Analyzing Students’ Learning Engagements Using PLS-SEM: A Case Study in A Small Private Online Programming Course

Nowadays, particularly during the epidemic situation, the increasing popularity of online-offline education. In computer science education, a small private online programming course (SPOPC) is an important curriculum course to enhance the students’ problem-solving, creative thinking and teamwork activities. The main purpose of this study is to investigate the influences of students’ composite engagements (learning performances and personal feelings) in a SPOPC. We applied an online engagement framework for higher education and partial least squares structural equation modeling (PLS-SEM) approach. Our analysis was conducted from a Python programming course at Feng Chia University, Taiwan, held in the fall semester of 2021–2022 through OpenEdu platform. Our results show that (1) Strongest mediator performance was ‘Video watching’ affected by four significant relations; (2) Behavioral engagement ‘BE’ and emotional engagement ‘EE’ are positively affected ‘Video watching’ performance and they were inextricably linked factors; (3) Social engagement ‘SE’ significantly affected the ‘Term project’ and ‘Final score’ through ‘Midtest’; (4) Cognitive engagement ‘CE’ did not significantly affect the ‘Term project’; Most important is that (5) Students’ first feelings about the course (the first part of the semester) are important factors in deciding to continue a related course or drop out of the course in the future. These findings have valuable practical implications for course instructors who design and lead the programming course.