A Model Predicting Student Engagement and Intention with Mobile Learning Management Systems

Abstract

Aim/Purpose: The aim of this study is to develop and evaluate a comprehensive model that predicts students’ engagement with and intent to continue using mobile-Learning Management Systems (m-LMS). Background: m-LMS are increasingly popular tools for delivering course content in higher education. Understanding the factors that affect student engagement and continuance intention can help educational institutions to develop more effective and user-friendly m-LMS platforms. Methodology: Participants with prior experience with m-LMS were employed to develop and evaluate the proposed model that draws on the Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and other related models. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to evaluate the model. Contribution: The study provides a comprehensive model that takes into account a variety of factors affecting engagement and continuance intention and has a strong predictive capability. Findings: The results of the study provide evidence for the strong predictive capability of the proposed model and supports previous research. The model identifies perceived usefulness, perceived ease of use, interactivity, compatibility, enjoyment, and social influence as factors that significantly influence student engagement and continuance intention. Recommendations for Practitioners: The findings of this study can help educational institutions to effectively meet the needs of students for interactive, effective, and user-friendly m-LMS platforms. Recommendation for Researchers: This study highlights the importance of understanding the antecedents of students’ engagement with m-LMS. Future research should be conducted to test the proposed model in different contexts and with different populations to further validate its applicability. Impact on Society: The engagement model can help educational institutions to understand how to improve student engagement and continuance intention with m-LMS, ultimately leading to more effective and efficient mobile learning. Future Research: Additional research should be conducted to test the proposed model in different contexts and with different populations to further validate its applicability.