Board 315: Improving Students’ Decision-Making Behavior in Choosing an Engineering Pathway

Debapratim Ghosh; Samuel Harford; Houshang Darabi; Jennifer R. Amos

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: NSF Grantees Poster Session
Tagged Topic: NSF Grantees Poster Session
Page Count: 8
DOI: 10.18260/1-2--42893
Permanent URL: https://peer.asee.org/42893
Download Count: 190

Paper Authors

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Debapratim Ghosh University of Illinois, Urbana - Champaign

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Graduate Student in Statistics

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Samuel Harford

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Houshang Darabi The University of Illinois, Chicago orcid.org/0000-0001-7881-6542

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Dr. Houshang Darabi is a Professor of Industrial and Systems Engineering in the Department of Mechanical and Industrial Engineering at the University of Illinois Chicago. Dr. Darabi’s research focuses on the use of Big Data, process mining, data mining, Operations Research, high performance computing, and visualization in improving educational systems and students’ learning. Dr. Darabi’s research has been funded by federal and corporate sponsors including the National Science Foundation, and the National Institute of Occupational Health and Safety.

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Jennifer R. Amos University of Illinois, Urbana - Champaign orcid.org/0000-0002-9437-8201

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Dr Amos joined the Bioengineering Department at the University of Illinois in 2009 and is currently a Teaching Professor in Bioengineering. She is a AIMBE Fellow, BMES Board of Director Member, ABET Commissioner, two-time Fulbright Specialist in engineering education and has won multiple awards and recognitions for her teaching and scholarship of teaching. Outside of BME, she has also worked to revolutionize the future of graduate medical education serving as a founding member of the new Carle-Illinois College of Medicine, a first-of-its-kind engineering driven college of medicine. Amos is part of the Illinois NSF RED (Revolutionizing Engineering & Computer Science Departments) research team leading efforts to innovate assessment practices for engineering toward producing more holistic engineers. Amos has a decade’s worth of experience leading curriculum reform implementing robust assessment strategies at multiple institutions.

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Abstract

This project aims to serve the national interest by studying how engineering students make career and major of study decisions and implementing a web-based platform that helps first year undergraduate engineering students in deciding about their major of study. Three overarching goals for this NSF IUSE Level I Engaged Student Learning project are: 1) to advance understanding of the field in what information is used by students to make academic major decisions, 2) to refine available decision-making theories and models for major-selection decisions, 3) to develop an online, interactive infrastructure grounded in knowledge and theory about academic major selections to aid students in making informed major selection decisions. This works in progress paper will outline formation of a survey instrument to obtain understanding of the decision-making processes that first year students use to make decisions in selecting a major field of study related to goals 1 and 2.

The project uses both quantitative and qualitative methods to determine trends and differences in student decision making processes for selecting a major. The project leverages Social Cognitive Career Theory and Cognitive Information Processing as frameworks for forming an understanding of career related decision making. Students’ attitude towards different majors and career selection outcomes are tracked through surveys and interviews at two large midwestern universities enrolling a diverse class of students. The survey was designed with 58 Likert-type questions categorized into Career Decision Making Outcomes, Career Exploratory Plans, Career Environmental Exploration, Career Self Exploration and Career Thoughts Inventory. The IRB approved survey was sent to 2000 students across the two universities and across 13 different engineering majors with a response rate of 20%. Qualtrics was used for data collection and Python was used for data analysis. Initial analysis was performed to determine areas of significance. Interview questions related to theories of decision making and career exploration behavior formed the basis for semi-structured interviews. Early quantitative findings from 400 respondents were presented to the research team to inform additional questions for the qualitative portion of the study. Clustering was performed from the survey results to determine 20 candidates for interviews based on numeric and categorical variables such as major, race, domestic or international student status, and sub-scores of the survey. Semi-structured interviews were conducted via Zoom to add to the understanding and nuances of differences in student decision-making processes. Interviews were coded using grounded theory to identify themes related to career exploration and career decision making.

This paper illustrates the methods used and early results of an exploratory research project that tries to understand student choice in selection of majors and career paths. Subsequently, a comprehensive tool will be developed based on the quantitative results from the surveys and qualitative information gained from the interviews.

Citation
Format

Ghosh, D., & Harford, S., & Darabi, H., & Amos, J. R. (2023, June), Board 315: Improving Students’ Decision-Making Behavior in Choosing an Engineering Pathway Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42893

TY - CPAPER
AB - This project aims to serve the national interest by studying how engineering students make career and major of study decisions and implementing a web-based platform that helps first year undergraduate engineering students in deciding about their major of study. Three overarching goals for this NSF IUSE Level I Engaged Student Learning project are: 1) to advance understanding of the field in what information is used by students to make academic major decisions, 2) to refine available decision-making theories and models for major-selection decisions, 3) to develop an online, interactive infrastructure grounded in knowledge and theory about academic major selections to aid students in making informed major selection decisions. This works in progress paper will outline formation of a survey instrument to obtain understanding of the decision-making processes that first year students use to make decisions in selecting a major field of study related to goals 1 and 2.

The project uses both quantitative and qualitative methods to determine trends and differences in student decision making processes for selecting a major. The project leverages Social Cognitive Career Theory and Cognitive Information Processing as frameworks for forming an understanding of career related decision making. Students’ attitude towards different majors and career selection outcomes are tracked through surveys and interviews at two large midwestern universities enrolling a diverse class of students. The survey was designed with 58 Likert-type questions categorized into Career Decision Making Outcomes, Career Exploratory Plans, Career Environmental Exploration, Career Self Exploration and Career Thoughts Inventory. The IRB approved survey was sent to 2000 students across the two universities and across 13 different engineering majors with a response rate of 20%. Qualtrics was used for data collection and Python was used for data analysis. Initial analysis was performed to determine areas of significance. Interview questions related to theories of decision making and career exploration behavior formed the basis for semi-structured interviews. Early quantitative findings from 400 respondents were presented to the research team to inform additional questions for the qualitative portion of the study. Clustering was performed from the survey results to determine 20 candidates for interviews based on numeric and categorical variables such as major, race, domestic or international student status, and sub-scores of the survey. Semi-structured interviews were conducted via Zoom to add to the understanding and nuances of differences in student decision-making processes. Interviews were coded using grounded theory to identify themes related to career exploration and career decision making.

This paper illustrates the methods used and early results of an exploratory research project that tries to understand student choice in selection of majors and career paths. Subsequently, a comprehensive tool will be developed based on the quantitative results from the surveys and qualitative information gained from the interviews.
AU - Debapratim Ghosh
AU - Samuel Harford
AU - Houshang Darabi
AU - Jennifer R. Amos
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - Board 315: Improving Students’ Decision-Making Behavior in Choosing an Engineering Pathway
UR - https://peer.asee.org/42893
DO - 10.18260/1-2--42893
ER -