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Peer Reviewed
See detailA propensity score matching approach on predicting academic success of primary school students
Wollschläger, Rachel UL; Hornung, Caroline UL; Sonnleitner, Philipp UL et al

Scientific Conference (2020, July)

School career and academic achievement are known to greatly affect an individual’s path through life (e.g., Trapmann, Hell, Weigand & Schuler, 2007; Jimerson, 2001). In Luxembourg, recent findings ... [more ▼]

School career and academic achievement are known to greatly affect an individual’s path through life (e.g., Trapmann, Hell, Weigand & Schuler, 2007; Jimerson, 2001). In Luxembourg, recent findings indicate that at school entrance (i.e., the beginning of Grade 1) the majority of the students achieve or even surpass the required minimum level of core competencies such as mathematics and early literacy (Hoffmann, Hornung, Gamo, Esch, Keller, & Fischbach, 2018). However, in Grade 3 (i.e., after the first two years of elementary school) many students do no longer achieve the required minimum level of competencies in math and literacy (ibid.). Especially students with another language background than (any of) the official languages in Luxembourg (Luxembourgish, German, and French) and those socio-economically disadvantaged were found to be more likely not to obtain the competency level (ibid.). The current study aims to investigate which specific factors may facilitate (or hinder) learning progression by using longitudinal data of the Luxembourg School Monitoring Programme Épreuves Standardisées from Grade 1 (2014, 2015) to Grade 3 (2016, 2017, 2018). More specifically, students with irregular pathways (i.e., those who experienced grade retention) will be identified as treatment group and compared to a stratified control group of students following regular pathways. For each student of the treatment group, one or more students from the control group will be matched through propensity score matching, a matching procedure based on logistic regression, according to different pre-sets of variables. In a second step, the two groups will be compared in regards to competency levels as well as to socio-emotional context variables such as family background, student-teacher interaction, and school satisfaction aiming at identifying characteristics potentially facilitating (or hindering) a student’s school career. [less ▲]

Detailed reference viewed: 37 (17 UL)
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See detailDoes Conscientiousness Matter for Academic Success? Considering Different Facets of Conscientiousness and Different Educational Outcomes
Franzen, Patrick UL; van der Westhuizen, Lindie UL; Arens, A. Katrin et al

Poster (2020, April)

Conscientiousness is the strongest BIG-5 predictor of academic success. Both conscientiousness and academic success are broad concepts, consisting of multiple lower level facets. Conscientiousness facets ... [more ▼]

Conscientiousness is the strongest BIG-5 predictor of academic success. Both conscientiousness and academic success are broad concepts, consisting of multiple lower level facets. Conscientiousness facets might display differential relations to different indicators of academic success. To investigate these relations, conscientiousness facets need to be measured in an economic and valid way. We conducted two studies, validating a short conscientiousness scale measuring seven facets of conscientiousness (Industriousness, Task Planning, Perfectionism, Procrastination Refrainment, Tidiness, Control, Cautiousness), and testing the relations of these facets with GPA, test scores, school satisfaction, and engagement. The results supported the validity of the scale. Industriousness, Perfectionism, and Cautiousness revealed the highest relations to academic outcomes. GPA and test scores showed differential associations with the different conscientiousness facets. [less ▲]

Detailed reference viewed: 137 (6 UL)
Peer Reviewed
See detailSelf-concept, interest, and achievement within and across math and verbal domains in first- and third-graders
van der Westhuizen, Lindie UL; Arens, A. Katrin; Keller, Ulrich UL et al

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 ▲]

Detailed reference viewed: 45 (2 UL)
Peer Reviewed
See detailLangzeiteffekte von Klassenwiederholungen in der Sekundarstufe
Klapproth, Florian; Keller, Ulrich UL; Fischbach, Antoine UL

Scientific Conference (2020, March)

Detailed reference viewed: 41 (5 UL)
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Peer Reviewed
See detailContrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data
Levy, Jessica UL; Mussack, Dominic UL; Brunner, Martin et al

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 ▲]

Detailed reference viewed: 89 (11 UL)
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Peer Reviewed
See detailCircadian preference as a typology: Latent-class analysis of adolescents' morningness/eveningness, relation with sleep behavior, and with academic outcomes
Preckel, Franzis; Fischbach, Antoine UL; Scherrer, Vsevolod et al

in Learning and Individual Differences (2020), 78

Detailed reference viewed: 199 (30 UL)
Peer Reviewed
See detailMath and Reading Difficulties in a Multilingual Educational Setting
Martini, Sophie Frédérique UL; Fischbach, Antoine UL; Ugen, Sonja UL

Scientific Conference (2019, November 06)

Detailed reference viewed: 64 (4 UL)
Peer Reviewed
See detailSimilarities and differences of value-added scores from models with different covariates: A cluster analysis
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Scientific Conference (2019, November 06)

Detailed reference viewed: 82 (6 UL)
See detailDimensional and Social Comparison Effects on Domain-Specific Academic Self-Concepts and Interests with First- and Third-Grade Students
van der Westhuizen, Lindie UL; Arens, Katrin; Keller, Ulrich UL et al

Scientific Conference (2019, November 06)

Academic self-concepts (ASCs) are self-perceptions of one’s own academic abilities. The internal/external frame of reference (I/E) model (Marsh, 1986) explains the formation of domain-specific ASCs ... [more ▼]

Academic self-concepts (ASCs) are self-perceptions of one’s own academic abilities. The internal/external frame of reference (I/E) model (Marsh, 1986) explains the formation of domain-specific ASCs through a combination of social (i.e. comparing one’s achievement in one domain with the achievement of others in the same domain) and dimensional (i.e. comparing one’s achievement in one domain with one’s achievement in another domain) comparisons. This results into positive achievement-self-concept relations within the math and verbal domains, but into negative achievement-self-concept relations across these domains. The generalized internal/external frame of reference (GI/E) model (Möller, Müller-Kalthoff, Helm, Nagy, & Marsh, 2015) extends the I/E model to the formation of other domain-specific academic self-beliefs such as interest. Research on the validity of the (G)I/E model for elementary school children is limited, especially for first-graders. This study examined the associations between verbal and math achievement and corresponding domain-specific self-concepts and interests for first-graders and third-graders. Two fully representative Luxembourgish first-grader cohorts and two fully representative third-graders cohorts (N=21,192) were used. The analyses were based on structural equation modeling. The findings fully supported the (G)I/E model for third-graders: Achievement was positively related to self-concept and interest within matching domains. Negative relations were found between achievement and self-concept and between achievement and interest across domains. For first-graders, achievement was positively related to self-concept and interest within matching domains. However, the majority of cross-domain relations were non-significant, except for the negative path between math achievement and verbal interest. Hence, 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 for both grade levels. The findings are discussed within the framework of ASC differentiation and dimensional comparison theory applied to elementary school students. [less ▲]

Detailed reference viewed: 133 (7 UL)
Peer Reviewed
See detailNeed for Cognition across school tracks: The importance of learning environments
Colling, Joanne UL; Wollschläger, Rachel UL; Keller, Ulrich UL et al

Scientific Conference (2019, November 06)

Detailed reference viewed: 103 (9 UL)
See detailMonitoring du système scolaire – Le modèle luxembourgeois (invited talk)
Fischbach, Antoine UL

Scientific Conference (2019, October 17)

Detailed reference viewed: 75 (8 UL)
Peer Reviewed
See detailValue-added models: To what extent do estimates of school effectiveness depend on the selection of covariates?
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Scientific Conference (2019, September)

Detailed reference viewed: 90 (6 UL)