E-Learning Readiness Assessment Using Machine Learning Methods

Mohamed Zine, Fouzi Harrou (), Mohammed Terbeche, Mohammed Bellahcene, Abdelkader Dairi and Ying Sun
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Mohamed Zine: LEPPESE Laboratory, Institute of the Economics and Management Sciences, University Centre of Maghnia, PB 600-13300 Al-Zawiya Road, Al-Shuhada District, Maghnia 13300, Algeria
Fouzi Harrou: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Mohammed Terbeche: LEPPESE Laboratory, Institute of the Economics and Management Sciences, University Centre of Maghnia, PB 600-13300 Al-Zawiya Road, Al-Shuhada District, Maghnia 13300, Algeria
Mohammed Bellahcene: LEPPESE Laboratory, Institute of the Economics and Management Sciences, University Centre of Maghnia, PB 600-13300 Al-Zawiya Road, Al-Shuhada District, Maghnia 13300, Algeria
Abdelkader Dairi: Computer Science Department, University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), El Mnaouar, BP 1505, Oran 31000, Algeria
Ying Sun: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia

Sustainability, 2023, vol. 15, issue 11, 1-22

Abstract: Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students’ readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model’s five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students’ abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students’ e-learning readiness.

Keywords: e-learning readiness; ADKAR factors; machine learning; distance education; online learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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