Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
Women in Engineering
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.
Alford, L. K., & Bertacco, V. (2018, June), Student Perceptions of Their Abilities and Learning Environment in Large Introductory Computer Programming Courses – One Year Later Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--31012
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2018 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015