Applying Machine Learning to Understand the Student Perceptions and Learning for the Effectiveness of the Flipped Classroom Approach

Abstract

The flipped classroom system has attracted substantial interest from educational theorists. The concept here is to reverse the focus from lectures to active learning during class with the expectation that students will be more focused throughout the day. Students' perceptions of the flipped classroom model are examined in this article, and it also considers the relationship between the learning styles chosen by students and their performance in the flipped classroom. The study combines both qualitative and quantitative approaches in establishing whether flipped classes enhance students' learning outcomes or not. In addition, the research identifies the possibility of using Machine Learning (ML) methods to investigate student interaction and performance information so that learning difficulties may be identified early and flipped classroom activities personalized.