Scrutinizing student dropout issues in MOOCs using an intuitionistic fuzzy decision support system

Author:

Pandey Mamta1,Litoriya Ratnesh2,Pandey Prateek3

Affiliation:

1. Aditya Engineering College, Surempalem, Andhra Pradesh, India

2. Medi-caps University, Indore India

3. Jaypee University of Engineering and Technology, Guna India

Abstract

Massive open online courses (MOOCs) are a recent e-learning programme that has received widespread acceptance among several colleges. Student dropout from MOOCs is a big worry in higher education and policy-making circles, as it occurs frequently in colleges that offer these types of courses. The majority of student dropouts are caused by causes beyond the institution’s control. Using an IF-DEMATEL (Intuitive Fuzzy Decision-making Trial and Evaluation Laboratory) approach, the primary factors and potential causal relationships for the high dropout rate were identified. The most effective aspects of massive open online courses (MOOCs) are identified using IF-DEMATEL and CIFCS. Moreover, it explains the interconnectedness of the various MOOC components. As an added measure, a number of DEMATEL techniques are used to conduct a side-by-side comparison of the results. Decisions made by the educational organisation could benefit from the findings. According to the research, there are a total of twelve indicators across four dimensions that are related to online course withdrawal amongst students. Then, experienced MOOC instructors from various higher education institutions were invited to assess the level of influence of these characteristics on each other. Academic skills and talents, prior experience, course design, feedback, social presence, and social support were identified as six primary characteristics that directly influenced student dropout in MOOCs. Interaction, course difficulty and length, dedication, motivation, and family/work circumstances have all been found to play a secondary part in student dropout in massive open online courses (MOOCs). The causal connections between the major and secondary factors were traced and discussed. The results of this study can help college professors and administrators come up with and implement effective ways to reduce the high number of students who drop out of massive open online courses (MOOCs).

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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