Association for Computing Machinery
SIGCSE '18 Proceedings of the 49th ACM Technical Symposium on Computer Science Education
This study investigated patterns in the development of computational thinking practices in the context of the Exploring Computer Science (ECS) program, a high school introductory CS course and professional development program designed to foster deep engagement through equitable inquiry around CS concepts. Past research indicates that the personal relevance of the ECS experience influences students' expectancy-value towards computer science. Expectancy-value is a construct that is predictive of career choices. We extended our research to examine whether expectancy-value influences the development of computational thinking practices. This study took place in the context of two ECS implementation projects across two states. Twenty teachers, who implemented ECS in 2016-17, participated in the research. There were 906 students who completed beginning and end of year surveys and assessments. The surveys included demographic questions, a validated expectancy-value scale, and questions about students' course experiences. The assessments were developed and validated by SRI International as a companion to the ECS course. Overall, student performance statistically increased from pretest to posttest with effect size of 0.74. There were no statistically significant differences in performance by gender or race/ethnicity. These results are consistent with earlier findings that a personally relevant course experience positively influences students' expectancy for success. These results expanded on prior research by indicating that students' expectancy-value for computer science positively influenced student learning.
McGee, Steven; McGee-Tekula, Randi; Duck, Jennifer; McGee, Catherine; Dettori, Lucia; Greenberg, Ronald I.; Snow, Eric; Rutstein, Daisy; Reed, Dale; Wilkerson, Brenda; Yanek, Don; Rasmussen, Andrew M.; and Brylow, Dennis, "Equal Outcomes 4 All: A Study of Student Learning in ECS" (2018). Mathematics, Statistics and Computer Science Faculty Research and Publications. 636.