Computing-related Self-efficacy : The Roles of Computational Capabilities , Gender , and Academic Performance

Research has documented that self-efficacy is a good predictor of individuals’ behavior and motivation. In engineering education, self-efficacy has received much attention in dealing with issues regarding the retention of female engineering students. As an effort to further understand the role of self-efficacy in computer science and engineering education, this study focused on students’ self-efficacy beliefs about engineering/computer science, as well as self-efficacy beliefs about computer usage, and then examined their relations to computational capabilities, gender, and academic performance. This study is part of a larger project, supported by a National Science Foundation CISE Pathways to Revitalized Undergraduate Computing Education (CPATH) grant, designed to prepare students for pervasive, advanced computing in the workplace. These data included participants (N = 389) enrolled in undergraduate computer science or engineering courses in several engineering programs in 2009 and 2010. Participants completed measures of engineering/computer science self-efficacy, computer self-efficacy and self-ratings of six computational capabilities which had been indicated by industry as important for new hires: (1) database fundamentals, (2) process modeling and design, (3) basic knowledge of programming, (4) data analysis skills, (5) communication /organization tools, and (6) web searching. Two multiple regression analyses were conducted, one for engineering/computer science selfefficacy and one for computer self-efficacy. Gender, GPAs, and the six computational capabilities were regressed on each measure of self-efficacy. The results revealed significant gender differences on both self-efficacy constructs, providing support for the continuing existence of gender differences in computing-related self-efficacy. Individuals with higher GPAs reported higher levels of engineering/computer science self-efficacy. Each of the six computational capabilities showed a different relationship to engineering/computer science selfefficacy and to computer self-efficacy. Specifically, by controlling for gender and GPA, engineering/computer science self-efficacy was predicted by capabilities on database fundamentals, basic knowledge of programming, and data analysis. Computer self-efficacy was predicted by capabilities on basic knowledge of programming, communication /organization tools, and web searching. Process modeling and design did not predict either self-efficacy constructs. The study results provide support for the relationship among computing-related self-efficacy, gender, GPA, and specific computational capabilities. Given the importance of self-efficacy in learning, these findings have implications for computer science and engineering education. Besides continuing to acknowledge gender differences in computing-related self-efficacy, training interventions to enhance specific computational capabilities should be implemented to increase students’ beliefs in their ability to perform successfully in computing-related disciplines, in turn leading to better learning outcomes and higher learning motivation.