Recommender – Potentials and Limitations for Self-Study in Higher Education from an Educational Science Perspective

Christina Gloerfeld, Silke Wrede, Claudia de Witt, Xia Wang

Abstract


Artificial intelligence is one of the disruptive technologies, that drives change in our society and economy, but also in our educational system. Educational data mining, machine learning and expert systems are increasingly being used to support study and teaching. This article takes an educational science perspective to present an approach, how to use a recommendation system for students to support inquiry-based learning and self-directed learning. Along the course of the semester various AI-based applications like automatic assessments, interest visualizations or a learning strategy finder assist in the different phases of the semester. When planning and designing this recommendation systems, the most important premise is to foster self-determination of the students.

Keywords


AI in Education, recommender, educational science perspective, inquiry-based learning, self-directed learning, self-determination

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Copyright (c) 2020 Claudia dewitt, Christina Gloerfeld, Silke Wrede, Xia Wang


International Journal of Learning Analytics and Artificial Intelligence for Education. ISSN: 2706-7564
Creative Commons License