An Intelligent Personalized E-Learning System Based on Dynamic Learner's Preference

The impression of traditional learning has changed rapidly in the last couple of years. Classroom training is not the only option to learn. Learners find it challenging to select significant learning tutorials most of the time and relatively spend less time on actual learning. Indeed, traditional learning based on the “one size fits all” approach will, in general, help just a single instructive model. This paper zeroed in on improving the viability and nature of the personalized Intelligent E-Learning. The architecture makes a fruitful improvement that facilitates the personalization of learning paths and materials. We proposed a model with consideration of the learner's preference. A personalized search engine saves learners time to find specific learning material. An E-Learning system is beneficial for a commercial organization to overcome critical challenges in training and development for their employees. An experiment/survey was led to assess the performance of this model, and the outcomes are promising.