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

Authors

  • Christina Gloerfeld FernUniversität in Hagen
  • Silke Wrede FernUniversität in Hagen
  • Claudia de Witt FernUniversität in Hagen
  • Xia Wang Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)

DOI:

https://doi.org/10.3991/ijai.v2i2.14763

Keywords:

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

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.

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Published

2020-09-25

How to Cite

Gloerfeld, C., Wrede, S., de Witt, C., & Wang, X. (2020). Recommender – Potentials and Limitations for Self-Study in Higher Education from an Educational Science Perspective. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), 2(2), pp. 34–45. https://doi.org/10.3991/ijai.v2i2.14763

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Section

Papers