Teaching Style Recommender using Machine Learning

Abstract

this paper aims to bridge the gap between teaching and learning in engineering education. Different students learn using different techniques or styles. There can be different kinds of learners within a single class. The teacher needs to understand this difference in order to deliver their contents in the classroom effectively. The proposed system aids the faculty in understanding the classroom composition in terms of the learning styles of students and suggests specific teaching methods that can be helpful for the entire class. The system uses a survey called the Index of Learning Styles (ILS) to understand the learning style of the class. A machine learning model is used to suggest the possible teaching methods based on the learning styles to ensure effective classroom delivery by the faculty. The model is trained on a custom made dataset to understand the styles. In future, this model can be extended for subjects other than engineering and this could be used by all faculties regardless of their experience in the field.