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

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Emerging Technologies During the Era of COVID-19 Pandemic

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 348))

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Hofuf, Saudi Arabia

    Mohammed Amin Almaiah

  2. Computer Network and Information Systems Department, The World Islamic Sciences and Education University, Amman, Jordan

    Omar Almomani

  3. CIS Department, President, Irbid National University, Irbid, Jordan

    Ahmad Al-Khasawneh

  4. Professor, Hashemite University, Irbid, Jordan

    Ahmad Al-Khasawneh

  5. CIS Department, Hashemite University, Zarqa, Jordan

    Ahmad Al-Khasawneh

  6. Department of Software Engineering, Al-Zaytoonah University of Jordan, Amman, 11947, Jordan

    Ahmad Althunibat

Authors
  1. Mohammed Amin Almaiah
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  2. Omar Almomani
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  3. Ahmad Al-Khasawneh
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  4. Ahmad Althunibat
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Corresponding author

Correspondence to Mohammed Amin Almaiah .

Editor information

Editors and Affiliations

  1. Tokat Gaziosmanpasa University, Tokat, Turkey

    Ibrahim Arpaci

  2. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  3. Universiti Malaysia Kelantan, Kelantan, Malaysia

    Mohammed A. Al-Sharafi

  4. Polytechnic of Coimbra, Rua General Santos Costa, Portugal

    Gonçalo Marques

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Almaiah, M.A., Almomani, O., Al-Khasawneh, A., Althunibat, A. (2021). Predicting the Acceptance of Mobile Learning Applications During COVID-19 Using Machine Learning Prediction Algorithms. In: Arpaci, I., Al-Emran, M., A. Al-Sharafi, M., Marques, G. (eds) Emerging Technologies During the Era of COVID-19 Pandemic. Studies in Systems, Decision and Control, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-67716-9_20

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