Neurodivergent Student Characteristics and Engineering Course Outcomes

Manish Roy; Christa L. Taylor; Maria Chrysochoou

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Civil Engineering Division (CIVIL) Technical Session - Effective Teaching 3
Tagged Division: Civil Engineering Division (CIVIL)
Tagged Topic: Diversity
Permanent URL: https://peer.asee.org/47804

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Paper Authors

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Manish Roy University of Connecticut orcid.org/0000-0002-8203-222X

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Manish Roy is an Assistant Professor in Residence in the department of Civil and Environmental Engineering at the University of Connecticut. He obtained his Bachelor of Engineering degree in Civil Engineering (Hons.) at Jadavpur University in India. He obtained his MS and Doctoral degree in Civil Engineering at the West Virginia University and the University of Connecticut, respectively. He worked for nine years in the industry as an engineer/manager in India and Bangladesh before starting his graduate study in the US. He started his faculty career in 2019 at the University of Connecticut. His research interests lie in the field of concrete technology with a focus on finite element modeling of ultra high performance concrete. He is also interested in educational research. He is presently working on inclusive teaching practices considering the experience and needs of neurodivergent learners. This project is a part of an NSF-funded IUSE/PFE:RED grant.

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Christa L. Taylor University of Connecticut

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Christa L. Taylor, Ph.D., is an Independent Research Consultant and Research Affiliate with the Department of Educational Psychology at the University of Connecticut. Her research is focused on issues in creativity, social cognition, and neurodiversity. She received a Ph.D. in Social-Personality Psychology from the University at Albany, State University of New York before completing postdoctoral work at Yale University and Université catholique du Louvain in Belgium.

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Maria Chrysochoou University of Connecticut

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Maria Chrysochoou is a Professor and Head of the Department of Civil and Environmental Engineering at the University of Connecticut. She obtained her BS in Physics at the Aristotle University of Thessaloniki, her MS in Environmental Engineering at Technis

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Abstract

Though recognition of the importance of diversity and inclusion in engineering education has grown in recent years [1], little is known about the best practices for supporting neurodiverse students [2-3]. It has been suggested that neurodiverse students benefit from course assessments that allow for a more flexible mode of expressing knowledge [3]. However, evidence for improved learning outcomes on different types of course assessments is largely anecdotal. Characteristics associated with different forms of neurodiversity, such as attention deficit hyperactivity disorder (ADHD), autism spectrum, depression, and anxiety, are suggested to be normally distributed in the population [2]. Indeed, research suggests that these conditions are best conceptualized as dimensional [4-6] and that varying levels of these characteristics are associated with similar functional outcomes [7-8]. Thus, assessing how variation in neurodiverse characteristics of all students predicts performance on different types of engineering course assessments should help to shed light on how engineering faculty can support students who learn and think in different ways. To this end, undergraduate engineering students (N = 50) in a Soil Mechanics course participated in a study to determine if neurodiverse characteristics differentially predict performance on different types of course assessments. At the beginning of the Fall 2023 semester, students completed self-report assessments of neurodiverse characteristics (ADHD, autism spectrum, depression, and anxiety) and personal resources (self-efficacy, engagement, and motivation) using an online survey. Students also provided permission to record their grades on course assignments for analysis. Following the end of the semester, participating students’ scores were recorded for the following: (1) Average of scores for homework assignments; (2) Average of scores on quizzes; (3) Average of scores for each of three phases of the term project; (4) Average of scores for three mid-terms; (5) Score for class participation. Data will be analyzed using multiple regression models. The proposed paper will describe the course structure and design of the course assignments, which differ in their level of flexibility, as well as the results and conclusions of the analyses.

Citation
Format

Roy, M., & Taylor, C. L., & Chrysochoou, M. (2024, June), Neurodivergent Student Characteristics and Engineering Course Outcomes Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47804

TY  - CPAPER
AB  - Though recognition of the importance of diversity and inclusion in engineering education has grown in recent years [1], little is known about the best practices for supporting neurodiverse students [2-3]. It has been suggested that neurodiverse students benefit from course assessments that allow for a more flexible mode of expressing knowledge [3]. However, evidence for improved learning outcomes on different types of course assessments is largely anecdotal. Characteristics associated with different forms of neurodiversity, such as attention deficit hyperactivity disorder (ADHD), autism spectrum, depression, and anxiety, are suggested to be normally distributed in the population [2]. Indeed, research suggests that these conditions are best conceptualized as dimensional [4-6] and that varying levels of these characteristics are associated with similar functional outcomes [7-8]. Thus, assessing how variation in neurodiverse characteristics of all students predicts performance on different types of engineering course assessments should help to shed light on how engineering faculty can support students who learn and think in different ways. To this end, undergraduate engineering students (N = 50) in a Soil Mechanics course participated in a study to determine if neurodiverse characteristics differentially predict performance on different types of course assessments. At the beginning of the Fall 2023 semester, students completed self-report assessments of neurodiverse characteristics (ADHD, autism spectrum, depression, and anxiety) and personal resources (self-efficacy, engagement, and motivation) using an online survey. Students also provided permission to record their grades on course assignments for analysis. Following the end of the semester, participating students’ scores were recorded for the following: (1) Average of scores for homework assignments; (2) Average of scores on quizzes; (3) Average of scores for each of three phases of the term project; (4) Average of scores for three mid-terms; (5) Score for class participation. Data will be analyzed using multiple regression models. The proposed paper will describe the course structure and design of the course assignments, which differ in their level of flexibility, as well as the results and conclusions of the analyses.
AU  - Manish Roy
AU  - Christa L. Taylor
AU  - Maria Chrysochoou
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Neurodivergent Student Characteristics and Engineering Course Outcomes
UR  - https://peer.asee.org/47804
ER  -