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

Author

Listed:
  • Areej Alhothali

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi Arabia
    These authors contributed equally to this work.)

  • Maram Albsisi

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi Arabia
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Areej Alhothali & Maram Albsisi & Hussein Assalahi & Tahani Aldosemani, 2022. "Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A Review," Sustainability, MDPI, vol. 14(10), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6199-:d:819387
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    Citations

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    Cited by:

    1. Jingran Zhang & Feiyue Qiu & Wei Wu & Jiayue Wang & Rongqiang Li & Mujie Guan & Jiang Huang, 2023. "E-Learning Behavior Categories and Influencing Factors of STEM Courses: A Case Study of the Open University Learning Analysis Dataset (OULAD)," Sustainability, MDPI, vol. 15(10), pages 1-22, May.
    2. Ala Smadi & Ahmad Al-Qerem & Ahmad Nabot & Issam Jebreen & Amjad Aldweesh & Mohammad Alauthman & Awad M. Abaker & Omer Radhi Al Zuobi & Musab B. Alzghoul, 2023. "Unlocking the Potential of Competency Exam Data with Machine Learning: Improving Higher Education Evaluation," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    3. Silvia Gaftandzhieva & Ashis Talukder & Nisha Gohain & Sadiq Hussain & Paraskevi Theodorou & Yass Khudheir Salal & Rositsa Doneva, 2022. "Exploring Online Activities to Predict the Final Grade of Student," Mathematics, MDPI, vol. 10(20), pages 1-20, October.
    4. Abul Abrar Masrur Ahmed & Ravinesh C. Deo & Sujan Ghimire & Nathan J. Downs & Aruna Devi & Prabal D. Barua & Zaher M. Yaseen, 2022. "Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model," Sustainability, MDPI, vol. 14(17), pages 1-27, September.

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