Behavioral Involvement, Technology Acceptance, and Failure in Mobile Learning: A Systematic Review

Abstract

As we move further into the digital age, machine learning algorithms have become increasingly popular in E-learning for their ability to predict learner failure and assess behavioral engagement, particularly in mobile learning environments. This paper reports on the systematic review conducted by the most relevant research in the literature that uses machine learning algorithms to predict failure, verify acceptance of mobile technology, and analyze behavioral engagement in mobile learning platforms. The search was performed using research papers extracted from four commonly used databases and published between 2010 and 2023; the last database access was on 15/05/2023. Guided by the PRISMA checklist, the review followed a structured approach to select, analyze, and report relevant studies. Studies were selected based on strict inclusion and exclusion criteria, focusing on peer-reviewed articles that empirically test the application of machine learning in mobile learning contexts. Of the initial 332 screened articles, 20 were eligible for inclusion. The results highlight the transformative role that machine learning is playing in revolutionizing online mobile learning experiences.