Machine Learning in Educational Data Mining

Dear Colleagues,

Educational Data Mining and Learning Analytics are two interlinked and fast-growing research fields with a view to extracting meaningful information from educational data and improving the quality of education. The growth of interest in these fields is depicted by high-quality research which is mainly targeted around the employment of Data Mining (DM) and Machine Learning (ML) methods in data gathered from a variety of educational environments. Practical applications of ML in the EDM and LA fields open up new horizons and give rise to new challenges for scientists and researchers. Recent advances in these fields include issues such as transferability, explainability and interpretability of learning models. However, it is clear that there is much still to be done in both fields.

This Special Issue (SI) is centered on theory and practice of Machine Learning (ML) methods in the fields of EDM and/or LA. Therefore, we invite authors to submit original research that fall within the focus of the SI. Topics of interest include, but are not limited to:

• Prediction of student learning outcomes
• Identification of student behavioral patterns
• Early identification of at-risk students
• Personalized support and learning recommendations
• Student modelling
• Protecting student privacy in analyses of educational data
• Transferability of learning models
• Deep learning methods and applications
• Automatic assessment of student knowledge
• Game-based learning
• Smart class
• Course recommendation
• Interpretable and explainable Artificial Intelligence in educational applications

Dr. Georgios Kostopoulos
Assist. Prof. Dr. Sotiris Kotsiantis
Guest Editors

Manuscript Submission Information

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