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Using Machine Learning Algorithms to Predict People's Intention to Use Mobile Learning Platforms During the COVID-19
Iman Akour1, Muhammad Alshurideh2,3, Barween Al Kurdi4
1Information Systems Department, University of Sharjah, Sharjah, United Arab Emirates.
JMIR Medical Education
|January 14, 2021
Summary
Mobile learning adoption in UAE higher education is supported by technology acceptance and planned behavior models. Addressing student emotions is crucial for effective remote learning during the COVID-19 pandemic.
Area of Science:
- Educational Technology
- Higher Education Pedagogy
- Digital Learning
Background:
- COVID-19 pandemic necessitated widespread adoption of mobile learning in educational institutions globally.
- Disruption of traditional face-to-face teaching accelerated the use of mobile technologies for learning.
- Mobile learning platforms provide accessible web-based solutions for worldwide education.
Purpose of the Study:
- Investigate the adoption of mobile learning platforms in United Arab Emirates higher education.
- Analyze factors influencing university students' use of mobile learning for academic activities.
Main Methods:
- Utilized an extended technology acceptance model and theory of planned behavior.
- Collected 1880 questionnaires from university students in the United Arab Emirates.
- Employed partial least squares-structural equation modeling and machine learning for data analysis.
Main Results:
- All hypothesized relationships in the research model were supported by the data.
- The J48 classifier achieved 89.37% accuracy in predicting the dependent variable.
- The study confirmed the viability of mobile learning platforms for various academic tasks.
Conclusions:
- Remote learning systems offer significant benefits for teaching and learning during pandemics.
- Student emotions (anxiety, stress, sadness) can negatively impact the effectiveness of remote learning.
- Evaluating and addressing student emotional well-being is essential for successful remote education.
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