Adaptive recommender system for an intelligent classroom teaching model

Abstract

The development of information technology has facilitated the use of intelligent classroom models supported by information technology to improve the college students' comprehensive quality and ability. However, the existing models are too sophisticated to be applied to the actual teaching process, and ignore the individualized teaching characteristics of students. Therefore, an intelligent classroom model with adaptive learning resource recommendation is proposed. First, the entire teaching process was divided into three stages which were used to combine teachers' teaching and students' learning. Second, the key problems of the learning resources recommendation system were studied and a learning resource recommendation based on Teaching Resources-Latent Dirichlet Allocation (TR-LDA) is proposed. It used an improving structure model of three layers (documents, theme, and words). The proposed intelligent classroom model was verified in practical teaching. The results show that the new model with adaptive learning resources recommendation can help to improve students' learning efficiency. The relevant conclusions can be used as a reference for exploring the use of information technology to improve the quality of undergraduate professional course teaching.