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

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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.

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COVID-19behaviorfearintentmachine learningmobile learningonline learningpandemicpredictiontechnology acceptance modeltheory of planned behavior

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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.