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A new ML-based approach to enhance student engagement in online environment

Author

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  • Sarra Ayouni
  • Fahima Hajjej
  • Mohamed Maddeh
  • Shaha Al-Otaibi

Abstract

The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

Suggested Citation

  • Sarra Ayouni & Fahima Hajjej & Mohamed Maddeh & Shaha Al-Otaibi, 2021. "A new ML-based approach to enhance student engagement in online environment," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-16, November.
  • Handle: RePEc:plo:pone00:0258788
    DOI: 10.1371/journal.pone.0258788
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    References listed on IDEAS

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    1. Fahima Hajjej & Sarra Ayouni & Hadil Shaiba & Ala Saleh Alluhaidan, 2021. "Student Perspective-Based Evaluation of Online Transition During the COVID-19 Outbreak: A Case Study of PNU Students," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 16(5), pages 21-38, September.
    2. Jung-Sook Lee, 2014. "The Relationship Between Student Engagement and Academic Performance: Is It a Myth or Reality?," The Journal of Educational Research, Taylor & Francis Journals, vol. 107(3), pages 177-185, May.
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