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Yumou Wei; Paulo Carvalho; John Stamper – International Educational Data Mining Society, 2025
Educators evaluate student knowledge using knowledge component (KC) models that map assessment questions to KCs. Still, designing KC models for large question banks remains an insurmountable challenge for instructors who need to analyze each question by hand. The growing use of Generative AI in education is expected only to aggravate this chronic…
Descriptors: Artificial Intelligence, Cluster Grouping, Student Evaluation, Test Items
Verger, Mélina; Lallé, Sébastien; Bouchet, François; Luengo, Vanda – International Educational Data Mining Society, 2023
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term…
Descriptors: Prediction, Models, Student Behavior, Academic Achievement
Karimov, Ayaz; Saarela, Mirka; Kärkkäinen, Tommi – International Educational Data Mining Society, 2023
Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played…
Descriptors: Student Attitudes, Feedback (Response), Educational Games, Information Retrieval

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