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Stream/Area:
-
Subject:
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Paper ID:
IJSREM67123
Volume:
10 |
9 |
Sep 2026
Pages:
1-11
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
DEPARTMENT OF COMPUTER SCIENCE WITH DATA ANALYTICS
DR. NGP ARTS AND SCIENCE COLLEGE
DEPARTMENT OF COMPUTER SCIENCE
DR. NGP ARTS AND SCIENCE COLLEGE
Scholar, Master of Computing,
Curtin University, Western Australia
Inclusive e-learning platforms are increasingly adopted to accommodate diverse learners by providing flexible, accessible and personalized learning environments. However, understanding and predicting learner adoption remains a challenge due to the heterogeneity of learner behaviors, emotional feedback and interaction patterns [1], [2]. Traditional machine learning models often fail to capture complex feature representations, while end-to-end deep learning classifiers may suffer from overfitting when applied to limited educational datasets. This paper proposes a hybrid classification framework that integrates a Convolutional Neural Network (CNN) for feature extraction with a Support Vector Machine (SVM) for learner adoption classification. The CNN learns high-level representations from pedagogical, technological and sentiment-based learner interaction data, while the SVM performs robust classification using a maximum-margin decision strategy [8], [9]. Experimental evaluation conducted on a contextual e-learning learner interaction dataset demonstrates that the proposed CNN–SVM model outperforms conventional CNN and classical machine learning classifiers in terms of precision, recall and accuracy. The results indicate that decoupling feature learning and classification improves generalization and provides a reliable early-stage solution for learner adoption prediction in inclusive e-learning environments.
KEYWORDS
This paper presented a CNN–SVM hybrid framework for learner adoption classification in inclusive e-learning systems. By leveraging CNN-based feature extraction and SVM-based classification, the proposed approach effectively models complex learner interaction data while maintaining robust generalization. Experimental evaluation demonstrated improved performance over conventional deep learning and machine learning models. Future work will focus on incorporating temporal modeling techniques and attention mechanisms to capture evolving learner engagement patterns and further enhance adoption prediction accuracy.
[1] R. S. Baker and P. S. Inventado, “Educational data mining and learning analytics,” in Learning Analytics: From Research to Practice, J. A. Larusson and B. White, Eds. Berlin, Germany: Springer, 2014, pp. 61–75, doi: 10.1007/978-1-4614-3305-7_4. [2] C. Romero and S. Ventura, “Educational data mining: A review of the state of the art,” IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 6, pp. 601–618, Nov. 2010, doi: 10.1109/TSMCC.2010.2053532. [3] O. Zawacki-Richter, V. I. Marín, M. Bond and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education,” International Journal of Educational Technology in Higher Education, vol. 16, no. 1, pp. 1–27, 2019, doi: 10.1186/s41239-019-0171-0. [4] S. B. Kotsiantis, I. D. Zaharakis and P. E. Pintelas, “Machine learning: A review of classification and combining techniques,” Artificial Intelligence Review, vol. 26, no. 3, pp. 159–190, 2006, doi: 10.1007/s10462-007-9052-3. [5] Y. LeCun, Y. Bengio and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539. [6] I. Goodfellow, Y. Bengio and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [7] X. Zhang, J. Zhao and Y. LeCun, “Character-level convolutional networks for text classification,” in Advances in Neural Information Processing Systems (NeurIPS), 2015, pp. 649–657. [8] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995, doi: 10.1007/BF00994018. [9] V. N. Vapnik, Statistical Learning Theory. New York, NY, USA: Wiley, 1998. [10] Z. Liu, J. Tang, Y. Han, M. Chen and J. Wang, “A hybrid CNN–SVM approach for text classification,” Knowledge-Based Systems, vol. 163, pp. 852–861, Jan. 2019, doi: 10.1016/j.knosys.2018.10.015.
Publication Date
9/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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