Title: Recommender System for Personalized Adaptive E-learning Platforms to Enhance Learning Capabilities of Learners Based on their Learning Style and Knowledge Level
Abstract: In web-based and distance education, e-learning is premeditated as one of the most popular research area. Most of the educational institutions nowadays are adapting e-learning environments to give distinct and very efficient services to their learners. The main reason for this growing requisite is the rapid growth in the field of electronic media and e-learning platforms make possible the learning from anywhere and anytime. Due to the sprouting of numerous kinds of learning activities in the e-learning environment, learners find it intricate to select the learning activity that finest meets their criteria. It is very important for an e-learning system to automatically render personalized recommendations to steer learners so that they can enhance their learning capabilities. This paper presents a novel approach, a framework for edifice architecture for a recommender system for personalized adaptive e-learning environment by considering learning style and knowledge level of learners. As we know the knowledge level and the learning style of each learner is diverse so we should comprehend different needs of the learners and make available them recommendation based on their needs.
Publication Year: 2019
Publication Date: 2019-03-19
Language: en
Type: article
Access and Citation
Cited By Count: 4
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