2571-1075ISSN (Online)
Volume 9 , Issue 2 , PP: 28-36, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Senthil Kumar R. 1 * , T. Ramesh 2 , K. R. N. Aswini 3
Doi: https://doi.org/10.54216/IJBES.090204
This paper presents an adaptive optimization algorithm for personalized learning pathways in e-learning environments. The proposed algorithm dynamically adjusts the learning path for each student based on their performance, preferences, and learning behavior. By integrating machine learning techniques with a rule-based system, the algorithm optimizes content delivery and ensures a tailored learning experience that aligns with individual needs. The system continuously monitors learners’ progress, adapts to their evolving knowledge levels, and suggests the most relevant resources and activities to enhance engagement and comprehension. Experimental results demonstrate significant improvements in learning outcomes, reduced time to completion, and enhanced user satisfaction, making the approach a promising solution for personalized e-learning systems.
Reinforcement Learning for Personalized Instruction , Learner Engagement Enhancement , Dynamic Adaptation of Learning Content , Predictive Analytics in Digital Education , E-Learning Platforms , Intelligent E-Learning Systems
[1] Vanitha, V., Krishnan, P., & Elakkiya, R. (2019). Collaborative optimization algorithm for learning path construction in E-learning. Computers & Electrical Engineering, 77, 325-338.
[2] Amin, S., Uddin, M. I., Alarood, A. A., Mashwani, W. K., Alzahrani, A., & Alzahrani, A. O. (2023). Smart E-learning framework for personalized adaptive learning and sequential path recommendations using reinforcement learning. IEEE Access.
[3] Zheng, Y., Wang, D., Zhang, J., Li, Y., Xu, Y., Zhao, Y., & Zheng, Y. (2024). A unified framework for personalized learning pathway recommendation in e-learning contexts. Education and Information Technologies, 1-38.
[4] Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023). Adaptive learning using artificial intelligence in e-learning: a literature review. Education Sciences, 13(12), 1216.
[5] Pushpa, M. (2012). ACO in e-Learning: Towards an adaptive learning path. International Journal on Computer Science and Engineering, 4(3), 458.
[6] Essa, S. G., Celik, T., & Human-Hendricks, N. E. (2023). Personalized adaptive learning technologies based on machine learning techniques to identify learning styles: A systematic literature review. IEEE Access, 11, 48392-48409.
[7] Chandra Sekar P. and et.al (2023) “Firefly Optimized Resource Control and Routing Stability in MANET”, Engineering Proceedings, Volume-59,Issue-1, https://www.mdpi.com/2673-4591/59/1/18
[8] Chandra Sekar P. and et.al(2022) “An Energy Efficient Architecture for Furnace Monitor and Control in Foundry Based on Industry 4.0 Using IoT”, Scientific Computing for Internet of Health Informatics Things ,Volume 2022 ,Article ID 1128717 , https://doi.org/10.1155/2022/1128717
[9] Chandra Sekar, P &Mangalam, H (2018), ‘Third generation memetic optimization technique for energy efficient routing stability and load balancing in MANET’, Cluster Computing The Journal of Networks, Software Tools and Applications Published in online and DOI number is https://doi.org/10.1007/s10586-017-1524- x,ISSN1573-7543.(IF: 2.040).
[10] Gobi, N., Rathinavelu, A. Analyzing cloud based reviews for product ranking using feature based clustering algorithm. Cluster Comput 22 (Suppl 3), 6977–6984 (2019). https://doi.org/10.1007/s10586-018-1996-3
[11] Bhatnagar, G., Gobi, N., Aqeel, H., & Solanki, B. S. (2023). Sparrow-based Differential Evolutionary Search Algorithm for Mobility Aware Energy Efficient Clustering in MANET Network. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 135-142.
[12] Kalpana, A.V.,Chandrasekar, T.,Ramalingam, R.Ramesh, T.,Chitra, M. Enhancing healthcare integration with IoT for seamless and responsive patient care Future of AI in Biomedicine and Biotechnology, 2024, pp. 147–18
[13] Rakesh, K.; Krishna Teja, B.; Venkata Akhil, M.; Ramesh, T. Identity-Based Data Outsourcing with Comprehensive Auditing in Cloud-Based Healthcare Applications Lecture Notes in Networks and Systems ,2021.
[14] Tam, V., Lam, E. Y., & Fung, S. T. (2014). A new framework of concept clustering and learning path optimization to develop the next-generation e-learning systems. journal of computers in education, 1, 335-352.
[15] Smaili, E. M., Khoudda, C., Sraidi, S., Azzouzi, S., & Charaf, M. E. H. (2022). An innovative approach to prevent learners’ dropout from moocs using optimal personalized learning paths: an online learning case study. Statistics, Optimization & Information Computing, 10(1), 45-58.
