AI-based Student Engagement Detection: Leveraging Convolution Neural Network Models for Classroom Analytics
Keywords:
Student Engagement, Deep Learning, Convolutional Neural Networks (CNN), MobileNet, Xception, NASNetMobile.”Abstract
In modern educational environments, accurately detecting and quantifying student engagement is essential for optimizing learning outcomes. This study explores deep learning-based approaches to analyze student engagement using convolutional neural networks (CNN) and advanced architectures, including MobileNet, Xception, NASNetMobile, and a hybrid Xception + NASNetMobile model. The dataset utilized for evaluation consists of labeled student engagement images, sourced from Kaggle. The analysis involves training and validating these models to assess their effectiveness in capturing engagement levels. Performance evaluation is conducted using accuracy, recall, precision, and F1-score metrics to determine classification accuracy and robustness. Experimental results indicate that deep learning architectures effectively distinguish varying engagement levels, with hybrid models demonstrating superior performance in feature extraction and classification. The integration of NASNetMobile and Xception further enhances the model’s ability to capture intricate facial and behavioral cues indicative of engagement. These findings highlight the potential of deep learning frameworks in developing intelligent, automated engagement detection systems, contributing to adaptive learning technologies and real-time student monitoring for enhanced educational experiences.
Downloads
References
Bhardwaj, P., Gupta, P. K., Panwar, H., Siddiqui, M. K., Morales-Menendez, R., & Bhaik, A. (2021). Application of deep learning on student engagement in e-learning environments. Computers & Electrical Engineering, 93, 107277.
Sharma, P., Joshi, S., Gautam, S., Maharjan, S., Khanal, S. R., Reis, M. C., ... & de Jesus Filipe, V. M. (2022, August). Student engagement detection using emotion analysis, eye tracking and head movement with machine learning. In International Conference on Technology and Innovation in Learning, Teaching and Education (pp. 52-68). Cham: Springer Nature Switzerland.
Pillai, A. S. (2022). Student Engagement Detection in Classrooms through Computer Vision and Deep Learning: A Novel Approach Using YOLOv4. Sage Science Review of Educational Technology, 5(1), 87-97.
Ahmed, Z. A., Jadhav, M. E., Al-madani, A. M., Tawfik, M., Alsubari, S. N., & Shareef, A. A. A. (2022). Real-Time Detection of Student Engagement: Deep Learning-Based System. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2021, Volume 1 (pp. 313-323). Springer Singapore.
Delgado, K., Origgi, J. M., Hasanpoor, T., Yu, H., Allessio, D., Arroyo, I., ... & Bargal, S. A. (2021). Student engagement dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3628-3636).
Uçar, M. U., & Özdemir, E. (2022). Recognizing students and detecting student engagement with real-time image processing. Electronics, 11(9), 1500.
Mahmood, N., Bhatti, S. M., Dawood, H., Pradhan, M. R., & Ahmad, H. (2024). Measuring Student Engagement through Behavioral and Emotional Features Using Deep-Learning Models. Algorithms, 17(10), 458.
Pabba, C., & Kumar, P. (2022). An intelligent system for monitoring students' engagement in large classroom teaching through facial expression recognition. Expert Systems, 39(1), e12839.
Mazumder, D., Chatterjee, A., Chakraborty, A., & Karmakar, R. (2024, June). A Novel Student Engagement Level Detection and Emotion Analysis Using Ensemble Learning. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
Santoni, M. M., Basaruddin, T., Junus, K., & Lawanto, O. (2024). Automatic detection of students’ engagement during online learning: a bagging ensemble deep learning approach. IEEE Access.
Hasnine, M. N., Bui, H. T., Tran, T. T. T., Nguyen, H. T., Akçapınar, G., & Ueda, H. (2021). Students’ emotion extraction and visualization for engagement detection in online learning. Procedia Computer Science, 192, 3423-3431.
Xie, N., Liu, Z., Li, Z., Pang, W., & Lu, B. (2023). Student engagement detection in online environment using computer vision and multi-dimensional feature fusion. Multimedia Systems, 29(6), 3559-3577.
Selim, T., Elkabani, I., & Abdou, M. A. (2022). Students engagement level detection in online e-learning using hybrid efficientnetb7 together with tcn, lstm, and bi-lstm. IEEE Access, 10, 99573-99583.
Mandia, S., Mitharwal, R., & Singh, K. (2024). Automatic student engagement measurement using machine learning techniques: A literature study of data and methods. Multimedia Tools and Applications, 83(16), 49641-49672.
Ahmad, N., Khan, Z., & Singh, D. (2023, March). Student engagement prediction in moocs using deep learning. In 2023 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 1-6). IEEE.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
