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DOI:http://dx.doi.org/10.26855/er.2022.05.003

Analysis of Recent Trends in E-Learning Personalization Techniques

Date: May 27,2022 |Hits: 1011 Download PDF How to cite this paper

Raja Marappan*, S. Bhaskaran

School of Computing, SASTRA Deemed University, Thirumalaisamudram, Thanjavur, India.

*Corresponding author: Raja Marappan

Abstract

Customized e-learning dependent on a recommender framework is perceived as the most fascinating exploration field in schooling and education in this last decade, since the learning style is explicit for every learner. Indeed, from the information on their learning style, it is simpler to suggest a teaching technique works around a collection of the most satisfactory learning objects to give a superior profit from the instructive level. This research concentrates on using various recommendation and data mining approaches for personalized learning in an e-learning environment. Personalized learning helps the learners to choose their right recommendations effectively at any point in time. This paper is focused to provide an in-depth analysis of the recent well-known personalization approaches using different soft computing strategies such as ontology-based approach, self‐organizing maps, association mining, Long Short-Term Memory (LSTM), content-based filtering, and AprioriAll algorithms. This research analyzes the personalization of the various learning preferences of the learners in the recommender systems for effective recommendation.

References

Bhaskaran, S., Marappan, R., Santhi, B. (2021). Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications. Mathematics, 2021, 9, 197. https://doi.org/10.3390/math9020197.

Bhaskaran, S., Marappan, R., Santhi, B. (2020). Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets. Mathematics, 2020, 8, 1106. https://doi.org/10.3390/math8071106.

Blazheska-Tabakovska, N., Ivanovic, M., Klasnja-Milicevic, A., and Ivkovic, J. (2017). Comparison of E-Learning Personalization Systems: Protus and PLeMSys. International Journal of Emerging Technologies in Learning (iJET), 12(1), 57-70.

Doja, M. N. (2020). An Improved Recommender System for E-Learning Environments to Enhance Learning Capabilities of Learners. In Proceedings of ICETIT 2019 (pp. 604-612). Springer, Cham.

Ghauth, K. I. and Abdullah, N. A. (2010). Learning materials recommendation using good learners’ ratings and content-based filtering. Educational technology research and development, 58(6), pp. 711-727.

Hasibuan, Z. A. (2017). Step-Function Approach for E-Learning Personalization. Telkomnika, 15(3).

Keefe, J. W. (1987). Learning Style: Theory and Practice. National Association of Secondary School Principals, Reston, VA., ISBN: 0-88210- 201-X, p. 53.

Klašnja-Milićević, A., Vesin, B., Ivanović, M., and Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & education, 56(3), 885-899.

Marappan, R., Sethumadhavan, G. (2021). Solving Graph Coloring Problem Using Divide and Conquer-Based Turbulent Particle Swarm Optimization. Arab J SciEng, (2021). https://doi.org/10.1007/s13369-021-06323-x.

Marappan, R., Sethumadhavan, G. (2020). Complexity Analysis and Stochastic Convergence of Some Well-known Evolutionary Operators for Solving Graph Coloring Problem. Mathematics, 2020, 8, 303. https://doi.org/10.3390/math8030303.

Marappan, R., Sethumadhavan, G. (2018). Solution to Graph Coloring Using Genetic and Tabu Search Procedures. Arab J SciEng, 43, 525-542 (2018). https://doi.org/10.1007/s13369-017-2686-9.

Nafea, S. M., Siewe, F., and He, Y. (2019, February). A novel algorithm for course learning object recommendation based on student learning styles. In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE) (pp. 192-201). IEEE.

Raja Marappan, Gopalakrishnan Sethumadhavan, R. K. Srihari. (2016). New approximation algorithms for solving graph coloring problem – An experimental approach, Perspectives in Science, Volume 8, 2016, Pages 384-387, ISSN 2213-0209, https://doi.org/10.1016/j.pisc.2016.04.083.

Raja Marappan, Gopalakrishnan Sethumadhavan, U. Harimoorthy. (2016). Solving channel allocation problem using new genetic operators – An experimental approach. Perspectives in Science, Volume 8, 2016, Pages 409-411, ISSN 2213-0209, https://doi.org/10.1016/j.pisc.2016.04.091.

Raja Marappan. (2021). A New Multi-Objective Optimization in Solving Graph Coloring and Wireless Networks Channels Allocation Problems. Int. J. Advanced Networking and Applications, Volume: 13 Issue: 02 Pages: 4891-4895 (2021).

Raja Marappan, S. Bhaskaran, N. Aakaash, S. MathuMitha. (2022). Analysis of COVID-19 Prediction Models: Design & Analysis of New Machine Learning Approach. Journal of Applied Mathematics and Computation, 6(1), 121-126. DOI: http://dx.doi.org/10.26855/jamc.2022.03.013.

Raja Marappan, S. Bhaskaran, S. Ashwadh, H. Aathi Raj. (2022). Extraction of Drug Review Polarity Using Sentimental Analysis. Journal of Applied Mathematics and Computation, 6(2), 167-177. DOI: http://dx.doi.org/10.26855/jamc.2022.06.001.

S. Balakrishnan, Tamilarasi Suresh, Raja Marappan. (2021). Analysis of Recent Trends in Solving NP Problems with New Research Directions Using Evolutionary Methods. International Journal of Research Publication and Reviews, Vol (2), Issue (8), (2021) Page 1429-1435.

S. Balakrishnan, Tamilarasi Suresh, Raja Marappan. (2021). A New Multi-Objective Evolutionary Approach to Graph Coloring and Channel Allocation Problems. Journal of Applied Mathematics and Computation, 5(4), 252-263. DOI: http://dx.doi.org/10.26855/jamc.2021.12.003.

Tai, D. W. S., Wu, H. J., & Li, P. H. (2008). Effective e‐learning recommendation system based on self‐organizing maps and associ-ation mining. the electronic library.

Tam, V., Lam, E. Y., and Fung, S. T. (2012, July). Toward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimization. In 2012 IEEE 12th International Conference on Advanced Learning Technologies (pp. 592-596). IEEE.

Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., and Duval, E. (2012). Context-aware recommender systems for learning: a survey and future challenges. IEEE Transactions on Learning Technologies, 5(4), pp. 318-335.

Yu, Z., Nakamura, Y., Jang, S., Kajita, S., and Mase, K. (2007, July). Ontology-based semantic recommendation for context-aware e-learning. In International Conference on Ubiquitous Intelligence and Computing (pp. 898-907). Springer, Berlin, Heidelberg.

Zhou, Y., Huang, C., Hu, Q., Zhu, J., and Tang, Y. (2018). Personalized learning full-path recommendation model based on LSTM neural networks. Information Sciences, 444, 135-152.

How to cite this paper

Analysis of Recent Trends in E-Learning Personalization Techniques

How to cite this paper:  Raja Marappan, S. Bhaskaran. (2022). Analysis of Recent Trends in E-Learning Personalization Techniques. The Educational Review, USA6(5), 167-170.

DOI: http://dx.doi.org/10.26855/er.2022.05.003

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