Characterization of Student’s Performance in Massive Open Online Courses (MOOC) - Ching Joe, Tan (Universitas Islam Indonesia, 2024)
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.