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Transfer Learning Approach for Analyzing Attentiveness of Students in an Online Classroom Environment with Emotion Detection

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Innovations in Computational Intelligence and Computer Vision

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1424))

Abstract

There is a crucial need for advancement in the online educational system due to the unexpected, forced migration of classroom activities to a fully remote format, due to the coronavirus pandemic. Not only this, but online education is the future, and its infrastructure needs to be improved for an effective teaching–learning process. One of the major concerns with the current video call-based online classroom system is student engagement analysis. Teachers are often concerned about whether the students can perceive the teachings in a novel format. Such analysis was involuntarily done in the offline mode, however, is difficult in an online environment. This research presents an autonomous system for analyzing the students’ engagement in the class by detecting the emotions exhibited by the students. This is done by capturing the video feed of the students and passing the detected faces to an emotion detection mode. The emotion detection model in the proposed architecture was designed by fine-tuning VGG16 pre-trained image classifier model. Lastly, the average student engagement index is calculated. Authors received considerable performance setting reliability of the use of the proposed system in real time giving a future scope to this research.

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Notes

  1. 1.

    https://github.com/karankv26/Google-image-webscraper.

  2. 2.

    https://github.com/karankv26/Google-image-webscraper/tree/main/dataset.

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Author information

Authors and Affiliations

  1. Sri Sairam Engineering College, Chennai, India

    K. V. Karan & R. Ranjana

  2. G H Raisoni College of Engineering, Nagpur, India

    Vedant Bahel

  3. Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, India

    T. Subha (Assistant Professor)

Authors
  1. K. V. Karan
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  2. Vedant Bahel
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  3. R. Ranjana
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  4. T. Subha
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Corresponding author

Correspondence to K. V. Karan .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India

    Satyabrata Roy

  2. Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India

    Deepak Sinwar

  3. Department of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia

    Thinagaran Perumal

  4. Department of Computer Engineering, Koszalin University of Technology, Koszalin, Poland

    Adam Slowik

  5. Departamento de Engenharia Mecânica Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    JoĂŁo Manuel R. S. Tavares

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Karan, K.V., Bahel, V., Ranjana, R., Subha, T. (2022). Transfer Learning Approach for Analyzing Attentiveness of Students in an Online Classroom Environment with Emotion Detection. In: Roy, S., Sinwar, D., Perumal, T., Slowik, A., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision . Advances in Intelligent Systems and Computing, vol 1424. Springer, Singapore. https://doi.org/10.1007/978-981-19-0475-2_23

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