IRO Journals
Journal of Trends in Computer Science and Smart Technology

Affective Student Engagement Detection using Machine Learning: A Systematic Survey on Methods, Modalities and Learning Environments

Open Access
Published: 04 July, 2026
Pages: 517-537
  • Shruthi H K. , Shruthi H K.
    Department of Computer Science, CHRIST University, Bengaluru, India
    Department of Computer Science, CHRIST University, Bengaluru, India
  • Jayapriya J. , Jayapriya J.
    Department of Computer Science, CHRIST University, Bengaluru, India
    Department of Computer Science, CHRIST University, Bengaluru, India
  • Vinay M. , Vinay M.
    Department of Computer Science, CHRIST University, Bengaluru, India
    Department of Computer Science, CHRIST University, Bengaluru, India
  • Raju Ramakrishna Gondkar Raju Ramakrishna Gondkar
    Department of Statistics, CHRIST University, Bengaluru, India
    Department of Statistics, CHRIST University, Bengaluru, India
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How to Cite

H K., Shruthi, Jayapriya J., Vinay M., and Raju Ramakrishna Gondkar. 2026. “Affective Student Engagement Detection Using Machine Learning: A Systematic Survey on Methods, Modalities and Learning Environments”. Journal of Trends in Computer Science and Smart Technology 8 (3): 517-37. https://doi.org/10.36548/jtcsst.2026.3.005.
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Keywords

Student Engagement Detection
Emotion Recognition
Affective Computing
Machine Learning
Multimodal
Online and Hybrid Learning

Abstract

Student engagement is one of the essential factors an academic success, but tracking the internal affective conditions of learners is a complicated problem in the modern educational environment. The results show that there has been a major shift from using simple behavioral monitoring to more complex affective computing, though visual modalities (63%) are the most used because of their scalability and non-invasive nature. Nevertheless, the results highlight an increased use of multimodal fusion and deep learning systems, particularly, hybrid models (e.g. CNN-LSTM), to effectively learn fine-grained academic emotions (e.g. frustration, boredom, and enjoyment) in online, traditional, and hybrid classes. Furthermore, the review shows a methodological shift towards “in-the-wild” study designs with an emphasis on ecological validity in authentic and uncontrolled learning settings. Although there is significant technical advancement, major gaps remain in establishing standardized benchmark datasets, explaining AI models, and deploying ethical, privacy-protecting frameworks. Overall, this survey highlights that future developments should focus on promoting more responsive, inclusive, and empathetic learning through the development of real-time, pedagogically validated intervention systems.

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