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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science
Ching Joe, Tan
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Characterization of Student’s Performance in Massive Open Online Courses (MOOC) Ching Joe, Tan
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art3

Abstract

Massive Open Online Courses (MOOC) allow students to learn online at any time and from any location. Unfortunately, poor completion rates and a large student group make it difficult for teachers to keep track of their student’s progress. Due to a lack of adequate counselling, students who perform poorly are more likely to give up. The goal of this study was to predict student’s certification by analyzing data on student’s learning behavior. The initial data on learning behavior was obtained from edX, a well-known MOOC platform. Based on this data, three statistical models such as logistic regression, graph convolutional network, and cluster analysis were utilized to predict student’s performance. The proposed model’s usefulness was demonstrated by using a testing set of data from the actual courses. Our findings showed that tracking student activity in terms of number of unique days active, watching videos, participating in forum discussions, and exploring more courseware content might help predict student’s performance in MOOC and enhance completion rates.
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