Analysis and Intervention Mechanism for Learning Behaviors of Higher Vocational Students in Mobile Learning Environments

Abstract

With the rapid digital transformation of higher vocational education, mobile learning has become a key means of addressing the limitations of traditional instruction and supporting fragmented, career-oriented learning. However, issues such as low engagement, weak persistence, and fragmented behavioral patterns continue to hinder instructional quality. Existing research often relies on static or single-dimension analyses, lacking quantitative tools to capture the dynamic processes of information diffusion, group imitation, and behavioral evolution. This gap has hindered the theoretical precision required for designing effective intervention mechanisms. To address this deficiency, an evolutionary game model was constructed that integrates the topological features of mobile interaction networks with information diffusion rules to analyze the mechanisms and determinants of learner behavior. Based on model equilibrium analyses, a targeted multidimensional intervention mechanism was proposed, including payoff-matrix optimization, structural adjustment of networks, initial-strategy guidance, and dynamic adaptive regulation. The model shifts research from static description to dynamic quantitative prediction and broadens the application boundary of the evolutionary game theory in educational technology research. In addition, it offers practical strategies for improving mobile learning management and instructional quality in higher vocational institutions. The findings offer substantive academic value and practical significance.