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Student Perceptions of Their Abilities and Learning Environment in Large Introductory Computer Programming Courses – One Year Later

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Women in Engineering Division Technical Session 6

Tagged Division

Women in Engineering

Page Count

20

DOI

10.18260/1-2--31012

Permanent URL

https://peer.asee.org/31012

Download Count

41

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Paper Authors

biography

Laura K Alford University of Michigan

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Laura K. Alford is a Lecturer and Research Investigator at the University of Michigan.

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biography

Valeria Bertacco University of Michigan

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Valeria Bertacco is Arthur F. Thurnau Professor of Electrical Engineering and Computer Science and Associate Dean for Physical Sciences and Engineering in the Rackham Graduate School at the University of Michigan. She is also an Adjunct Professor of Computer Engineering at the Addis Ababa Institute of Technology. Her research interests are in the area of computer design, with emphasis on specialized architecture solutions and design viability, in particular reliability, validation and hardware-security assurance. She joined the University of Michigan in 2003, after working with the Advanced Technology Group of Synopsys, which she joined via the acquisition of Systems Science Inc. Valeria has been a member of the Executive Committee of the Design Automation Conference since 2013, serving in the role of Technical Program Chair for 2017. The conference is the top forum in hardware and embedded systems design, attracting over 7,000 attendees yearly. She has also served as Associate Editor of the IEEE Transactions on Computer Aided Design. She is the author of three books on correctness in computer design.

She received her M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 1998 and 2003; and a Computer Engineering degree ("Dottore in Ingegneria") summa cum laude from the University of Padova, Italy in 1995. Valeria is the recipient of the IEEE CEDA Early Career Award, NSF CAREER award, the Air Force Office of Scientific Research's Young Investigator award and the IBM Faculty Award. From the University of Michigan she received the Vulcans Education Excellence Award, the Herbert Kopf Service Excellence Award, the Sarah Goddard Power Award for contribution to the betterment of women and the Rackham Faculty Recognition Award. Valeria is an ACM Distinguished Scientist and an IEEE Fellow.

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Abstract

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

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