Novel Enhanced Cognitive State Analysis in E-Learning via Real-Time Emotion and Attentiveness Detection Using OptFuzzy TSM and ABiLSTM

Document Type : Research Paper

Authors

1 Department of Information Technology, Vishwakaram Government Engineering College, Chandkheda, Ahmedabad, Gujarat 382424, India

2 Department of History, Western Caspian University, Azerbaijan, Urban

3 CMS Business School, Jain (Deemed to be University), Bengaluru, India

4 Department of Commerce, Shaheed Bhagat Singh College, University of Delhi, India

5 Department of Social Sciences, Azerbaijan University of Architecture and Construction, Urban

Abstract

The emotional state of an online learner has drawn a lot of attention. Accurately predicting a student's emotional state can improve learning outcomes through designated mediation. Still, keeping an eye on and sustaining students attention in online classes is challenging because there isn't any immediate supervision. To identify these challenges based on the learner's emotional states, this paper presents a novel, efficient, Optimized Fuzzy approach and signifies solutions to inspire the learner. The Improved Multi-Task Cascaded Convolutional Networks (IMTCNN) are used to identify the face region in real time. Different emotions are classified by analyzing extracted facial expressions using an Optimized Takagi-Sugeno and Mamdani fuzzy systems (Fuzzy TSM) approach. With the Enhanced Mother Optimization Algorithm (EMO), the hyperparameters in the classification approach are optimized. The proposed method determines whether learners are attentive or inattentive during online learning sessions by computing an Attention-based bi-directional long-short term memory (ABiLSTM) to predict cognitive states. To improve learning efficiency and productivity, users receive real-time feedback. The proposed approach can give instructors ongoing feedback, allowing them to modify the way they teach and keep students interested and engaged. With recognition rates of over 98.21% accuracy on the proposed datasets, the study's results are encouraging and outperforming those of other approaches.

Keywords

Main Subjects

References

 [1] S. Ahmad, et al., Multi-clustered mathematical model for student cognitive skills prediction optimization, IEEE
 Access, 11 (2023), 65371-65381. https://doi.org/10.1109/ACCESS.2023.3285612
 [2] M. Arefi, Geometric clustering fuzzy regression based on c-means clustering, Iranian Journal of Fuzzy Systems, 22(3)
 (2025), 87-101. https://doi.org/10.22111/ijfs.2025.51386.9078
 [3] E. Z. Z. A. I. M. Aymane, D. A. H. B. I. Aziz, H. A. I. D. I. N. E. Abdelfatteh, A. Q. Q. A. L. Abdelhak, Enabling
 sustainable learning: A machine learning approach for an eco-friendly multi-factor adaptive E-learning system,
 Procedia Computer Science, 236 (2024), 533-540. https://doi.org/10.1016/j.procs.2024.05.063
 [4] K. Benabbes, et al., A new hybrid approach to detect and track learner’s engagement in e-learning, IEEE Access,
 11(8) (2023), 70912-70929. https://doi.org/10.1109/ACCESS.2023.3293827
 [5] S. Gupta, et al., A multimodal facial cues based engagement detection system in e-learning context using deep
 learning approach, Multimedia Tools and Applications, 82(18) (2023), 28589-28615. https://doi.org/10.1007/
 s11042-023-14392-3
 [6] C. Q. Huang, et al., XKT: Towards explainable knowledge tracing model with cognitive learning theories for questions
 of multiple knowledge concepts, IEEE Transactions on Knowledge and Data Engineering, 15(7) (2024), 7308-7325.
 https://doi.org/10.1109/TKDE.2024.3418098
 [7] M. N. Kouahla, et al., Emorec: A new approach for detecting and improving the emotional state of learners in an
 e-learning environment, Interactive Learning Environments, 31(10) (2023), 6223-6241. https://doi.org/10.1080/
 10494820.2022.2029494
 [8] R. Kukkar, et al., Prediction of student academic performance based on their emotional wellbeing and interaction
 on various e-learning platforms, Education and Information Technologies, 28(8) (2023), 9655-9684. https://doi.
 org/10.1007/s10639-022-11573-9
 [9] V. T. Lokare, P. M. Jadhav, An AI-based learning style prediction model for personalized and effective learning,
 Thinking Skills and Creativity, 51 (2024), 101421. https://doi.org/10.1016/j.tsc.2023.101421
 [10] H. Ma, et al., Predicting student performance in future exams via neutrosophic cognitive diagnosis in personalized
 e-learning environment, IEEE Transactions on Learning Technologies, 16(5) (2023), 680-693. https://doi.org/
 10.1109/TLT.2023.3240931
 [11] H. Ma, et al., Predicting examinee performance based on a fuzzy cloud cognitive diagnosis framework in e-learning
 environment, Soft Computing, 27(24) (2023), 18949-18969. https://doi.org/10.1007/s00500-023-08100-4
 [12] M. R. Mastani Shabestari, N. Mikaeilvand, A novel approach to time-fractional equations using fuzzy beta Laplace
 transform iterative technique and its applications in fluid dynamics, Iranian Journal of Fuzzy Systems, 22(3) (2025),
 1-19. https://doi.org/10.22111/ijfs.2025.50308.8872
 [13] R. Mustapha, G. Soukaina, Q. Mohammed, A. Es-Sˆaadia, Towards an adaptive e-learning system based on deep
 learner profile, machine learning approach, and reinforcement learning, International Journal of Advanced Computer
 Science and Applications, 14(5) (2023), 1-7. https://doi.org/10.1207/s15430421tip4104_2
 [14] N. G. Praveena, S. S Nath, A fuzzy based efficient and blockchain oriented secured routing in vehicular Ad-Hoc
 networks, Iranian Journal of Fuzzy Systems, 21(6) (2024), 15-31. https://doi.org/10.22111/ijfs.2024.48069.
 8461
 [15] H. S. Raghavendra, et al., Student education analysis of e-learning during COVID-19 using support regression ran
dom forest algorithm, SN Computer Science, 5(6) (2024), 727. https://doi.org/10.1007/s42979-024-03065-z
 [16] S. Rathi, et al., Affective state prediction of E-learner using SS-ROA based deep LSTM, Array, 19 (2023), 100315.
 https://doi.org/10.1016/j.array.2023.100315
 [17] G. Shi, et al., One for all: A unified generative framework for image emotion classification, IEEE Transactions
 on Circuits and Systems for Video Technology, 34(8) (2023), 7057-7068. https://doi.org/10.1109/TCSVT.2023.
 3341840
 [18] R. Wang, L. Chen, A. Ayesh, Multimodal motivation modelling and computing towards motivationally intelligent
 E-learning systems, CCF Transactions on Pervasive Computing and Interactions, 5(1) (2023), 64-81. https://doi.
 org/10.1007/s42486-022-00107-4
 [19] C. Zuo, et al., GUGEN: Global user graph enhanced network for next POI recommendation, IEEE Transactions on
 Mobile Computing, 8(9) (2024), 14975-14986. https://doi.org/10.1109/TMC.2024.3455107
Iranian Journal of Fuzzy Systems
Volume 22, Issue 4
July and August 2025
Pages 57-75

Files

Share

How to cite

Statistics