Analysis of Students Performance in Flipped Classroom-based Vocational Education Using Data Mining Tools

Abstract

This research investigates the impact of applying a Flipped Classroom Approach (FCA) in Vocational Education and proposes an improved approach that integrates Cognitive Theory of Multimedia Learning (CTML) with flipped learning based on Revised Bloom’s taxonomy. Educational data mining (EDM) using statistics and data analysis was applied to transform large educational raw data into useful and meaningful information. This information can be applied to gain insights into students and their learning environments, enhance teaching support, and optimize decision-making in educational systems. EDM was performed on the student activities recorded on online learning management system (Moodle) including, pre-class activities, attendance, and after-class assignments. A correlation analysis was performed to explore the relationship between their activities in Moodle and academic performance. Results show that students who followed the flipped learning strategy outperformed those who did not, and the video designed according to CTML had a positive impact on student outcomes. The study also revealed that after-class assignments had a significant influence on students’ results, and that the students’ final results could be predicted through their assignment performance.