Predicting the Acceptance of Mobile Learning Applications During COVID-19 Using Machine Learning Prediction Algorithms

Almaiah, Mohammed Amin; Almomani, Omar; Al-Khasawneh, Ahmad; Althunibat, Ahmad

The global spread of COVID-19 has motivated many universities to adopt online distance learning systems. Mobile learning applications could play a crucial role during this pandemic. Mobile learning applications are increasing popularity among learners due to their benefits and effectiveness. However, the acceptance of mobile learning system among university students is limited. Therefore, this study seeks to understand the main factors influencing the acceptance of mobile learning applications by proposing a hybrid model by combining the TAM with new constructs of TUT model. Machine learning algorithms were employed to analyze the hypothesized relationships among the constructs in the proposed model. The research findings found that RandomForest and IBK algorithms are the best two algorithms in predicting the main determinants of mobile learning acceptance as comparison with other machine learning algorithms with an accuracy of 81.3%. The results of machine learning predictive algorithms showed that constructs of perceived enjoyment, perceived ease of use, perceived usefulness, effectiveness, efficiency, behavioural intention to use and utilization could predict the acceptance of mobile learning within accuracy rate of 87%. The results of this paper will offer valuable directions for mobile learning designers and developers to better promote mobile learning application utilization in universities.