文章基本信息

标题：Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A Review
本地全文：下载
作者：Areej Alhothali ; Maram Albsisi ; Hussein Assalahi 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：10
页码：6199
DOI：10.3390/su14106199
语种：English
出版社：MDPI, Open Access Journal
摘要：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.