WCSE

Predicting Factors Influencing Acceptance on Online Learning Platforms During the COVID-19 Pandemic

Abstract— The sudden shift from the traditional face-to-face classes to fully online learning due to the COVID-19 pandemic stunned the education sector worldwide. The abrupt adjustment led to quick decision on what online learning platform to utilize during the COVID-19 pandemic. The aim of this study was to predict factors influencing students actual use and acceptance of online learning platforms during the COVID-19 pandemic. Through convenience sampling, a total of 712 respondents answered the selfadministered online survey voluntarily. Utilizing random forest classifier with Python 3.8, the results revealed that system and information quality, ease of use, and perceived ease of use of online learning platforms led to high and very high actual use. The results of this study indicated that as long as there is comfort, ease of use, design, user friendly, and accessibility among the online learning platforms, there would be high to very high actual usage, leading to acceptance among students. This study presented a 92% accuracy with 0.00 standard deviation, thus indicating the reliability of the outcome. Lastly, the findings could be applied by other educational sectors worldwide.

Index Terms— Random forest classifier, online learning platforms, TAM, delone and mclean IS success model.

Ardvin Kester S. Ong
School of Industrial Engineering and Engineering Management, Mapua University, Philippines
Ma. Janice J. Gumasing
School of Industrial Engineering and Engineering Management, Mapua University, Philippines
Yogi Tri Prasetyo
School of Industrial Engineering and Engineering Management, Mapua University, Philippines

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Cite: Ardvin Kester S. Ong, Ma. Janice J. Gumasing, Yogi Tri Prasetyo, "Predicting Factors Influencing Acceptance on Online Learning Platforms During the COVID-19 Pandemic," WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications, pp. 247-252, Sanya, China, April 15-18, 2022.