Analysis on quality of learning in e-Learning platforms

Abstract

This paper plans to introduce a model that concerns e-Learning quality management systems under two phases: (i) Questionnaire preparation and (ii) Predicting the impact of e-Learning quality. To analyze the quality of learning in an e-Learning platform, initially, the questionnaire will be prepared with respect to various drivers such as (i) Degree of flexibility and adaptability, (ii) Degree of supportability (students and staff) (iii) Staff qualification and experience, (iv) Performance assessment and (v) Learner's interest. The first driver includes factors like learner control, learner activity, motivation and feedback. The second driver includes factors like technical skills, cost and technical crisis and internet access. The third driver includes factors like awareness of new technology, whether the team includes instructional designers, multimedia procedures and so on. The fourth driver (Performance assessment) includes the impact of a performance evaluation using Artificial Intelligence (AI) methods. Finally, the fifth driver includes course materials, gaming and learners' self-interests. The prepared questionnaire is distributed to different age groups of people and is demanded to fill up the precise information as much as possible. These responses from the people are then taken for analysis purposes. In this research work, the analysis is carried out based on SEM analysis, which identifies the learning quality in the e-Learning platform.