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 conceptbased knowledge modeling. We evaluate our approach in the domain of learning programming.
REF proposed a method for personalized recommendation of assignments (tasks or exercises) in an adaptive educational system, which has enhanced existing adaptive navigation approaches by considering the limited time for learning, learners' knowledge level and the difficulty of the exercises.
Exercises recommending for limited time learning
RecSysTEL@RecSys
Computer Science