)

Self-concept, interest, and achievement within and across math and verbal domains in first- and third-graders

Scientific Conference (2020, April)

The generalized internal/external frame-of-reference (G)I/E model explains the formation of domain-specific motivational-affective constructs through social and dimensional comparisons. We examined the ... [more ▼]

The generalized internal/external frame-of-reference (G)I/E model explains the formation of domain-specific motivational-affective constructs through social and dimensional comparisons. We examined the associations between verbal and math achievement and corresponding domain-specific academic self-concepts (ASCs) and interests for first-graders and third-graders (N=21,192). Positive achievement-self-concept and achievement-interest relations were found within matching-domains in both grades, while negative cross-domains achievement-self-concept and achievement-interest relations were only found for third-graders. These findings suggest that while the formation of domain-specific ASCs and interests seem to rely on social and dimensional comparisons for third-graders, only social comparisons seem to be in operation for first-graders. Gender and cohort invariance was established in both grade levels. Findings are discussed within the framework of ASC differentiation and dimensional comparison theory. [less ▲]

Contrasting classical and machine learning approaches in the estimation of value-added scores in large-scale educational data.

Scientific Conference (2020, March)

Contrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data

in Frontiers in Psychology (2020), 11

There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical ... [more ▼]

There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these “imported” techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed. [less ▲]