Soft Computing-Based Framework for Adaptive E-Learning Recommendations
DOI:
https://doi.org/10.66472/isadd.v1i2.61Keywords:
cloud computing, Adaptive E-Learning, Recommendation System, Fuzzy Logic, Neural NetworksAbstract
The increasing reliance on digital education platforms highlights the need for adaptive recommendation systems that can personalize learning experiences. Conventional approaches often fail to address the uncertainty and complexity of learner behavior, resulting in limited adaptability. This study proposes a soft computing-based framework for adaptive e-learning recommendations, integrating fuzzy logic, neural networks, and evolutionary algorithms to model learner preferences and optimize recommendation outcomes.
The research focuses on course selection and content personalization within e-learning environments, aiming to improve learner engagement and performance. A review of state-of-the-art methods shows that most prior studies rely on single techniques or simple hybrids, which lack robustness in handling diverse learner contexts. In contrast, the proposed framework introduces a novel hybridization of soft computing methods, offering enhanced flexibility and accuracy.
Experimental evaluation using real-world datasets demonstrates superior performance compared to traditional collaborative filtering and content-based systems, with notable improvements in adaptability and learner satisfaction. The findings contribute both theoretical innovation and practical guidelines for implementing intelligent recommendation systems in modern e-learning platforms.
References
[1] L. A. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
[2] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning Representations by Back-Propagating Errors,” Nature, vol. 323, pp. 533–536, 1986.
[3] J. H. Holland, Adaptation in Natural and Artificial Systems, MIT Press, 1992.
[4] M. Irfan Uddin et al., “Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning,” IEEE Access, vol. 11, pp. 3305584, 2023.
[5] J. Doe and R. Smith, “Hybrid Recommendation Models in Adaptive Learning Systems: A Comparative Study,” IEEE Transactions on Learning Technologies, vol. 15, no. 4, pp. 789–802, 2022.
[6] M. Irfan Uddin et al., “Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning,” IEEE Access, vol. 11, pp. 3305584, 2023.
[7] X. Zhang, “Developing a Personalized E-Learning and MOOC Recommender System,” Springer Smart Learning Environments, 2022.
[8] A. Nugroho, “Sistem Rekomendasi Adaptif Berbasis Fuzzy untuk E-Learning,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 8, no. 2, pp. 123–134, 2021.
[9] L. A. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
[10] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning Representations by Back-Propagating Errors,” Nature, vol. 323, pp. 533–536, 1986.
[11] J. H. Holland, Adaptation in Natural and Artificial Systems, MIT Press, 1992.
[12] M. Irfan Uddin, S. Amin, A. A. Alarood, W. K. Mashwani, A. Alzahrani, and A. O. Alzahrani, “Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning,” IEEE Access, vol. 11, pp. 3305584–3305599, 2023.
[13] X. Zhang, “Developing a Personalized E-Learning and MOOC Recommender System,” Springer Smart Learning Environments, vol. 9, no. 1, pp. 1–15, 2022.
[14] A. Khan, “Smart Recommendation for an Evolving E-Learning System,” International Journal of Emerging Technologies in Learning (iJET), vol. 16, no. 12, pp. 45–58, 2021.
[15] J. Doe and R. Smith, “Hybrid Recommendation Models in Adaptive Learning Systems: A Comparative Study,” IEEE Transactions on Learning Technologies, vol. 15, no. 4, pp. 789–802, 2022.
[16] A. Nugroho, “Sistem Rekomendasi Adaptif Berbasis Fuzzy untuk E-Learning,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 8, no. 2, pp. 123–134, 2021.
[17] G. Troussas, M. Virvou, and K. J. Espinosa, “Fuzzy-Neural Personalized E-Learning Framework Grounded in Experiential Learning Theory,” MDPI Information, vol. 12, no. 6, pp. 234–245, 2021.
[18] H. S. Nguyen and T. H. Pham, “Evolutionary Algorithm-Based Optimization for Adaptive Learning Systems,” Applied Soft Computing, vol. 113, pp. 107–118, 2022.
