Context-Aware Factorization for Personalized Student's Task Recommendation

Collaborative filtering one of the recommendation techniques has been applied for e-learning recently. This technique makes an assumption that each user rates for an item once. However, in educational environment, each student may perform a task (problem) several times. Thus, applying original collaborative filtering for student's task recommendation may produce unsatisfied results. We propose using context-aware models to utilize all interactions (performances) of the given student-task pairs. This approach can be applied not only for personalized learning environment (e.g., recommending tasks to students) but also for predicting student performance. Evaluation results show that the proposed approach works better than the none-context method, which only uses one recent performance.