Predictors of university students’ intentions to enroll in computer programming courses: a mixed-method investigation

Abstract

Students in technology majors such as Computer Science and Information Technology need to take a series of computer programming courses to graduate. Yet, not all students will persist in taking programming courses as required, and little is known about the factors influencing their enrollment intentions. Research is needed to better understand which factors predict students’ enrollment intentions for programming courses. Based on Expectancy-Value Theory, we surveyed three hundred university students in China to measure their intentions of enrolling in future programming courses and factors influencing their enrollment intentions. Mediated Multiple Indicators Multiple Causes models were run to identify the relationships between these factors and enrollment intentions. Expectancies, intrinsic value, attainment value, and utility value of the courses directly predict enrollment intentions. Peer comparison, quality of teaching, and programming experiences were found to influence enrollment intentions through expectancies and values indirectly. No gender difference was found in expectancies and enrollment intentions. Qualitative data analysis confirmed the findings from the quantitative data analysis and further revealed that course difficulty is another major factor influencing enrollment intentions. These findings have implications for supporting students' expectancies and values in computer programming and curriculum design.