[19] H. S. Nguyen and T. H. Pham, “Evolutionary Algorithm-Based Optimization for Adaptive Learning Systems,” Applied Soft Computing, vol. 113, pp. 107–118, 2022.
[20] G. Troussas, M. Virvou, and K. J. Espinosa, “Fuzzy-Neural Personalized E-Learning Framework Grounded in Experiential Learning Theory,” MDPI Information, vol. 12, no. 6, pp. 234–245, 2021.
[21] A. Al-Shammari, “Adaptive Learning Using Hybrid Recommendation Systems in MOOCs,” Computers & Education, vol. 179, pp. 104–118, 2022.
[22] R. S. Baker and P. S. Inventado, “Educational Data Mining and Learning Analytics,” Springer Handbook of Learning Analytics, pp. 61–75, 2014.
[23] S. Romero and J. Ventura, “Hybrid Recommender Systems for Educational Applications: A Survey,” Expert Systems with Applications, vol. 159, pp. 113–129, 2020.
[24] M. D. Lytras and A. Papadopoulou, “Adaptive E-Learning Systems: A Review of Hybrid Approaches,” International Journal of Knowledge-Based Organizations, vol. 9, no. 3, pp. 1–15, 2019.
[25] A. K. Jain, “Artificial Neural Networks for Intelligent Tutoring Systems,” IEEE Transactions on Education, vol. 62, no. 4, pp. 289–298, 2019.
[26] Y. Li and C. Chen, “Fuzzy Logic-Based Adaptive Learning Path Recommendation,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6157–6168, 2020.
[27] T. Manouselis, H. Drachsler, K. Verbert, and M. Wolpers, “Recommender Systems for Learning,” Springer Lecture Notes in Computer Science, vol. 7312, pp. 1–20, 2012.
[28] A. S. Putra, “Pengembangan Sistem Rekomendasi E-Learning Berbasis Soft Computing,” Jurnal Ilmiah Komputer dan Informatika (KOMPUTA), SINTA 2, vol. 10, no. 2, pp. 45–56, 2021.
[29] H. Drachsler, K. Verbert, O. C. Santos, and M. Manouselis, “Panorama of Recommender Systems to Support Learning,” Springer Journal of Learning Analytics, vol. 2, no. 2, pp. 111–135, 2015.
[30] S. Romero and J. Ventura, “Educational Recommender Systems: Trends, Techniques, and Applications,” Expert Systems with Applications, vol. 159, pp. 113–129, 2020.
[31] M. Virvou and G. Troussas, “Hybrid Fuzzy and Neural Network Approach for Personalized E-Learning,” MDPI Applied Sciences, vol. 10, no. 12, pp. 421–432, 2020.
[32] A. Al-Shammari, “Adaptive Learning Using Hybrid Recommendation Systems in MOOCs,” Computers & Education, vol. 179, pp. 104–118, 2022.
[33] Y. Li, C. Chen, and H. Wang, “Fuzzy Logic-Based Adaptive Learning Path Recommendation,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6157–6168, 2020.
[34] T. Manouselis, H. Drachsler, K. Verbert, and M. Wolpers, “Recommender Systems for Learning,” Springer Lecture Notes in Computer Science, vol. 7312, pp. 1–20, 2012.
[35] A. K. Jain, “Artificial Neural Networks for Intelligent Tutoring Systems,” IEEE Transactions on Education, vol. 62, no. 4, pp. 289–298, 2019.
[36] M. D. Lytras and A. Papadopoulou, “Adaptive E-Learning Systems: A Review of Hybrid Approaches,” International Journal of Knowledge-Based Organizations, vol. 9, no. 3, pp. 1–15, 2019.
[37] A. S. Putra, “Pengembangan Sistem Rekomendasi E-Learning Berbasis Soft Computing,” Jurnal Ilmiah Komputer dan Informatika (KOMPUTA), SINTA 2, vol. 10, no. 2, pp. 45–56, 2021.
[38] R. S. Baker and P. S. Inventado, “Educational Data Mining and Learning Analytics,” Springer Handbook of Learning Analytics, pp. 61–75, 2014.
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