An Affective Computing Model for Online Tutoring using Facial Expressions

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 8

Year of Publication : 2023

Authors : K. Revathi, T. Tamilselvi, R. Saravanakumar, T. Divya

10.14445/23488549/IJECE-V10I8P101

How to Cite?

K. Revathi, T. Tamilselvi, R. Saravanakumar, T. Divya, "An Affective Computing Model for Online Tutoring using Facial Expressions," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 8, pp. 1-8, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I8P101

Abstract:

Online tutoring is becoming increasingly popular as a way to get extra help outside of the classroom. It can help students close knowledge gaps in core topics and improve their grades by giving them a convenient, personalized and safe resource they can use when they need extra academic help. Online learning is not new, but throughout the pandemic, the shift from conventional to online educational institutions forced changes to rules, techniques, apps, and infrastructures to accommodate the new learning culture. Examining the possibilities of online learning has become more critical due to COVID-19. With advanced technology, it is possible to bring effective online tutoring modules. Assessing the coordination of students engaging in the process is a difficult task. Using facial expressions, one can grasp their mode in the interaction. Thus affective computing plays a vital role in designing online tutoring. In this paper, a deep learning model for examining students' mode is experimented with and evaluated against popular practice models. The experimental results provide better performance as compared to other models of interest.

Keywords:

Affective computing, Convolutional Neural Network (CNN), Deep learning, Facial expressions, Online tutoring.

References:

