References of "Brunner, Martin"
     in
Bookmark and Share    
Full Text
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 (in press)

Detailed reference viewed: 97 (18 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: 37 (3 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: 45 (4 UL)
Peer Reviewed
See detailValue-added modeling in primary school: What covariates to include?
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Scientific Conference (2019, August)

Detailed reference viewed: 80 (7 UL)
Full Text
Peer Reviewed
See detailMethodological Issues in Value-Added Modeling: An International Review from 26 Countries
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

in Educational Assessment, Evaluation and Accountability (2019), 31(3), 257-287

Value-added (VA) modeling can be used to quantify teacher and school effectiveness by estimating the effect of pedagogical actions on students’ achievement. It is gaining increasing importance in ... [more ▼]

Value-added (VA) modeling can be used to quantify teacher and school effectiveness by estimating the effect of pedagogical actions on students’ achievement. It is gaining increasing importance in educational evaluation, teacher accountability, and high-stakes decisions. We analyzed 370 empirical studies on VA modeling, focusing on modeling and methodological issues to identify key factors for improvement. The studies stemmed from 26 countries (68% from the USA). Most studies applied linear regression or multilevel models. Most studies (i.e., 85%) included prior achievement as a covariate, but only 2% included noncognitive predictors of achievement (e.g., personality or affective student variables). Fifty-five percent of the studies did not apply statistical adjustments (e.g., shrinkage) to increase precision in effectiveness estimates, and 88% included no model diagnostics. We conclude that research on VA modeling can be significantly enhanced regarding the inclusion of covariates, model adjustment and diagnostics, and the clarity and transparency of reporting. [less ▲]

Detailed reference viewed: 81 (17 UL)
Peer Reviewed
See detailThe use of value-added models for the identification of schools that perform “against the odds”
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Poster (2019, July)

Value-added (VA) modeling aims to quantify the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds. VA modeling is primarily used for accountability and high ... [more ▼]

Value-added (VA) modeling aims to quantify the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds. VA modeling is primarily used for accountability and high-stakes decisions. To date, there seems to be no consensus concerning the calculation of VA models. Our study aims to systematically analyze and compare different school VA models by using longitudinal large-scale data emerging from the Luxembourg School Monitoring Programme. Regarding the model covariates, first findings indicate the importance of language (i.e., language(s) spoken at home and prior language achievement) in VA models with either language or math achievement as a dependent variable, with the highest amount of explained variance in VA models for language. Concerning the congruence of different VA approaches, we found high correlations between school VA scores from the different models, but also high ranges between VA scores for single schools. We conclude that VA models should be used with caution and with awareness of the differences that may arise from methodological choices. Finally, we discuss the idea that VA models could be used for the identification of schools that perform “against the odds”, especially for those schools that have positive VA scores over several years. [less ▲]

Detailed reference viewed: 51 (4 UL)
Peer Reviewed
See detailExploration of Different School Value-Added Models in a Highly Heterogeneous Educational Context
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Scientific Conference (2019, April)

Detailed reference viewed: 78 (13 UL)
Peer Reviewed
See detailModéliser la « valeur ajoutée » en éducation primaire et secondaire : 674 publications en revue
Levy, Jessica UL; Gamo, Sylvie UL; Keller, Ulrich UL et al

Scientific Conference (2018, January)

L’approche statistique du type de « valeur ajoutée » (« value added ») a comme but de quantifier l’effet des acteurs pédagogiques sur la performance des élèves, indépendamment de leur origine (p. ex ... [more ▼]

L’approche statistique du type de « valeur ajoutée » (« value added ») a comme but de quantifier l’effet des acteurs pédagogiques sur la performance des élèves, indépendamment de leur origine (p. ex. Braun, 2005), c’est-à-dire de déterminer la valeur dans la performance de l’élève du fait qu’il étudie avec tel professeur ou /et qu’il soit dans telle école. Ces indices de valeur ajoutée une fois déterminés sont souvent utilisés pour prendre des décisions de reddition de compte (« accountability » ; p.ex. Sanders, 2000) L’idée est de faire une évaluation standardisée de la qualité des enseignants ou des écoles à travers l’évolution des résultats des élèves. Même si les valeurs ajoutées sont devenues plus populaires durant ces dernières années, il n’y a pas de consensus concernant la méthode pour les calculer, ni sur l’intégration de variables explicatives (p. ex. Newton et al., 2010). Le but de notre étude est de faire une revue de littérature concernant les valeurs ajoutées en éducation primaire et secondaire. Pour ce faire, nous avons utilisé les bases de données ERIC, Scopus, PsycINFO et Psyndex et nous avons analysé et classifié rigoureusement 674 études de 32 pays différents. La moitié des études recensées concerne les valeurs ajoutées au niveau des enseignants et les autres concernent celles au niveau des écoles ou directeurs. 370 études ont utilisé des données empiriques pour calculer des indices de valeur ajoutée. Dans un certain nombre d’études, les variables utilisées sont précisées, mais dans approximativement 15% des publications, le modèle statistique utilisé n’est pas spécifié. La plupart des études ont utilisé la performance des années précédentes des élèves comme prédicteur ; en revanche, des variables cognitives ou motivationnelles des élèves n’ont presque jamais été prises en considération. Cette revue de littérature permet de souligner, en vue des enjeux politiques importants des valeurs ajoutées, qu’il est nécessaire d’avoir plus de transparence, rigueur et consensus, surtout sur le plan méthodologique. [less ▲]

