Investigation of Student’s Engagement in Blended PBL-based Engineering Course and its Influence on Performance

Abstract

Learner engagement in digital or online learning has been identified as one of the many challenges and personalizing the digital learning content to keep students motivated and engaged throughout the duration of course is gaining much interest among academia. The purpose of this study is to identify the levels of student engagement and understand the relationship between learner engagement and their academic performance. This study has used k-means machine learning algorithm to identify the levels of student's engagement and tried to identify the relationship between the engagement metrics and student performance using correlation analysis. Based on students’ engagement metrics, k-mean algorithm classifies students in to two levels, namely High engaged students, and Low engaged students. The results of correlation analysis showed that there was a positive correlation between the engagement metrics and performance. Identifying the levels of student engagement possibly will help in personalizing the learning by recommending the e-content based on engagement metrics and identifying the relationship between the engagement metrics and performance might help faculty to design activities and content for the courses.