Personalized E-learning System Using Linear Regression for Intelligent Tutoring Systems

Abstract

An e-learning system allows the learners to attend courses online from any geographical location, at any time using the Internet. The standard e-learning system does not give learners an individualistic model as it is not personalized and is inappropriate for all users. Hence, the satisfaction level of learning a course online is low for many users. This problem can be solved by applying an adaptive learning model for e-learning systems. This type of learning combines intelligent teaching with machine learning techniques to personalize the learners’ learning experience. In adaptive e-learning, the learning content is delivered based on the learner's knowledge level, experience level related to the course, interests, and background. This kind of learning favours an effective way to deal with the self-paced learning. A framework for developing an adaptive system is explored here, based on the core concepts of adaptive/personalized e-learning systems or the so called intelligent tutoring systems (ITS).