2023

Günther, L. (2023, im Druck): Der Start in das Mathematikstudium als krisenhafter Bildungsprozess. In: T. Hamann, M. Helmerich, D. Kollosche, K. Lengnink & S. Pohlkamp (Hrsg.) (2023). Mathematische Bildung neu denken: Andreas Vohns erinnern und weiterdenken (S. x–y). WTM-Verlag.

Le Quy, T., Friege, G., & Ntoutsi, E. (2023). A review of clustering models in educational data science towards fairness-aware learning. https://doi.org/10.1007/978-981-99-0026-8_2

Le Quy, T., Nguyen, T. H., Friege, G., & Ntoutsi, E. (2023). Evaluation of Group Fairness Measures in Student Performance Prediction Problems. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases (Vol. 1752, pp. 119–136). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-23618-1_8

Stanja, J., Gritz, W., Krugel, J., Hoppe, A., & Dannemann, S. (2023). Formative assessment strategies for students’ conceptions—The potential of learning analytics. British Journal of Educational Technology, 54(1), 58–75. https://doi.org/10.1111/bjet.13288

2022

Günther, L., Marten, N., & Berendes, K. (2022). Informal learning situations in the context of mathematics studies. Development of an analysis framework. International Network for Didactic Research in University Mathematics.

Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., & Ntoutsi, E. (2022). A survey on datasets for fairness‐aware machine learning. WIREs Data Mining and Knowledge Discovery, 12(3). https://doi.org/10.1002/widm.1452

Sebastian, R., Ewerth, R., & Hoppe, A. (2022). Grade Level Filtering for Learning Object Search using Entity Linking. Third International Workshop on Investigating Learning During Web Search (IWILDS’22) co-located with SIGIR ’22.

2021

Le Quy, T., Roy, A., Friege, G., & Ntoutsi, E. (2021). Fair-Capacitated Clustering. Educational Data Mining. https://educationaldatamining.org/EDM2021/EDM2021Proceedings.pdf

Navarrete, E., Hoppe, A., & Ewerth, R. (2021). A Review on Recent Advances in Video-based Learning Research: Video Features, Interaction, Tools, and Technologies. https://doi.org/10.34657/9171

Roski, M., Walkowiak, M., & Nehring, A. (2021). Universal Design for Learning: The More, the Better? Education Sciences, 11(4), 164. https://doi.org/10.3390/educsci11040164

Vorträge / Presentations

2023

Marvin Roski & Andreas Nehring (2023): Mining Digital Learning Data in Education: A Step-by-Step-Guide Using WordPress. 20th Biennial EARLI Conference.

2022

Marvin Roski & Andreas Nehring (2022): Supporting Inclusive Science Learning through ML. International Conference for AI-based Assessments in STEM Education, University of Georgia, USA.

Marvin Roski, Anett Hoppe & Andreas Nehring (2022): I3Lern: ML für eine individualisierte Lernunterstützung aller Lernenden. Gesellschaft für Didaktik der Chemie und Physik Jahrestagung 2022 (GDCP).

Sascha Schanze, Tom Bleckmann, Lukas Dieckhoff, Gunnar Friege, Andreas Nehring, Jos Oldag & Marvin Roski (2022): Daten in der naturwissenschaftsdidaktischen Forschung. Gesellschaft für Didaktik der Chemie und Physik Jahrestagung 2022 (GDCP).

2021

Marvin Roski and Andreas Nehring (2021): Machine learning-based assessment for inclusive learning support. Jahrestagung der Gesellschaft für Didaktik der Chemie und Physik (GDCP) 2021.

Marvin Roski, Anett Hoppe, Sarah Dannemann, Stefan Dietze, Ralph Ewerth, Gunnar Friege, Ivana Marenzi, Eirini Ntoutsi, Sascha Schanze and Andreas Nehring (2021): Machine Learning in Science Education: Looking into Tomorrow’s Schools – A Systematic Review. Conference of the European Science Education Research Association (ESERA) 2021.