Student Perceptions of Their Abilities and Learning Environment in Large Introductory Computer Programming Courses – One Year Later
Laura K. Alford, Valeria Bertacco · 2020
Over the past 30 years, women completing computer science and computer engineering undergraduate degrees have been a minority compared to their male counterparts.Three obstacles to gender diversity in computer science and computer engineering are: stereotyped traits, perceived abilities, and learning environment.Identifying implicit bias as a component of these obstacles, we implemented a series of activities designed to lessen the impact of implicit bias on our students in large-enrollment introductory computer programming courses.One element of assessing the success of our program is to use entry and exit surveys to gauge the change in students' perceptions of their abilities and learning environment.In particular, we are interested in the difference between men's and women's perceptions of their abilities and the learning environments in these courses.The initial findings of these entry and exit surveys found that while there are differences between men's and women's responses, the differences were not as great as we had feared.However, due to the relatively large number of responses (1200+) it is possible that even a small difference in, for example, student agreement/disagreement with "I believe that other students in computer programming courses will be welcoming of me" could have a disproportionately large effect on the number of women deciding to major in computer science/computer engineering.After improving the survey process based on recommendations from the initial study, we embarked on a 5 year program to gather data and assess the gender differences in two sequential large programming courses.Our overarching research question is: Do women and men show a statistically significant difference in their perceptions of their abilities and learning environment as measured by self-efficacy, intimidation by programming, and feelings of inclusion?This paper will present the first set of entry and exit survey results (Fall 2017) analyzed using mixed model ANOVA for repeated measures of questions on self-efficacy, intimidation by programming, and feelings of inclusion.Statistically significant results include: women have lower self-efficacy than men in both courses, and women are more intimidated by programming than men in the second programming course.Although we cannot reject the null hypothesis for any of our three hypotheses regarding these questions, we can still glean useful insight from this data set.