E-Learning Readiness Assessment Using Machine Learning Methods

Mohamed Zine, Fouzi Harrou, Mohammed Terbeche, Mohammed Bellahcene, Abdelkader Dairi, Ying Sun

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)8924
Issue number11
StatePublished - Jun 1 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-06-06
Acknowledgements: This research received no external funding. The authors (Fouzi Harrou and Ying Sun) would like to acknowledge the support of the King Abdullah University of Science and Technology (KAUST) in conducting this research. The authors (Mohamed zine, Terbeche Mohammed, Bellahcene Mohammed) acknowledge that ‘This research was carried out under the aegis of the Directorate General of Scientific Research and Technological Development of the Algerian Ministry of Higher Education and Scientific Research’.

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law
  • Geography, Planning and Development


Dive into the research topics of 'E-Learning Readiness Assessment Using Machine Learning Methods'. Together they form a unique fingerprint.

Cite this