Affiliations: [a]
School of Information Management, Jiangxi University of Finance and Economics, Nanchang, China
| [b]
School of Computer and Information Engineering, Jiangxi Agriculture University, Nanchang, China
Abstract: Intelligent exercise recommendation is a research focus in the field of online learning that can help learners quickly find exercises suitable for them from the exercise bank. However, exercise recommendation differs from product or film recommendation because of some special requirements. First, the recommended exercises must cover all knowledge points geared toward the learning objective of the learner. Second, the difficulty of exercises must match the knowledge level of the target learner. In response to the above requirements, this study proposes an exercise recommendation algorithm that integrates learning objective and assignment feedback. This algorithm considers not only the coverage of knowledge points but also the knowledge level of learners to help them find highly suitable exercises. According to this algorithm, the learning objective of the learner must be initially identified to obtain a course knowledge set that suits his/her learning objective. Second, the understanding of the learner about the knowledge set must be judged based on the assignment feedback. Third, suitable exercises are recommended based on the knowledge level of the learner and the course knowledge structure. The proposed algorithm is experimentally verified by using a real-world dataset and by comparing it with other algorithms. The experimental results show that the proposed algorithm significantly outperforms the other algorithms in both precision and recall. Based on these results, the proposed algorithm can achieve an excellent recommendation performance.
Keywords: Recommender systems, online learning, assignment feedback, course knowledge set
