The Educational Review, USA

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Analysis of Recent Trends in E-Learning Personalization Techniques

Raja Marappan*, S. Bhaskaran

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

*Corresponding author: Raja Marappan

Published: May 27,2022


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.


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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.