Using Machine Learning Algorithms to Predict People's Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach

Iman Akour; Muhammad Alshurideh; Barween Al Kurdi; Amel Al Ali; Said Salloum

doi:10.2196/24032

Using Machine Learning Algorithms to Predict People's Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach

JMIR Med Educ. 2021 Feb 4;7(1):e24032. doi: 10.2196/24032.

Authors

Iman Akour^#¹, Muhammad Alshurideh^#^{2

3}, Barween Al Kurdi^#⁴, Amel Al Ali^#¹, Said Salloum^#⁵

Affiliations

¹ Information Systems Department, University of Sharjah, Sharjah, United Arab Emirates.
² Department of Management, University of Sharjah, Sharjah, United Arab Emirates.
³ Department of Marketing, The University of Jordan, Amman, Jordan.
⁴ Department of Business Administration, Faculty of Economics and Administrative Sciences, The Hashemite University, Zarqa, Jordan.
⁵ Research Institute of Sciences & Engineering, University of Sharjah, Sharjah, United Arab Emirates.

^# Contributed equally.

PMID: 33444154
PMCID: PMC8081278
DOI: 10.2196/24032

Abstract

Background: Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide.

Objective: This study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions.

Methods: An extended technology acceptance model and theory of planned behavior model were proposed to analyze university students' adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey.

Results: Based on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable.

Conclusions: Our study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic.

Keywords: COVID-19; behavior; fear; intent; machine learning; mobile learning; online learning; pandemic; prediction; technology acceptance model; theory of planned behavior.

©Iman Akour, Muhammad Alshurideh, Barween Al Kurdi, Amel Al Ali, Said Salloum. Originally published in JMIR Medical Education (http://mededu.jmir.org), 04.02.2021.