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.

Explainable recommender systems (RS) have traditionally followed a one-size-fits-all approach, delivering the same explanation level of detail to each user, without considering their individual needs and goals. Further, explanations in RS have so far been presented mostly in a static and non-interactive manner. To fill these research gaps, we aim in this paper to adopt a user-centered, interactive explanation model that provides explanations with different levels of detail and empowers users to interact with, control, and personalize the explanations based on their needs and preferences. We followed a user-centered approach to design interactive explanations with three levels of detail (basic, intermediate, and advanced) and implemented them in the transparent Recommendation and Interest Modeling Application (RIMA). We conducted a qualitative user study (N = 14) to investigate the impact of providing interactive explanations with varying level of details on the users’ perception of the explainable RS. Our study showed qualitative evidence that fostering interaction and giving users control in deciding which explanation they would like to see can meet the demands of users with different needs, preferences, and goals, and consequently can have positive effects on different crucial aspects in explainable recommendation, including transparency, trust, satisfaction, and user experience.

Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user’s context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user’s personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user’s context.