Advancing a Model of Students' Intentional Persistence in Machine Learning and Artificial Intelligence

Sharon Ferguson; James Magarian; Alison Olechowski; Katherine Mao

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Conference: 2022 ASEE Annual Conference & Exposition
Location: Minneapolis, MN
Publication Date: August 23, 2022
Start Date: June 26, 2022
End Date: June 29, 2022
Conference Session: CIT Division Technical Session #6
Page Count: 48
DOI: 10.18260/1-2--41724
Permanent URL: https://peer.asee.org/41724
Download Count: 2240

Paper Authors

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Sharon Ferguson University of Toronto

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Sharon is a PhD student in the department of Mechanical and Industrial Engineering at the University of Toronto. She previously completed her Bachelors in Industrial Engineering also at the University of Toronto. She is passionate about supporting women in Engineering and STEM more broadly, both within and outside of her research. She has held fellowships in Ethics of AI and Technology & Society organizations.

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James Magarian Massachusetts Institute of Technology

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James Magarian, PhD, is a Senior Lecturer at the Gordon-MIT Engineering Leadership Program at Massachusetts Institute of Technology, where he has taught in engineering leadership, design, and ethics. His current research centers on engineering work and careers, with a focus on engineering career pathways and student persistence. Prior to joining MIT, James served as a mechanical engineer and engineering manager in the aerospace industry.

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Alison Olechowski University of Toronto

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Alison Olechowski is an Assistant Professor in the Department of Mechanical & Industrial Engineering and the Troost Institute for Leadership Education in Engineering (Troost ILead). She completed her PhD at the Massachusetts Institute of Technology (MIT) studying product development decision-making during complex industry projects. Dr. Olechowski completed her BSc (Engineering) at Queen’s University and her MS at MIT, both in Mechanical Engineering. Dr. Olechowski studies the processes and tools that teams of engineers use in industry as they design innovative new products.

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Katherine Mao University of Toronto

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Katherine is a recent graduate of the University of Toronto's Engineering Science program majoring in Robotics. She wants to build tech to transform the way humans interact with the world and has an interest in human-centered and interdisciplinary approaches to design.

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Abstract

Machine Learning and Artificial Intelligence are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition algorithms, recidivism algorithms, and resume reviewing algorithms, when they are designed without diversity in mind. As Machine Learning (ML) and Artificial Intelligence (AI) expand into more areas of our lives, we must take action to promote diversity among those working in this field. A critical step in this work is understanding why some students who choose to study ML/AI later leave the field.

While the persistence of diverse populations has been studied in engineering specifically, and Science, Technology, Engineering and Math (STEM) more generally, there is a lack of research investigating factors that influence persistence in ML/AI. In this work, we present the advancement of a model of intentional persistence in ML/AI in order to identify areas for improvement. We surveyed undergraduate and graduate students enrolled in ML/AI courses at a major North American university in fall 2021. We examine persistence across demographic groups, such as gender, international student status, student loan status, and visible minority status. We investigate independent variables that distinguish ML/AI from existing studies of persistence in STEM, such as the varying emphasis on non-technical skills, the ambiguous ethical implications of the work, and the highly competitive and lucrative nature of the field.

Our findings suggest that short-term intentional persistence in ML/AI is associated with academic enrollment factors such as major and level of study. In terms of long-term intentional persistence, we found that measures of professional role confidence developed to study persistence in engineering are also important predictors of intent to remain in ML/AI. Unique to our study, we show that wanting your work to have a positive social benefit is a negative predictor of long-term intentional persistence in ML/AI, and women generally care more about this. We find some evidence that having high confidence in non-technical interpersonal skills may also be a positive predictor of long-term intentional persistence. We provide recommendations to educators to meaningfully discuss ML/AI ethics in classes and encourage the development of interpersonal skills to help increase diversity in the field.

Citation
Format

Ferguson, S., & Magarian, J., & Olechowski, A., & Mao, K. (2022, August), Advancing a Model of Students' Intentional Persistence in Machine Learning and Artificial Intelligence Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41724

TY - CPAPER
AB - Machine Learning and Artificial Intelligence are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition algorithms, recidivism algorithms, and resume reviewing algorithms, when they are designed without diversity in mind. As Machine Learning (ML) and Artificial Intelligence (AI) expand into more areas of our lives, we must take action to promote diversity among those working in this field. A critical step in this work is understanding why some students who choose to study ML/AI later leave the field.

While the persistence of diverse populations has been studied in engineering specifically, and Science, Technology, Engineering and Math (STEM) more generally, there is a lack of research investigating factors that influence persistence in ML/AI. In this work, we present the advancement of a model of intentional persistence in ML/AI in order to identify areas for improvement. We surveyed undergraduate and graduate students enrolled in ML/AI courses at a major North American university in fall 2021. We examine persistence across demographic groups, such as gender, international student status, student loan status, and visible minority status. We investigate independent variables that distinguish ML/AI from existing studies of persistence in STEM, such as the varying emphasis on non-technical skills, the ambiguous ethical implications of the work, and the highly competitive and lucrative nature of the field.

Our findings suggest that short-term intentional persistence in ML/AI is associated with academic enrollment factors such as major and level of study. In terms of long-term intentional persistence, we found that measures of professional role confidence developed to study persistence in engineering are also important predictors of intent to remain in ML/AI. Unique to our study, we show that wanting your work to have a positive social benefit is a negative predictor of long-term intentional persistence in ML/AI, and women generally care more about this. We find some evidence that having high confidence in non-technical interpersonal skills may also be a positive predictor of long-term intentional persistence. We provide recommendations to educators to meaningfully discuss ML/AI ethics in classes and encourage the development of interpersonal skills to help increase diversity in the field.

AU - Sharon Ferguson
AU - James Magarian
AU - Alison Olechowski
AU - Katherine Mao
CY - Minneapolis, MN
DA - 2022/08/23
PB - ASEE Conferences
TI - Advancing a Model of Students' Intentional Persistence in Machine Learning and Artificial Intelligence
UR - https://peer.asee.org/41724
DO - 10.18260/1-2--41724
ER -