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Student Perceptions of Their Abilities and Learning Environment in Large Introductory Computer Programming Courses - Underrepresented Minorities

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Assessment of Learning in ECE Courses

Tagged Division

Electrical and Computer

Tagged Topic

Diversity

Page Count

29

DOI

10.18260/1-2--33297

Permanent URL

https://peer.asee.org/33297

Download Count

211

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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. She researches ways to use data-informed analysis of students' performance and perceptions of classroom environment to support DEI-based curricula improvements.

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biography

Andrew DeOrio University of Michigan Orcid 16x16 orcid.org/0000-0001-5653-5109

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Andrew DeOrio is a teaching faculty member at the University of Michigan and a consultant for web and machine learning projects. His research interests are in ensuring the correctness of computer systems, including medical and IOT devices and digital hardware, as well as engineering education. In addition to teaching software and hardware courses, he teaches Creative Process and works with students on technology-driven creative projects. His teaching has been recognized with the Provost's Teaching Innovation Prize, and he has twice been named Professor of the Year by the students in his department.

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Abstract

Over the past 30 years, students of color have been underrepresented among students completing computer science and computer engineering undergraduate degrees. While we do not directly control the rate at which students apply and are accepted at our institutions, we should strive to create environments that are conducive to URM student success once they are accepted.

A five year program is aimed at increasing the enrollment and graduation rates of women and underrepresented minority (URM) students in computer science and engineering at a competitive public research institution. Three obstacles to diversity in computer science and computer engineering have been identified: stereotyped traits, perceived abilities, and learning environment. Identifying implicit bias and imposter syndrome as components of these obstacles, we include a series of class activities designed to lessen the impact of implicit bias and imposter syndrome 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.

Previously, we investigated the difference between men’s and women’s perceptions of their abilities and the learning environments in introductory computing courses. We found a statistically significant association between gender and perception of self-efficacy, but not in how those perceptions change over the course of the term. In this paper, we expand our analysis to examine the experience of URM compared to non-URM students.

Our research question is: Are URM students associated with different perceptions of their abilities and learning environment as measured by confidence in success, intimidation by programming, and feelings of inclusion?

Our data set is comprised of entry and exit survey data for three semesters of introductory computing courses. The survey data will be analyzed using mixed model ANOVA for repeated measures of questions on self-efficacy, intimidation by programming, and feelings of inclusion. The results and analysis will be presented in this paper.

Alford, L. K., & DeOrio, A. (2019, June), Student Perceptions of Their Abilities and Learning Environment in Large Introductory Computer Programming Courses - Underrepresented Minorities Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33297

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