An Effective Prediction Model for Online Course Dropout Rate

Abstract

Due to tremendous reception on digital learning platforms, many online users tend to register for online courses in MOOC offered by many prestigious universities all over the world and gain a lot on cutting edge technologies in niche courses. As the reception of online courses is increasing on one side, there have been huge dropouts of participants in the online courses causing serious problems for the course owners and other MOOC administrators. Hence, it is deemed necessary to find out the root causes of course dropouts and need to prepare a workable solution to prevent that outcome in the future. In this connection, the authors made use of three machine learning algorithms such as support vector machine, random forest, and conditional random fields. The huge samples of datasets were downloaded from the Open University of China, that is, almost 7K student profiles were extracted for the empirical analysis. The datasets were loaded into a confusion matrix and analyzed for the accuracy, precision, recall, and f-score of the model.

Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

“…Overall, all these approaches produced acceptable results, allowing the prediction of students' performance to achieve overall accuracy varying from 65% to 90% [4]. It is a considerable challenge to optimize the results obtained by predictions because different algorithms typically present a large variance in performance rates depending on the combination of several features (e.g., balance among classes, amount of data, input variables, and others) [1].…”

“…Considering the role of forum posts as predictors is also a significant subject of research. They can be an appropriate source of predictive elements (e.g., the number of posts, number of comments, number of votes up, and so on) that are not normally available in face-to-face courses [1].…”

“…Herodotou et al [26] demonstrated that the courses in which teachers used predictive methods delivered at least 15% higher output than the classes without their use [1]. Nevertheless, it is challenging to reach this state because the fewer data is available, the less accurate prediction can be obtained.…”

“…Considering technological progress and increasing demand for online education, VLEs have perceived fast development over the past few years [1]. VLE developers and providers have fully taken advantage of the internet and current web technologies and operate modern education systems to improve students' knowledge and skills.…”

The Sustainability of a Community of Inquiry in Online Course Satisfaction in Virtual Learning Environments in Higher Education

“…There have been a number of concerned issues; notably, self-directed learning, assessment tools, immediate teacher feedback, teacher expertise and personal traits, technical problems, poor engagement, and a lack of interaction, which trigger dissatisfaction among online students [1]. Apart from that, other issues, such as limited access to hardware and software, personal circumstances, lack of experience [2], limited feedback, unsupervised online learning [3], a lack of motivation, technology and Internet issues, and data privacy and security concerns [4], also affect satisfaction [2] and contribute to the failure of online courses, which leads to increasing dropout rates [5][6][7]. These are a number of crucial issues that need to be taken into consideration in order to sustain the quality of online courses.…”