Designing an Automated Intelligent e-Learning System to Enhance the Knowledge using Machine Learning Techniques

Abstract

The modern digital world requires its users to learn continuously in order to enhance their knowledge in the working environment and the academic sector. This kind of learning is significantly facilitated by the E-Learning platform, which is better than the traditional methods. As E-Learning offers benefits like time and space independence, many learners have made it their choice. However, since an abundant of E-Learning courses are available on websites, learners are confused as to which is the right one to choose. This paper proposes an Automated Intelligent Learning (AIL) methodology which covers the entire Teaching-Learning Process (TLP) to overcome this issue. It enables the selection of suitable topics and framing an appropriate course syllabus and assessment questions for the users. In it, the learner satisfies topic selection based on Bloom's taxonomy. This enables high-quality knowledge outcomes in the learner. The subject curriculum is framed by using Hierarchical clustering techniques. This helps the user to fix suitable topics and conveniently generate questions using machine learning techniques. The proposed methodology was evaluated by carrying out post and pre-assessment tests on undergraduate students from computer science courses. The performance analysis of the proposed methodology was compared with that of the existing methodology. It was observed that the proposed methodology is effective in applying the topic selection hierarchical method to make a perfect syllabus for the course, and assessment questions. Besides, it was found to enable the learner to learn without any confusion or distraction.