Multi-Bit Collection and Hierarchical Classification Algorithm of Online Open Course Learning Behavior Data with Library Security Modeling

Abstract

An improved particle swarm optimization (PSO) and K-means hybrid clustering algorithm is proposed. The algorithm enhances the diversity of the population by introducing a small probability random mutation operation during the running process, and classifies the complex learning behaviors in the hybrid MOOC context by means of staged dimensionality reduction and clustering, and then identifies the features. Taking the “course push module” and “friend push module” in the framework as the experimental objects, two methods are adopted: the correlation of courses and subjects and the calculation of learners' course selection difference. Use GSEQ software to analyze the online learning behavior sequences of different types of learners, and explore the laws of learning behavior.