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Detection of Student Engagement in E-Learning Environments Using EfficientnetV2-L Together with RNN-Based Models

Farhad Mortezapour Shiri1,*, Ehsan Ahmadi2, Mohammadreza Rezaee1, Thinagaran Perumal1

1 Faculty of Computer Science and Information Technology, University Putra Malaysia (UPM), Serdang, Malaysia
2 Department of Electrical and Computer Engineering, University of Wisconsin, Madison, USA

* Corresponding Author: Farhad Mortezapour Shiri. Email: email

Journal on Artificial Intelligence 2024, 6, 85-103. https://doi.org/10.32604/jai.2024.048911

Abstract

Automatic detection of student engagement levels from videos, which is a spatio-temporal classification problem is crucial for enhancing the quality of online education. This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos. The evaluation of these models utilizes the DAiSEE dataset, a public repository capturing student affective states in e-learning scenarios. The initial model integrates EfficientNetV2-L with Gated Recurrent Unit (GRU) and attains an accuracy of 61.45%. Subsequently, the second model combines EfficientNetV2-L with bidirectional GRU (Bi-GRU), yielding an accuracy of 61.56%. The third and fourth models leverage a fusion of EfficientNetV2-L with Long Short-Term Memory (LSTM) and bidirectional LSTM (Bi-LSTM), achieving accuracies of 62.11% and 61.67%, respectively. Our findings demonstrate the viability of these models in effectively discerning student engagement levels, with the EfficientNetV2-L+LSTM model emerging as the most proficient, reaching an accuracy of 62.11%. This study underscores the potential of hybrid spatio-temporal networks in automating the detection of student engagement, thereby contributing to advancements in online education quality.

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APA Style
Shiri, F.M., Ahmadi, E., Rezaee, M., Perumal, T. (2024). Detection of student engagement in e-learning environments using efficientnetv2-l together with rnn-based models. Journal on Artificial Intelligence, 6(1), 85-103. https://doi.org/10.32604/jai.2024.048911
Vancouver Style
Shiri FM, Ahmadi E, Rezaee M, Perumal T. Detection of student engagement in e-learning environments using efficientnetv2-l together with rnn-based models. J Artif Intell . 2024;6(1):85-103 https://doi.org/10.32604/jai.2024.048911
IEEE Style
F.M. Shiri, E. Ahmadi, M. Rezaee, and T. Perumal "Detection of Student Engagement in E-Learning Environments Using EfficientnetV2-L Together with RNN-Based Models," J. Artif. Intell. , vol. 6, no. 1, pp. 85-103. 2024. https://doi.org/10.32604/jai.2024.048911



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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