پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 31 |
| تعداد شمارهها | 834 |
| تعداد مقالات | 8,015 |
| تعداد مشاهده مقاله | 14,852,484 |
| تعداد دریافت فایل اصل مقاله | 9,586,508 |
Novel Enhanced Cognitive State Analysis in E-Learning via Real-Time Emotion and Attentiveness Detection Using OptFuzzy TSM and ABiLSTM | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 22، شماره 4، مهر و آبان 2025، صفحه 57-75 اصل مقاله (2.45 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2025.49950.8829 | ||
| نویسندگان | ||
| Jignesh Vaniya* 1؛ Maleyka Alizada2؛ Pooja Nagpal3؛ Biplab Kumar Dey4؛ Dr. Gulara Alesger Abbbasova5 | ||
| 1Department of Information Technology, Vishwakaram Government Engineering College, Chandkheda, Ahmedabad, Gujarat 382424, India | ||
| 2Department of History, Western Caspian University, Azerbaijan, Urban | ||
| 3CMS Business School, Jain (Deemed to be University), Bengaluru, India | ||
| 4Department of Commerce, Shaheed Bhagat Singh College, University of Delhi, India | ||
| 5Department of Social Sciences, Azerbaijan University of Architecture and Construction, Urban | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| E-learning؛ cognitive state؛ dual Mamdani and neuro-fuzzy inference system؛ Archerfish Hunting Optimization Algorithm (AHOA)؛ Improved Position Enhancement Faster Network (IPEFNet) | ||
| مراجع | ||
|
[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 | ||
|
آمار تعداد مشاهده مقاله: 400 تعداد دریافت فایل اصل مقاله: 249 |
||