[1] Michelle Velasquez Bean et al., “Effective Practices for Online Tutoring,” The Academic Senate for California Community Colleges, pp. 1-23, 2019.
[Publisher link]
[2] Tom Hamilton Stubber, What are the benefits of Online Tutoring?, 2023. [Online]. Available: https://tutorcruncher.com/tutoring-online/what-are-the-benefits-of-online-tutoring/
[3] Online Schools, The Many Benefits of Online Tutoring, 2023. [Online]. Available: https://www.onlineschools.org/online-tutoring/
[4] Khanh Nguyen Phuong Tran et al., “What are Tutors’ Perceptions of an Online Tutoring Project-Digital Learning Companion-During the COVID-19 Pandemic? A Case Study in Taiwan,” Universal Access in the Information Society, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[5] Lih-Juan Chan Lin, Hong-Yen Lin, and Tze-Han Lu, “Online Tutoring Service for Enriching College Students’ Experience in Community Engagement,” Procedia - Social and Behavioral Sciences, vol. 191, pp. 1676-1681, 2015.
[CrossRef] [Google Scholar] [Publisher link]
[6] David Hardt, Markus Nagler, and Johannes Rincke, “Tutoring in (Online) Higher Education: Experimental Evidence,” Economics of Education Review, vol. 92, p. 102350, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[7] Junhao Zeng et al., “Application of Deep Self-Attention in Knowledge Tracing,” Arxiv, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[8] Xia Sun et al., “Dynamic Key-Value Memory Networks with Rich Features for Knowledge Tracing,” IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 8239-8245, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[9] Sherry Ruan, Wei Wei, and James Landay, “Variational Deep Knowledge Tracing for Language Learning,” LAK’21: 11th International Learning Analytics and Knowledge Conference, Irvine, CA, USA, pp. 323-332, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[10] Shalini Pandey, and George Karypis, “A Self - Attentive Model for Knowledge Tracing,” arXiv:1907.06837, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[11] Sun Duo, and Lu Xue Song, “An E-Learning System Based on Affective Computing,” Physics Procedia, vol. 24, pp. 1893-1898, 2012.
[CrossRef] [Google Scholar] [Publisher link]
[12] Chih-Hung Wu, Yueh-Min Huang, and Jan-Pan Hwang, “Review of Affective Computing in Education/Learning: Trends and Challenges,” British Journal of Educational Technology, vol. 47, no. 6, pp. 1304-1323, 2016.
[CrossRef] [Google Scholar] [Publisher link]
[13] Paweł Tarnowski et al., “Emotion Recognition using Facial Expressions,” Procedia Computer Science, vol. 108, pp.1175-1184, 2017.
[CrossRef] [Google Scholar] [Publisher link]
[14] Pooja G. Nair, and Sneha R, “A Review: Facial Recognition using Machine Learning,” International Journal of Recent Engineering Science, vol. 7, no. 3, pp. 85-89, 2020.
[CrossRef] [Google Scholar] [Publisher link]
[15] Cheng-Hung Wang, and Hao-Chiang Koong Lin, “Emotional Design Tutoring System Based on Multimodal Affective Computing Techniques,” International Journal of Distance Education Technologies, vol. 16, no. 1, pp. 103-117, 2018.
[CrossRef] [Google Scholar] [Publisher link]
[16] María Dolores Lozano et al., “Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications,” Mobile Information Systems, vol. 2018, pp. 1-10, 2018.
[CrossRef] [Google Scholar] [Publisher link]
[17] R. S. Smith, and T. Windeatt, “Facial Action Unit Recognition using Multi-Class Classification,” Neurocomputing, vol. 150, pp. 440- 448, 2015.
[CrossRef] [Google Scholar] [Publisher link]
[18] D. Y. Liliana, “Emotion Recognition from Facial Expression using Deep Convolutional Neural Network,” Journal of Physics: Conference Series, 2018 International Conference of Computer and Informatics Engineering, Bogor, Indonesia, vol. 1193, pp. 1-5, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[19] Joyassree Sen et al., “Face Recognition using Deep Convolutional Network and One-Shot Learning,” SSRG International Journal of Computer Science and Engineering, vol. 7, no. 4, pp. 23-29, 2020.
[CrossRef] [Google Scholar] [Publisher link]
[20] Elaheh Yadegaridehkordi et al., “Affective Computing in Education: A Systematic Review and Future Research,” Computers & Education, vol. 142, p. 103649, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[21] Varun Bajaj, and Ram Bilas Pachori, “Detection of Human Emotions using Features Based on the Multiwavelet Transform of EEG Signals,” Brain Computer Interfaces, vol. 74, pp. 215-240, 2015.
[CrossRef] [Google Scholar] [Publisher link]
[22] Ujjwal Sharma, and Braj Bhushan, “Development and Validation of Indian Affective Picture Database,” International Journal of Psychology, vol. 54, no. 4, pp. 462-467, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[23] S. S. Poorna et al., “Facial Emotion Recognition using DWT Based Similarity and Difference features,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, pp. 524-527, 2018.
[CrossRef] [Google Scholar] [Publisher link]
[24] Maruti Vijayshankar Mishra, Sonia Baloni Ray, and Narayanan Srinivasan, “Cross-Cultural Emotion Recognition and Evaluation of Radboud Faces Database with an Indian Sample,” PLoS ONE, vol. 13, no. 10, pp. 1-12, 2018.
[CrossRef] [Google Scholar] [Publisher link]
[25] Anvar Sadath, Ram Kumar, and Magnus Karlsson, “Expressed Emotion Research in India: A Narrative Review,”Indian Journal of Psychological Medicine, vol. 41, no. 1, pp. 18-26, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[26] R. Naleiny, and R. V. P. Eswaramurthy, “A Novel Facial Appearance Appreciation with Pooled Color for Adaptive Skin Color Extraction,” International Journal of Computer & Organization Trends (IJCOT), vol. 6, no. 5, pp. 11-18, 2016.
[Publisher link]
[27] Opeyemi Oyelesi, and Akingbade Kayode Francis, “Face Recognition for Access Control using PCA Algorithm,” SSRG International Journal of VLSI & Signal Processing, vol. 4, no. 2, pp. 22-27, 2017.
[CrossRef] [Publisher link]
[28] Santhosh Shivaprakash, and Sannangi Viswaradhya Rajashekararadhya, “ACLSDN: A Heuristic Face Recognition Framework with Adaptive Cascaded Deep Learning using Spectral Feature Selection,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 3, pp. 73-93, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[29] Thetechwriters, Facial Emotion Detection using CNN, 2021. [Online]. Available: https://www.analyticsvidhya.com/blog/2021/11/facial-emotion-detection-using-cnn/