Machine Learning for Flipped Teaching in Higher Education—A Reflection

Machine learning is one of the revolutionary fields in present days that being successfully implemented in many areas. It enables a machine or a system to learn from various data input by the end-user and provides the next set of possible outcomes. Almost all the search engines and commercial sites have implemented various algorithms for commercialization purposes as well as customization of user data for prediction. Machine learning has a significant role in the education sector to explore various possibilities through which, the system can perform a cognitive analysis based on a given set of input data by the end-users, who can be the students or the teachers. Especially in a flipped classroom model, where the student-centric approach is adopted; machine learning can be a revolutionary approach to find the requirements of learner based on their existing skills. This paper provides an analysis of various types of machine learning that can be implemented through a learning management system for flipped classroom activities. In this paper, a new framework is proposed which can be implemented for effective flipped teaching in higher education in order to reduce the manual tasks of the teachers. The successful implementation of this approach can play a vital role in the community of learners.