Educational Explainable Recommender Usage and its Effectiveness in High School Summer Vacation Assignment
Kyosuke Takami, Yiling Dai, Brendan Flanagan, Hiroaki Ogata
Abstract
Explainable recommendations, which provide explanations about why an item is recommended, help to improve the transparency, persuasiveness, and trustworthiness. However, few research in educational technology utilize explainable recommendations. We developed an explanation generator using the parameters from Bayesian knowledge tracing models. We used this educational explainable recommendation system to investigate the effects of explanation on the summer vacation assignment for high school students. Comparing the click counts of recommended quizzes with and without explanations, we found that the number of clicks was significantly higher for quizzes with explanations. Furthermore, system usage pattern mining revealed that students can be divided to three clusters— none, steady and late users. In the cluster of steady users, recommended quizzes with explanations were continuously used. These results suggest the effectiveness of an explainable recommendation system in the field of education.