Work-In-Progress: Continued evaluation of engineering self-efficacy and judgement for an artificial intelligence, modeling, and simulations (AIMS) certificate program

Samuel James Murphy; Martell Cartiaire Bell; Rachel Vitali; Jae-eun Russell

Download Paper | Permalink

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: MECH - Technical Session 9: Advanced Mechanical Engineering Topics
Tagged Division: Mechanical Engineering Division (MECH)
Tagged Topic: Diversity
Permanent URL: https://peer.asee.org/48531

Request a correction

Paper Authors

biography

Samuel James Murphy The University of Iowa

visit author page

Samuel Murphy is a doctoral candidate in the Mechanical Engineering department at the University of Iowa, where he also received his B.S.E in 2020 and M.S.E. in 2022. His research interests include engineering education and human activity recognition.

visit author page

biography

Martell Cartiaire Bell The University of Iowa

visit author page

I'm am a second year Ph.D student in Mechanical Engineering at the University of Iowa with a dual focus in engineering education and automation/artificial intelligence in manufacturing.

visit author page

biography

Rachel Vitali The University of Iowa orcid.org/0000-0002-1436-6148

visit author page

Dr. Rachel Vitali is an Assistant Professor in the Mechanical Engineering Department at the University of Iowa. Prior to her appointment, she was a NASA-funded TRISH postdoctoral fellow in the Industrial & Operations Engineering Department at the University of Michigan, where she also received her B.S.E. in 2015, M.S.E in 2017, and Ph.D. in 2019 from the Mechanical Engineering Department. As director of the Human Instrumentation and Robotics (HIR) lab, she
leads multiple lines of research in engineering dynamics with applications to wearable technology for analysis of human motion in a variety of contexts ranging from warfighters to astronauts. In addition to her engineering work, she also has an interest in engineering education research, which most recently has focused on incorporating authentic engineering educational experiences through engineering history education and open-ended modeling problems designed to initiate the productive beginnings of engineering judgement and engineering identity.

visit author page

biography

Jae-eun Russell orcid.org/0000-0001-5622-9451

visit author page

Dr. Russell serves as the Associate Director for the Office of Teaching, Learning & Technology at the University of Iowa. She completed her Ph.D. in Educational Psychology from the University of Iowa. Her research examines instructional practices that support successful student learning. Her research also involves autonomous motivation, self-regulated learning, technology adoption, and learning analytics adoption.

visit author page

Download Paper | Permalink

Abstract

As science, technology, engineering, and mathematics (STEM) continue to advance, engineering education for both undergraduate and graduate students need to be updated accordingly. Undergraduate students benefit from exposure to the state-of-the-art technologies and techniques to successfully join an ever-changing workforce. Graduate students also benefit from learning about cutting edge tools to succeed in their research goals and contribute to their respective research communities. The artificial intelligence, modeling, and simulation (AIMS) certificate program was created at a mechanical engineering program of a large midwestern university to help address these missing components. The goals of the program are for students to learn reliable computer simulation and design under uncertainty, gain an understanding of the new pathways to achieve robust and affordable modeling with artificial intelligence and machine learning, and become proficient in utilizing hybrid models toward intelligent complex machines. Most AIMS courses include projects throughout the semester that provide a hands-on component to implementing course material. These projects help students develop a deeper understanding of the material, contribute to improved retention of knowledge gained, and encourage collaboration amongst students. This work aims to explore how participation in AIMS courses affects the development of students' engineering self-efficacy (ESE) and engineering judgement (EJ), in addition to what other factors may be related to ESE and EJ development.

