Developing an intelligent and sustainable model to improve E-learning satisfaction based on the learner’s personality type: data mining approach in high education systems

Abstract

PurposeAlthough E-learning has been in use for over two decades, running parallel to traditional learning systems, it has gained increased attention due to its vital role in universities in the wake of the COVID-19 pandemic. The primary challenge within E-learning pertains to the maintenance of sustainable effectiveness and the assurance of stakeholders' satisfaction. This research focuses on an intelligence-driven solution to recommend the most effective approach to education policymakers by considering the unique characteristics of all components within the educational system (course type, student and teacher characteristics, and technological features) to achieve a sustainable E-learning system.Design/methodology/approachThrough a systematic literature review and qualitative content analysis, a conceptual model of the critical components influencing E-learning quality and satisfaction has been developed. The proposed model comprises six main dimensions: usage, service quality, learning system quality, content quality, perceived usefulness, and individual characteristics. These dimensions are further divided into 15 components and 114 sub-components. A data mining process encompassing two scenarios has been designed to prioritize the components impacting E-learning success.FindingsIn the first scenario, data mining techniques identified the most influential components based on the features outlined in the conceptual model. According to the results, the components affecting E-learning quality enhancement in the studied case are “usage purpose, system loyalty, technical and supportive system quality, and student characteristics.” The second scenario examines the impact of individuals' personality types and learning styles on E-learning satisfaction across various aspects (Average System Satisfaction, Overall System Satisfaction, Efficiency and Effectiveness, Skill Enhancement, and Personal Enhancement). The findings reveal that, with an accuracy of over 70%, E-learning satisfaction for diplomat and guard learners is influenced by the alignment between “course learning style” and “student-suggested course learning style.” Conversely, for analyzer and researcher types, satisfaction levels are impacted by the “learning style compatible with their personality type.”Originality/valueImplementing a dynamic model founded on data mining enables educational institutions to personalize the E-learning experience for each individual as much as possible. The study’s findings indicate that “achieving higher satisfaction levels in the E-learning process is not necessarily contingent upon providing a learning style congruent with learners' personality types.” Rather, perceived and suggested learning styles exert a more profound influence. Consequently, providing prescriptive principles for higher education institutions seeking to enhance E-learning quality is inadvisable. Instead, adopting a dynamic, knowledge-based process that furnishes recommendations to policymakers for each course—tailored to the specific course type, teaching records, current processes and technology, and student type—is highly recommended.