Inhaltsverzeichniz
Publikationen
2024
Roski, M., Sebastian, R., Ewerth, R., Hoppe, A., Nehring, A. (2024): Learning analytics and the Universal Design for Learning (UDL): A clustering approach. Computers & Education, 214, 105028. https://doi.org/10.1016/j.compedu.2024.105028
Roski, M., Ewerth, R., Hoppe, A., Nehring, A. (2024): Exploring Data Mining in Chemistry Education: Building a Web-Based Learning Platform for Learning Analytics. Journal of Chemical Education. https://doi.org/10.1021/acs.jchemed.3c00794
2023
Bleckmann, T., Friege, G. (2023): Concept maps for formative assessment: Creation and implementation of an automatic and intelligent evaluation method. Knowledge Management & E-Learning, 15(3), pages 433–447. https://doi.org/10.34105/j.kmel.2023.15.025
Günther, L., Marten, N., Berendes, K. (2022): Informal learning situations in the context of mathematics studies. Development of an analysis framework. INDRUM 2022 PROCEEDGINGS: Fourth conference of the International Network for Didactic Research in University Mathematics. Hannover.
Günther, L., Hochmuth, R. (2023): Thoughts on Different Types of Mathematical Enculturation at the Secondary-Tertiary-Transition . In The 13th congress of the European Society for Research in Mathematics Education (CERME 13). Budapest, Hungary. https://hal.science/hal-04406688
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.
Laukert, L., Hausberger, T., Hochmuth, R. (2023): Calculus at the School to University transition: early stages of a structuralist perspective in Real Analysis. In The 13th congress of the European Society for Research in Mathematics Education (CERME 13). Budapest, Hungary. https://hal.science/hal-04406746
Le Quy, T., Friege, G., Ntoutsi, E. (2023): Multi-fair Capacitated Students-Topics Grouping Problem. In Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings, Part I (pp. 507-519). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-33374-3_40
Le Quy, T., Friege, G., & Ntoutsi, E. (2023). A review of clustering models in educational data science towards fairness-aware learning. Educational Data Science-Essentials, Approaches, and Tendencies – Proactive Education based on Empirical Big Data Evidence, Springer. 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
Navarrete, E., Nehring, A., Schanze, S., Ewerth, R., Hoppe, A. (2023): A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness. arXiv preprint. https://arxiv.org/abs/2301.13617
Roski, M., Sebastian, R., Ewerth, R., Hoppe, A., Nehring, A. (2023): Dropout Prediction in a Web Environment based on Universal Design for Learning. International Conference on Artificial Intelligence in Education (AIED), Tokyo, Japan. https://doi.org/10.1007/978-3-031-36272-9_42
Stamatakis, M., Gritz, W., Oldag, J., Hoppe, A., Schanze, S., Ewerth, R. (2023): Automatic Analysis of Student Drawings in Chemistry Classes. In: Artificial Intelligence in Education (AIED), Tokyo, Japan. https://doi.org/10.1007/978-3-031-36272-9_78
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
Le Quy, T., Nguyen, T. H., Friege, G., Ntoutsi, E. (2022). Evaluation of group fairness measures in student performance prediction problems. In The 7th Workshop on Data Science for Social Good (SoGood) co-located with ECML/PKDD 2022.
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 (EDM). https://educationaldatamining.org/EDM2021/EDM2021Proceedings.pdf
Le Quy, T., Ntoutsi, E. (2021): Towards fair, explainable and actionable clustering for learning analytics. In The 14th International Conference on Educational Data Mining (EDM).
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
Roski, M., Hoppe, A., Dannemann, S., Dietze, S., Ewerth, R., Friege, G., Marenzi, I., Ntoutsi, E., Schanze, S., Nehring, A. (2021): Machine learning in science education: looking into tomorrow’s schools – A systematic review. 14th Conference of the European Science Education Research Association (ESERA).
Vorträge / Presentations
2023
Bleckmann, T., Friege, G. (2023): Verwendung von ML zur Auswertung Concept Maps in der Mechanik. DPG-Frühjahrstagung der Sektion Atome, Moleküle, Quantenoptik und Photonik (SAMOP).
Bleckmann T., Gritz, W., Friege G. (2023): Using Machine Learning for a qualitative evaluation of Concept Maps: Newopportunities for formative assessment? NARST 96th Annual International Conference. Chicago, IL, USA
Markovnikova, A. Schanze, S. (2023): Virtuelles Chemielabor: die Rolle von Embodied Education für Kinder mit Zerebralparese. Gesellschaft für Didaktik der Chemie und Physik Jahrestagung (GDCP), Hamburg.
