E-Learning Readiness Assessment Using Machine Learning Methods
Sustainability
; 15(11):8924, 2023.
Article
in English
| ProQuest Central | ID: covidwho-20245432
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.
Environmental Studies; e-learning readiness; ADKAR factors; machine learning; distance education; online learning; Higher education; Students; Identification methods; Communication; Correlation analysis; Questionnaires; Educational technology; Colleges & universities; Developing countries--LDCs; Nonlinear systems; Correlation coefficients; Correlation coefficient; Reinforcement; Learning algorithms; Decision trees; COVID-19; Infrastructure; Pandemics; Flexibility; Online instruction; Algorithms; Access to education; Distance learning; Algeria
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Observational study
/
Prognostic study
/
Randomized controlled trials
Language:
English
Journal:
Sustainability
Year:
2023
Document Type:
Article
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