Finding Paths for Explainable MOOC Recommendation: A Learner Perspective
This work addresses the need for explainable recommendations in online education, which is important for learners and educators, but it is incremental as it applies existing graph reasoning methods to a new domain.
The paper tackled the problem of explainable course recommendations for MOOCs by proposing a graph reasoning system, and it validated the approach through user studies and experiments on two educational datasets, showing practical implications and generalizability.
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.