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Computing-Related Self-Efficacy: The Roles of Gender, Academic Performance, and Computational Capabilities

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2011 ASEE Annual Conference & Exposition


Vancouver, BC

Publication Date

June 26, 2011

Start Date

June 26, 2011

End Date

June 29, 2011



Conference Session

Pedagogical Issues in Computing

Tagged Division

Computers in Education

Page Count


Page Numbers

22.366.1 - 22.366.11



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Paper Authors


Chia-Lin Ho North Carolina State University

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Chia-Lin Ho is a doctoral student in Industrial/Organizational Psychology at North Carolina State University. She received a B.S. in Psychology and a Bachelor of Business Administration at the National Cheng-Chi University in Taiwan in 2002 and her Masters in I/O Psychology at the University of North Carolina at Charlotte in 2005. Her research interests include measurement and evaluation issues, individual differences, leadership, cross-cultural studies, work motivation, and the application of technology on human resources management.

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Dianne Raubenheimer North Carolina State University

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Dr. C. Dianne Raubenheimer is Director of Assessment in the College or Engineering and Adjunct Assistant Professor in the Department of Adult and Higher Education at NC State University. Within the College of Engineering she serves as the coordinator of ABET and other accreditation processes, acts as a resource/consultant to faculty in the different programs, develops and implements assessment plans, and serves as the primary educational assessment/data analyst adviser on the Dean’s staff. A particular interest is in helping faculty to develop and implement classroom-based assessment and action research plans to establish the effectiveness of instruction and to use the data to improve teaching and student learning. She is currently working with several engineering faculty assessing the impact of in-class use of technology on teaching and student learning. Dianne has also worked as an education consultant for a number of organizations conducting program evaluations and is currently serving as external evaluator on several grants. Her research interests focus on faculty development, action research, the role of technology in teaching and learning, and assessment in higher education.

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Computing-related Self-efficacy: The Roles of Gender, Academic Performance, and Computational CapabilitiesAbstractResearch has documented that self-efficacy is a great predictor of individuals’ behaviors andmotives. In engineering education, self-efficacy has received a significant amount of attention indealing with the issues regarding retention of female engineering students. As an effort tounderstand the role of self-efficacy in engineering education, this study focuses on self-efficacytowards computing and engineering and examines their relations to gender, GPAs, andcomputational capabilities.This study is part of a larger project, supported by a National Science Foundation CISEPathways to Revitalized Undergraduate Computing Education (CPATH), designed to preparestudents for pervasive, advanced computing in the workplace. These data included participants(N = 418) enrolled in undergraduate engineering courses in several programs in 2009 and 2010.These participants completed measures of computer self-efficacy and self-efficacy towardsengineering, self-ratings of six computational capabilities (i.e., database fundamentals, processmodeling and design, basic knowledge of programming, data analysis skills, communicationtools/organization, and web search) indicated as important to industry representatives, anddemographic information.Two multiple regression analyses were conducted, each for computer self-efficacy and self-efficacy towards engineering/computer science. Gender, GPAs, and six computationalcapabilities were regressed on each measure of self-efficacy. The analysis results revealedsignificant gender differences in computer self-efficacy and self-efficacy towardsengineering/computer science, providing support for the continuing existence of genderdifferences in computing-related self-efficacy. Individuals with higher GPAs reported higherlevels of self-efficacy towards engineering/computer science. Ratings on computationalcapabilities also showed different relations to computer self-efficacy and self-efficacy towardsengineering/computer science. Specifically, controlling gender and GPAs, self-efficacy towardsengineering was predicted by reported skill level on database fundamentals, basic knowledge ofprogramming, and data analysis. Computer self-efficacy was predicted by capabilities on basicknowledge of programming, communication tools/organization, and web search above andbeyond gender and GPAs.The study results provide support for the relations of computing-related self-efficacy, gender,GPAs, and skill level of specific computational capabilities. Given the importance of self-efficacy in learning, these findings have implications for engineering education. Besidescontinuing to acknowledge gender differences in computing-related self-efficacy, traininginterventions to enhancing computational capabilities should be implemented to increasestudents’ computer self-efficacy and self-efficacy towards engineering/computer science and, inturn, lead to more learning involvement and higher learning motivation.Analysis results for “self-efficacy towards engineering”Analysis results for “computer self-efficacy”

Ho, C., & Raubenheimer, D. (2011, June), Computing-Related Self-Efficacy: The Roles of Gender, Academic Performance, and Computational Capabilities Paper presented at 2011 ASEE Annual Conference & Exposition, Vancouver, BC. 10.18260/1-2--17647

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