The Learning Behavior Analysis of Online Vocational Education Students and Learning Resource Recommendation Based on Big Data

Abstract

The Learning Behavior Analysis (LBA) of students is a major function provided by most online education platforms. Based on the results of LBA, students can get customized learning path and recommended learning resources that suit their own situations, which can help them plan their individual training programs of Online Vocational Education (OVE). At first, this paper studied the resource recommendation algorithm based on the modelling of students’ online learning behavior sequences, and solved the problem of conventional learning resource recommendation algorithms of ignoring the dynamic changes in students’ learning preferences. Then, this paper presented the process of LBA applicable for OVE students, and gave a diagram of the flow of using big data to perform LBA. After that, this paper developed a bidirectional encoder based on self-attention model, and described the co-occurrence characteristics and dependencies of students’ online learning behavior sequences. At last, the model pre-training and fine-tuning processes were introduced in detail, and the experimental results verified the effectiveness of the proposed analysis and recommendation methods.