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 (details withheld for review). However, due to the relatively large number of responses (1200+) it is possible that even a small difference in, for example, “How interested are you in majoring or minoring in Computer Science or Computer Engineering?” could have a disproportionately large effect on the number of women deciding to major in computer science/computer engineering.
As part of that initial analysis, we identified several actions to improve our data gathering and analysis. In particular, we have addressed the following three improvements:
Improvement #1: Continue to administer the surveys and use results to guide future course development and other possible interventions.
Action: We have IRB approval for a 5 year program to assess the success of this program on combating implicit bias.
Improvement#2: Ask demographic information at the end of the entry survey to avoid tainting data with preconceived notions of what the answers should be (stereotype threat).
Action: We moved all demographic-based questions to the end of the survey.
Improvement #3: Analyze the revised surveys more rigorously to determine metrics that are statistically significant.
Action: We will be processing the new, revised surveys using mixed design ANOVA.
Our research question is:
Do women and men show a statistically significant difference in their perceptions of their abilities and learning environment as measured by confidence in success, intimidation by programming, and feelings of inclusion?
Our data set will be comprised of entry and exit survey data for the first semester of this 5 year program (Fall 2017). The results and analysis will be presented in this paper.
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