Learner Modeling and Recommendation of Learning Resources using Personal Knowledge Graphs

Educational recommender systems (ERS) are playing a pivotal role in providing recommendations of personalized resources and activities to students, tailored to their individual learning needs. A fundamental part of generating recommendations is the learner modeling process that identifies students' knowledge state. Current ERSs, however, have limitations mainly related to the lack of transparency and scrutability of the learner models as well as capturing the semantics of learner models and learning materials. To address these limitations, in this paper we empower students to control the construction of their personal knowledge graphs (PKGs) based on the knowledge concepts that they actively mark as 'did not understand (DNU)' while interacting with learning materials. We then use these PKGs to build semantically-enriched learner models and provide personalized recommendations of external learning resources. We conducted offline experiments and an online user study (N=31), demonstrating the benefits of a PKG-based recommendation approach compared to a traditional content-based one, in terms of several important user-centric aspects including perceived accuracy, novelty, diversity, usefulness, user satisfaction, and use intentions. In particular, our results indicate that the degree of control students are able to exert over the learner modeling process, has positive consequences on their satisfaction with the ERS and their intention to accept its recommendations.