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Paper Type
ERF
Description
The field of education has the potential to better facilitate student learning by employing educational recommenders that adapt the learning process to the needs of individual learners. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design of these systems. The theory considered here, self-regulated learning, focuses on putting students in control of their learning and is appropriate in situations where learning is autonomous. This research proposes a design science approach to investigate a theoretical base of self-regulated learning (SRL) for a knowledge-based recommender design framework. Existing research on knowledge-based recommender design with an inclusion of an ontology component will guide development of this artifact. Anticipated results include the formation of design principles to inform the creation of SRL guided recommenders for both practical applications and future research.
Paper Number
1244
Recommended Citation
McNett, Alicia and Noteboom, Cherie Bakker, "A Self-Regulated Learning Approach to Educational Recommender Design" (2023). AMCIS 2023 Proceedings. 5.
https://aisel.aisnet.org/amcis2023/sig_ed/sig_ed/5
A Self-Regulated Learning Approach to Educational Recommender Design
The field of education has the potential to better facilitate student learning by employing educational recommenders that adapt the learning process to the needs of individual learners. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design of these systems. The theory considered here, self-regulated learning, focuses on putting students in control of their learning and is appropriate in situations where learning is autonomous. This research proposes a design science approach to investigate a theoretical base of self-regulated learning (SRL) for a knowledge-based recommender design framework. Existing research on knowledge-based recommender design with an inclusion of an ontology component will guide development of this artifact. Anticipated results include the formation of design principles to inform the creation of SRL guided recommenders for both practical applications and future research.
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