Board 158: Engagement Patterns Across Race, Gender and Family Income in Engineering Classrooms

Denise Wilson; Lauren Summers; Joanna Wright

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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: NSF Grantees Poster Session
Tagged Topics: Diversity and NSF Grantees Poster Session
Page Count: 19
DOI: 10.18260/1-2--32276
Permanent URL: https://peer.asee.org/32276
Download Count: 601

Paper Authors

biography

Denise Wilson University of Washington

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Denise Wilson is a professor of electrical engineering at the University of Washington, Seattle. Her research interests in engineering education focus on the role of self-efficacy, belonging, and other non-cognitive aspects of the student experience on engagement, success, and persistence and on effective methods for teaching global issues such as those pertaining to sustainability.

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Lauren Summers University of Washington

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Lauren Summers is a doctoral student in the College of Education at the University of Washington, Seattle. Her research interests focus on the potential roles of socioeconomic status, ethnicity, gender, and other political identifiers in determining undergraduate engagement across a variety of majors, including engineering.

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Joanna Wright University of Washington

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Joanna Wright is an M.Ed. student in Learning Sciences and Human Development at the University of Washington, Seattle. Her education research interests span early childhood through higher education, with a focus on the impact of pedagogical practices and contexts on learning and development.

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Abstract

This IUSE-funded study investigated differences in behavioral and emotional engagement that emerge across family income, gender, and race in engineering classrooms. Engagement levels and engagement patterns were measured across seven sophomore- and junior-level engineering courses at a large public university. Differences in engagement were evaluated quantitatively between the two numerical majority races in this study (Asian, white), between genders, between international and domestic students, and across three levels of family income. Sample sizes for other racial groups (black, Pacific Islander, Native American, Hispanic, and Other) were too small to support analyses by family income and were not included in this study.

Initial analyses of variance (ANOVA) revealed significant differences in at least one form of engagement between Asian and white students, between men and women, between domestic and international students, and across family income levels. As a result, all four demographic variables (race, gender, country of origin, family income) were retained in a subsequent linear regression to understand potential interactions among these demographic variables. Since these models were weak, the analysis then looked at engagement patterns rather than engagement levels.

In this next phase of analysis, scores for the five engagement variables were classified using a non-parametric k-means clustering approach. The data optimally separated into two main categories: less engaged students (Cluster 1) and more engaged students (Cluster 2). Among domestic students, 100% of low income Asian women and 82% of low income Asian men (82%) fell into the more engaged cluster, while high-income Asian women (83%) fell into the less engaged cluster. Among international students (who were entirely Asian in this sample), low income Asian men and high income Asian women were among those who had the highest percentage of lesser engaged students (40% of each group, respectively) while middle income Asian men and middle income Asian women had the highest percentage of more engaged students (approximately 80% of each). Overall, the k-means clustering approach provided greater insight into the data than traditional statistical analysis techniques. Differences and trends among all four demographic variables (gender, family income, race, country of origin) emerged, showing that students from some demographic groups seem more susceptible to remaining less engaged in courses than other groups.

Citation
Format

Wilson, D., & Summers, L., & Wright, J. (2019, June), Board 158: Engagement Patterns Across Race, Gender and Family Income in Engineering Classrooms Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32276

TY  - CPAPER
AB  - This IUSE-funded study investigated differences in behavioral and emotional engagement that emerge across family income, gender, and race in engineering classrooms.  Engagement levels and engagement patterns were measured across seven sophomore- and junior-level engineering courses at a large public university.  Differences in engagement were evaluated quantitatively between the two numerical majority races in this study (Asian, white), between genders, between international and domestic students, and across three levels of family income.  Sample sizes for other racial groups (black, Pacific Islander, Native American, Hispanic, and Other) were too small to support analyses by family income and were not included in this study.  

Initial analyses of variance (ANOVA) revealed significant differences in at least one form of engagement between Asian and white students, between men and women, between domestic and international students, and across family income levels.  As a result, all four demographic variables (race, gender, country of origin, family income) were retained in a subsequent linear regression to understand potential interactions among these demographic variables. Since these models were weak, the analysis then looked at engagement patterns rather than engagement levels.  

In this next phase of analysis, scores for the five engagement variables were classified using a non-parametric k-means clustering approach. The data optimally separated into two main categories: less engaged students (Cluster 1) and more engaged students (Cluster 2).  Among domestic students, 100% of low income Asian women and 82% of low income Asian men (82%) fell into the more engaged cluster, while high-income Asian women (83%) fell into the less engaged cluster.  Among international students (who were entirely Asian in this sample), low income Asian men and high income Asian women were among those who had the highest percentage of lesser engaged students (40% of each group, respectively) while middle income Asian men and middle income Asian women had the highest percentage of more engaged students (approximately 80% of each). 
Overall, the k-means clustering approach provided greater insight into the data than traditional statistical analysis techniques.  Differences and trends among all four demographic variables (gender, family income, race, country of origin) emerged, showing that students from some demographic groups seem more susceptible to remaining less engaged in courses than other  groups.   


AU  - Denise Wilson
AU  - Lauren Summers
AU  - Joanna Wright
CY  - Tampa, Florida
DA  - 2019/06/15
PB  - ASEE Conferences
TI  - Board 158: Engagement Patterns Across Race, Gender and Family Income in Engineering Classrooms
UR  - https://peer.asee.org/32276
DO  - 10.18260/1-2--32276
ER  -