Analysis of student course evaluation data for an IT subject: implications for improving STEM education
RIS ID
111979
Abstract
This paper reports data analysis of students’ satisfaction on a graduate course in information technology for 11 consecutive semesters over six years at an Australian university. We find a negative trend between course satisfaction and class size and a positive trend between teaching and course satisfactions, consistent with what reported in literature from other disciplines. This study also reveals that teaching satisfaction rate has a negative association with neutral rate but surprisingly no association with course dissatisfaction rate. This implies that improvement on student course satisfaction through good teaching may mainly be resulted from converting those undecided students from neutrality to satisfaction. Results of this data analysis support a parallel flow model between course satisfaction and both neutrality and dissatisfaction, which leads to a new strategy for achieving a high level of course satisfaction for other domain-complexity courses in science, technology, engineering and mathematics (STEM) education. Strategically, innovative and engaged teaching is still the key to achieve a high course satisfaction. Tactically, guided by existing and emerging teaching and learning theories, a number of specified measures are worth of consideration in course design and delivery for similar highly technical courses for achieving a high level of course satisfaction in future.
Publication Details
Guo, W., Li, W., Wang, Y. & Shen, J. (2017). Analysis of student course evaluation data for an IT subject: implications for improving STEM education. International Journal of Information and Education Technology, 7 (9), 635-640.