Accurate Exercise Recommendation Based on Multidimension -al Feature Analysis. (May 2020)

• Record Type: Journal Article
• Authors: Zhang, Shu; Cai, Jiaqi; Zhuge, Bin; Dong, Ligang; Jiang, Xian
• Is Part Of: Journal of physics. Volume 1544(2020)
• Publication Date: 2020-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1544/1/012048
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: With the rapid development of the Internet, online learning has developed rapidly, and learners are increasingly demanding the personalization and practicality of exercise. Facing the massive exercises in the online learning platform and the online examination system, how to choose the exercises that can be targeted and can make up for the knowledge loopholes has become a hot topic in the field of personalized recommendation of current teaching resources. In view of the fact that learners have a variety of learning features, and there are a large number of online exercises, various types and varying degrees of difficulty, this paper proposes a precise recommendation method based on Multidimensional features analysis for exercises. It quantifies the potential relationship between the learner and the exercise from three aspects: the heat of the exercise itself, the relevance of the knowledge among the exercises, and the similarity of the learner's style, using the linear combination and the Learning to Rank method to build the recommendation model to match learner and exercises exactly. Experiments show that the mean average precision of the ReMFA method reaches 36.8% when recommending five candidate exercises, which can provide learners with personalized exercise recommendation services, thereby improving the learner's learning efficiency.