Marten, N., Thiele K. (2023): Prototyp Zukunft – Lösungen für transformative Lehre teilen. Turn Conference, Köln.
Marten, N., Thiele, K. (2023): Studieren mit digitalen Medien. Paper presented at the ASIM Workshop STS-GMMS-EDU, Magdeburg.
Marten, N., Thiele K. (2023): The 21st SEFI Special Interest Group in Mathematics Seminar. Mathematics Teaching in Engineering Education (MSIG2023), Tampere, Finnland.
Roski M., Nehring A. (2023): Mining Digital Learning Data in Education: A Step-by-Step-Guide Using WordPress. 20th Biennial EARLI Conference.
Roski, M., & Nehring, A. (2023): Ich sehe was, was du nicht siehst. didacta Digital. https://avr-emags.de/emags/didactaDIGITAL/didactaDIGITAL_0223/#14
Roski, M., Sebastian, R., Ewerth, R., Hoppe, A., Nehring, A. (2023): Digitales Lernen mit UDL-Features: Learning Analytics durch Clustering. Gesellschaft für Didaktik der Chemie und Physik Jahrestagung 2023, Hamburg.
Roski, M., Nehring, A. (2023). I3Lern: Learning Analytics in einer web-basierten
Lernplattform f ̈ur den Chemieunterricht. Adaptives Lernen und KI in der schulischen
und beruflichen Bildung. Joachim Herz Stiftung.
2022
Bleckmann, T., Gritz, W., Friege, G. (2022): Analysis of Concept Maps for the use in Formative Assessment: Can Machine Learning help? NARST 95th Annual International Conference. Vancouver, British Columbia, Canada.
Bleckmann, T., Friege, G. (2022): Using Machine Learning to Analyze Concept Maps for Formative Assessment: An Overview of Opportunitites and Risks. Concept Map Conference. Valletta, Malta.
Bleckmann, T., Friege, G. (2022): Automatische Auswertung von Concept Maps: Wie kann Machine Learning helfen? Gesellschaft für Didaktik der Chemie und Physik Jahrestagung (GDCP).
Oldag, J., Stamatakis, M., Schanze, S. (2022): Analyse von Lernendenzeichnungen: Wie kann Machine Learning helfen? Gesellschaft für Didaktik der Chemie und Physik Jahrestagung (GDCP).
Roski M., Nehring A. (2022): Supporting Inclusive Science Learning through ML. International Conference for AI-based Assessments in STEM Education, University of Georgia, USA.
Roski M., Hoppe A., Nehring A. (2022): I3Lern: ML für eine individualisierte Lernunterstützung aller Lernenden. Gesellschaft für Didaktik der Chemie und Physik Jahrestagung (GDCP).
Schanze S., Bleckmann B., Dieckhoff L., Friege G., Nehring A., Oldag J., Roski M. (2022): Daten in der naturwissenschaftsdidaktischen Forschung. Gesellschaft für Didaktik der Chemie und Physik Jahrestagung (GDCP).
2021
Bleckmann, T., Dieckhoff, L., Friege, G. (2021): LernMINT – interdisziplinär, innovativ und zukunftsorientiert. GDCP Focus Conference: Machine Learning and Computer-Based Text Analysis. Potentials and Challenges for Science Education, online.
Oldag, J., Schanze, S. (2021): Kategoriensystem für Zeichenelemente in einer Lernendenzeichnung. Gesellschaft für Didaktik der Chemie und Physik Schwerpunkttagung Maschinelles Lernen und computerbasierte Textanalysen 2021.
Roski, M., Nehring A. (2021): Machine learning-based assessment for inclusive learning support. Jahrestagung der Gesellschaft für Didaktik der Chemie und Physik (GDCP) 2021.
Roski M., Hoppe A., Dannemann S., Dietze S., Ewerth R., Friege G., Marenzi I., Ntoutsi E., Schanze S., Nehring A. (2021): Machine Learning in Science Education: Looking into Tomorrow’s Schools – A Systematic Review. Conference of the European Science Education Research Association (ESERA) 2021.
2020
Roski, M., Hoppe, A., Dannemann, S., Dietze, S., Ewerth, R., Friege, G., Marenzi, I., Ntoutsi, E., Schanze, S., Nehring, A. (2020): Unterstützung von Lehr-Lern-Prozessen durch maschinelles Lernen. Gesellschaft für Didaktik der Chemie und Physik Jahrestagung 2020.
lernmint.org 