Exercises recommending for limited time learning

Abstract

In this paper we propose a method for personalized recommendation of assignments (tasks or exercises) in an adaptive educational system. Our main goal is to help students to achieve better performance in tests. To achieve this we enhance existing adaptive navigation approaches by considering the limited time for learning. Our strategy is to cover all the required topics at least to some extent rather than learn few topics perfectly. The proposed method uses utility-based recommending and concept-based knowledge modeling. We evaluate our approach in the domain of learning programming.