Detailed reference viewed: 137 (24 UL)
Full Text
Peer Reviewed
See detailBetween‐school variation in students’ achievement, motivation, affect, and learning strategies: Results from 81 countries for planning group‐randomized trials in education
Brunner, Martin; Keller, Ulrich UL; Wenger, Marina et al

in Journal of Research on Educational Effectiveness (2018), 11(3), 452-478

Detailed reference viewed: 128 (34 UL)
Peer Reviewed
See detailValue-Added Modelling in Primary and Secondary School: An Integrative Review of 674 Publications
Levy, Jessica UL; Keller, Ulrich UL; Brunner, Martin et al

Scientific Conference (2017, December)

Value-added (VA) modelling aims to quantify the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds (e.g., [1]); in other words, VA strives to model the added ... [more ▼]

Value-added (VA) modelling aims to quantify the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds (e.g., [1]); in other words, VA strives to model the added value of teaching. VA is typically used for teacher and/or school accountability (e.g., [2]). Although, VA models have gained popularity in recent years—a substantial increase of publications is to be observed over the last decade—, there is no consensus on how to calculate VA, nor is there a consensus whether and which covariates should be included in the statistical models (e.g., [3]). The aim of the present study is to conduct a to date non-existent integrative review on VA modelling in primary and secondary education. Starting with an exhaustive literature research in the ERIC, Scopus, PsycINFO, and Psyndex databases, we reviewed and thoroughly classified 674 VA publications from 32 different countries. Half of the studies investigated VA models at teacher level; the remaining looked at school or principal level. 370 studies used empirical data to calculate VA models. Most of these studies explained their covariates, but approximately 15% did not specify the model. Most studies used prior achievement as a covariate, but cognitive and/or motivational student data were almost never taken into consideration. Moreover, most of the studies did not adjust for methodological issues such as missing data or measurement error. To conclude, given the high relevance of VA—it is primarily used for high-stakes decisions— more transparency, rigor and consensus are needed, especially concerning methodological details. References [1] Braun, H. I. (2005). Using student progress to evaluate teachers: A primer on value-added models. Princeton, NJ: Educational Testing Service. [2] Sanders, W. L. (2000). Value-added assessment from student achievement data: Opportunities and hurdles. Journal of Personnel Evaluation in Education, 14(4), 329–339. [3] Newton, X., Darling-Hammond, L., Haertel, E., & Thomas, E. (2010). Value-added modeling of teacher effectiveness: An exploration of stability across models and contexts. Education Policy Analysis Archives, 18(23). [less ▲]

Detailed reference viewed: 105 (24 UL)
Full Text
Peer Reviewed
See detailAssessing Complex Problem Solving in the Classroom: Meeting Challenges and Opportunities
Sonnleitner, Philipp UL; Keller, Ulrich UL; Martin, Romain UL et al

in Csapó, Beno; Funke, Joachim (Eds.) The Nature of Problem Solving. Using research to inspire 21st century learning (2017)

At the time when complex problem solving was established as a key aspect of today’s educational curricula and a central competence of international assessment frameworks like PISA, it became evident that ... [more ▼]

At the time when complex problem solving was established as a key aspect of today’s educational curricula and a central competence of international assessment frameworks like PISA, it became evident that the educational context places special demands on assessment instruments used for this purpose. In this chapter, we show how these challenges can successfully be addressed by reviewing recent advancements in the field of complex problem solving. We use the example of the Genetics Lab, a newly developed and psychometrically sound microworld which emphasizes usability and acceptance amongst students, to discuss challenges and opportunities of assessing complex problem solving in the classroom. [less ▲]

Detailed reference viewed: 260 (50 UL)
Full Text
Peer Reviewed
See detailExtension procedures for confirmatory factor analysis.
Nagy, Gabriel; Brunner, Martin; Lüdtke, Oliver et al

in The Journal of Experimental Education (2017), 85(4), 574-596

Detailed reference viewed: 72 (3 UL)
Full Text
Peer Reviewed
See detailSolving arithmetic problems in first and second language: Does the language context matter?
Van Rinsveld, Amandine UL; Schiltz, Christine UL; Brunner, Martin et al

in Learning and instruction (2016)

Learning mathematics in a second language is a challenge for many learners. The purpose of the study was to provide new insights into the role of the language context in mathematic learning and more ... [more ▼]

Learning mathematics in a second language is a challenge for many learners. The purpose of the study was to provide new insights into the role of the language context in mathematic learning and more particularly arithmetic problem solving. We investigated this question in a GermaneFrench bilingual educational setting in Luxembourg. Participants with increasing bilingual proficiency levels were invited to solve additions in both their first and second instruction languages: German and French. Arithmetic problems were presented in two different conditions: preceded by a semantic judgment or without additional language context. In the French session we observed that additions were systematically performed faster in the condition with an additional language context. In contrast no effect of the context was observed in the German session. In conclusion, providing a language context enhanced arithmetic performances in bilinguals' second instruction language. This finding entails implications for designing optimal mathematic learning environments in multilingual educational settings. [less ▲]

Detailed reference viewed: 223 (24 UL)
Full Text
Peer Reviewed
See detailShort-term and medium-term effects of grade retention in secondary school on academic achievement and psychosocial outcome variables
Klapproth, Florian; Schaltz, Paule UL; Brunner, Martin et al

in Learning & Individual Differences (2016), 50

Detailed reference viewed: 183 (26 UL)
Peer Reviewed
See detailComplex Problem Solving Provides a Fairer Picture of Multilingual Students’ Cognitive Potential
Sonnleitner, Philipp UL; Brunner, Martin; Keller, Ulrich UL et al

Scientific Conference (2015, August)

Detailed reference viewed: 32 (3 UL)