Finding Paths for Explainable MOOC Recommendation: A Learner Perspective
Jibril Frej Neel Shah Marta Knežević Tanya Nazaretsky Tanja Käser
The increasing availability of Massive Open Online Courses (MOOCs) has
created a necessity for personalized course recommendation systems. These
systems often combine neural networks with Knowledge Graphs (KGs) to achieve
richer representations of learners and courses. While these enriched
representations allow more accurate and personalized recommendations,
explainability remains a significant challenge which is especially problematic
for certain domains with significant impact such as education and online
learning. Recently, a novel class of recommender systems that uses
reinforcement learning and graph reasoning over KGs has been proposed to
generate explainable recommendations in the form of paths over a KG. Despite
their accuracy and interpretability on e-commerce datasets, these approaches
have scarcely been applied to the educational domain and their use in practice
has not been studied. In this work, we propose an explainable recommendation
system for MOOCs that uses graph reasoning. To validate the practical
implications of our approach, we conducted a user study examining user
perceptions of our new explainable recommendations. We demonstrate the
generalizability of our approach by conducting experiments on two educational
datasets: COCO and Xuetang.