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MUSSACK Dominic

Main Referenced Co-authors
CARDOSO-LEITE, Pedro  (8)
Brunner, Martin (4)
FISCHBACH, Antoine  (4)
KELLER, Ulrich  (4)
LEVY, Jessica  (4)
Main Referenced Keywords
longitudinal data (2); machine learning (2); school effectiveness (2); large-scale assessment (1); model comparison (1);
Main Referenced Disciplines
Education & instruction (6)
Neurosciences & behavior (1)
Social & behavioral sciences, psychology: Multidisciplinary, general & others (1)

Publications (total 8)

The most downloaded
114 downloads
Mussack, D., Flemming, R., Schrater, P., & Cardoso-Leite, P. (2019). Towards discovering problem similarity through deep learning: combining problem features and user behavior. In Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019) (pp. 618). https://hdl.handle.net/10993/46719

The most cited

8 citations (Scopus®)

Levy, J., Mussack, D., Brunner, M., Keller, U., Cardoso-Leite, P., & Fischbach, A. (2020). Contrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data. Frontiers in Psychology, 11, 2190. doi:10.3389/fpsyg.2020.02190 https://hdl.handle.net/10993/44313

Levy, J., Mussack, D., Brunner, M., Keller, U., Cardoso-Leite, P., & Fischbach, A. (11 November 2020). Tackling educational inequalities using school effectiveness measures [Paper presentation]. (Semi)virtual LuxERA Conference 2020, virtual and Esch-sur-Alzette, Luxembourg.

Levy, J., Mussack, D., Brunner, M., Keller, U., Cardoso-Leite, P., & Fischbach, A. (July 2020). Can machine learning methods lead to more precise measures of school effectiveness? An application of various machine learning approaches in the estimation of school value-added scores [Paper presentation]. 12th Conference of the International Test Commission, Belval, Luxembourg.

Levy, J., Mussack, D., Brunner, M., Keller, U., Cardoso-Leite, P., & Fischbach, A. (March 2020). Contrasting classical and machine learning approaches in the estimation of value-added scores in large-scale educational data [Paper presentation]. 8. Tagung der Gesellschaft für Empirische Bildungsforschung (GEBF2020), Potsdam, Germany. doi:10.3389/fpsyg.2020.02190

Levy, J., Mussack, D., Brunner, M., Keller, U., Cardoso-Leite, P., & Fischbach, A. (2020). Contrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data. Frontiers in Psychology, 11, 2190. doi:10.3389/fpsyg.2020.02190
Peer Reviewed verified by ORBi

Cardoso-Leite, P., Buchard, A., Tissieres, I., Mussack, D., & Bavelier, D. (2020). Media use, attention, mental health and academic performance among 8 to 12 year old children. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/46496.

Ansarinia, M., Mussack, D., Schrater, P., & Cardoso-Leite, P. (15 September 2019). A Formal Framework for Structured N-Back Stimuli Sequences [Paper presentation]. Conference on Cognitive Computational Neuroscience, Berlin, Germany. doi:10.32470/CCN.2019.1273-0

Flemming, R., Schmück, E., Mussack, D., Cardoso-Leite, P., & Schrater, P. (2019). A generalizable performance evaluation model of driving games via risk-weighted trajectories. In Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019) (pp. 551).
Peer reviewed

Mussack, D., Flemming, R., Schrater, P., & Cardoso-Leite, P. (2019). Towards discovering problem similarity through deep learning: combining problem features and user behavior. In Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019) (pp. 618).
Peer reviewed

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