Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A Review

Areej Alhothali, Maram Albsisi, Hussein Assalahi and Tahani Aldosemani
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Areej Alhothali: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi Arabia
Maram Albsisi: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi Arabia
Hussein Assalahi: English Language Institute, King Abdulaziz University, Jeddah 22254, Saudi Arabia
Tahani Aldosemani: College of Education, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia

Sustainability, 2022, vol. 14, issue 10, 1-23

Abstract: Recent years have witnessed an increased interest in online education, both massive open online courses (MOOCs) and small private online courses (SPOCs). This significant interest in online education has raised many challenges related to student engagement, performance, and retention assessments. With the increased demands and challenges in online education, several researchers have investigated ways to predict student outcomes, such as performance and dropout in online courses. This paper presents a comprehensive review of state-of-the-art studies that examine online learners’ data to predict their outcomes using machine and deep learning techniques. The contribution of this study is to identify and categorize the features of online courses used for learners’ outcome prediction, determine the prediction outputs, determine the strategies and feature extraction methodologies used to predict the outcomes, describe the metrics used for evaluation, provide a taxonomy to analyze related studies, and provide a summary of the challenges and limitations in the field.

Keywords: MOOCs; SPOCs; student performance; student dropout; machine learning; learning behaviour; learning analytics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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