Analysis of User's Learning Styles and Academic Emotions through Web Usage Mining

Digital learning environment has seen a considerate amount of growth as opposed to the traditional learning environment with a massive shift towards digitalization in day-to-day life. When teaching in person, one can get a deep understanding of how students are grasping information, and their performance can be monitored. On the other hand, this task becomes challenging when in digital learning process. Several techniques have been studied for identification of learning styles and academic emotions such as questionnaires, facial expression recognition, biometrics data, etc. This paper proposes a system which aims at identification and analysis of learning styles and emotional behaviour of users based on the techniques of web usage mining where web server logs are used for data capturing as they have many advantages over other techniques. The logs are further pre-processed and used for extraction of desired results using different machine learning methodologies like data clustering, classification to name a few. This will help to identify hidden patterns of users over the period of completion of course and ultimately improve the teaching learning efficiency.