ESE is an individual’s belief regarding their ability to achieve a specific goal based on their engineering knowledge, in this case specifically related to the content covered by AIMS courses. Past literature shows how one’s beliefs about their ability can influence their behavior and goal attainment. EJ is an individual’s capacity to determine and execute tasks that will lead to a predicted outcome. Judgement about capabilities is typically developed in parallel to engineering problem solving. In this study, ESE and EJ are measured via an online survey distributed during AIMS courses. To date, responses have been collected from 63 undergraduate and 15 graduate students. The survey also asks about student participation in various workshops held on campus that teach diverse topics that cannot be covered in class due to time constraints. Topics range from interacting with research participants and their data to hands on skills (soldering, building robots, etc.) and programming in various coding languages.

The study will also account for other independent factors that may influence the relationship between participation in AIMS courses and student’s ESE and EJ including the student’s academic standing (undergraduate or graduate), student participation in extracurricular activities (yes or no), and student interest in the AIMS certificate in general (yes, maybe, or no). Data will be analyzed using multiple regression statistical analysis to extract themes relating to the students’ ESE and EJ. A project from an AIMS course will also be described in detail to demonstrate how project-based learning is implemented in course work. Future work includes the continued collection of survey data.

Citation
Format

Murphy, S. J., & Bell, M. C., & Vitali, R., & Russell, J. (2024, June), Work-In-Progress: Continued evaluation of engineering self-efficacy and judgement for an artificial intelligence, modeling, and simulations (AIMS) certificate program Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/48531

TY - CPAPER
AB - As science, technology, engineering, and mathematics (STEM) continue to advance, engineering education for both undergraduate and graduate students need to be updated accordingly. Undergraduate students benefit from exposure to the state-of-the-art technologies and techniques to successfully join an ever-changing workforce. Graduate students also benefit from learning about cutting edge tools to succeed in their research goals and contribute to their respective research communities. The artificial intelligence, modeling, and simulation (AIMS) certificate program was created at a mechanical engineering program of a large midwestern university to help address these missing components. The goals of the program are for students to learn reliable computer simulation and design under uncertainty, gain an understanding of the new pathways to achieve robust and affordable modeling with artificial intelligence and machine learning, and become proficient in utilizing hybrid models toward intelligent complex machines. Most AIMS courses include projects throughout the semester that provide a hands-on component to implementing course material. These projects help students develop a deeper understanding of the material, contribute to improved retention of knowledge gained, and encourage collaboration amongst students. This work aims to explore how participation in AIMS courses affects the development of students&#39; engineering self-efficacy (ESE) and engineering judgement (EJ), in addition to what other factors may be related to ESE and EJ development.

ESE is an individual’s belief regarding their ability to achieve a specific goal based on their engineering knowledge, in this case specifically related to the content covered by AIMS courses. Past literature shows how one’s beliefs about their ability can influence their behavior and goal attainment. EJ is an individual’s capacity to determine and execute tasks that will lead to a predicted outcome. Judgement about capabilities is typically developed in parallel to engineering problem solving. In this study, ESE and EJ are measured via an online survey distributed during AIMS courses. To date, responses have been collected from 63 undergraduate and 15 graduate students. The survey also asks about student participation in various workshops held on campus that teach diverse topics that cannot be covered in class due to time constraints. Topics range from interacting with research participants and their data to hands on skills (soldering, building robots, etc.) and programming in various coding languages.

The study will also account for other independent factors that may influence the relationship between participation in AIMS courses and student’s ESE and EJ including the student’s academic standing (undergraduate or graduate), student participation in extracurricular activities (yes or no), and student interest in the AIMS certificate in general (yes, maybe, or no). Data will be analyzed using multiple regression statistical analysis to extract themes relating to the students’ ESE and EJ. A project from an AIMS course will also be described in detail to demonstrate how project-based learning is implemented in course work. Future work includes the continued collection of survey data.
AU - Samuel James Murphy
AU - Martell Cartiaire Bell
AU - Rachel Vitali
AU - Jae-eun Russell
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Work-In-Progress: Continued evaluation of engineering self-efficacy and judgement for an artificial intelligence, modeling, and simulations (AIMS) certificate program
UR - https://peer.asee.org/48531
ER -