Abstract
This investigation is done during COVID-19 to identify, rank, and classify MOOC (massive open online course) key acceptance factors (KAFs) from an Indian perspective. A systematic literature review identifies 11 KAFs of MOOC. One more novel factor named ‘contingent instructor' is proposed by the authors considering pandemic and new normal post-COVID-19. The paper implements two popular fuzzy MCDM (multiple-criteria decision-making) techniques, namely fuzzy TOPSIS and fuzzy AHP, on 12 KAFs. The fuzzy TOPSIS approach is used to rank factors. Affordability, performance expectancy and digital didactics are found as the top three KAFs. Fuzzy AHP classified KAFs into three groups, namely high, moderate, and low influential. Examination of the literature indicates that this study is among the first attempt to prioritize and classify MOOC KAFs using fuzzy TOPSIS and fuzzy AHP approach. The results offer managerial guidance to stakeholders for effective management of MOOC, resulting in higher acceptance rate. Likewise, this investigation will upgrade the comprehension of MOOC KAFs among academicians.
Highlights
TO THE STUDYThe educational domain is changing quickly due to progressions in the Information and Communication Technologies (ICT) (Paliwoda-Pękosz & Stal, 2015; Sharma et al, 2017)
The current study proposes the application of two popular fuzzy multi-criteria decision making (FMCDM) techniques, (1) Fuzzy AHP and (2) Fuzzy TOPSIS for Massive Open Online Course (MOOC) key acceptance factors (KAFs) classification and ranking respectively
Contingent instructor, being a novel factor got the fourth position in the ranking and grouped under the moderate influential category because it offers learning solutions to many learners during pandemic when very limited options of learning are available to them
Summary
The educational domain is changing quickly due to progressions in the Information and Communication Technologies (ICT) (Paliwoda-Pękosz & Stal, 2015; Sharma et al, 2017). Students’ incessant exit from courses puts learning and education in danger and blocks the formative advancement of MOOCs. The legitimate explanations given by researchers for low completion rate were: (1) limited self-regulated learning aspects; (2) lack of basic computing skills; (3) clashes with. A study is needed to identify factors that are critical for learners to accept MOOC. Based on that true generalisation of the facts cannot be done This motivated researchers to conduct study of MOOC acceptance in a densely populated developing nation like India for better generalisation of facts. The following section systematically examines prior MOOC literature to identify KAFs. The subsequent section conceptual development of adopted methodology discusses algorithm of two MCDM techniques namely, fuzzy TOPSIS and fuzzy AHP along with their application on MOOC KAFS. Studies were arranged as per their quality score for analysing their rigorousness, credibility, and relevance
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