A survey on learning style based performance prediction and course recommendation in e-khool learning platform using optimized deep residual neural network

Abstract

Due to the tremendous development of the Internet, E-learning platforms have nowadays been considered the most promising platform that assists students to develop their skills to attain successful outcomes in intended learning. In this technical world, mobile applications and web applications play an important role in online learning systems. Nowadays, learning technology is increasing rapidly with different versions to encourage the learners. Online learning is very useful for every learner and especially, it is very helpful during this covid 19 outbreak, enabling the learners to learn their desired courses in Learning Management System (LMS). However, the prediction performance of the learner is challenging in LMS. A course recommendation system guides the students to select the appropriate course and the personalized environment will have the potential to attract the learner to such a system. A recommended system is defined as an intelligent system that suggests a personalized set of data excerpted from a mass volume of information. This research is attempted to propose feasible methodologies for learning style-based performance prediction and course recommendation in the E-Khool learning platform using deep learning algorithms. This research will open many research works in the field of deep learning algorithms in electronic platforms.