Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

tonghui xu, University of Massachusetts, Lowell; Hsien-Yuan Hsu, University of Massachusetts, Lowell

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Paper ID #41785Integrating Data-Driven and Career Development Theory-Driven Approachesto Study High School Student Persistence in STEM Career Aspirationstonghui xu, University of Massachusetts, Lowell PhD studentDr. Hsien-Yuan Hsu, University of Massachusetts, Lowell Dr. Hsien-Yuan Hsu is an Assistant Professor in Research and Evaluation in the College of Education at the University of Massachusetts Lowell. Dr. Hsu received his PhD in Educational Psychology from Texas A&M University and has a background of statistics ©American Society for Engineering Education, 2024 Integrating Features Selection

Conference Session

DSA Technical Session 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Betul Bilgin, The University of Illinois at Chicago; Naomi Groza, The University of Illinois at Chicago

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

ReadinessIn the rapidly evolving landscape of chemical engineering (CHE), the incorporation of datascience has gained increasing importance. To equip students with the skills required for a data-driven industry, it is crucial to understand their perceptions of data science and their willingnessto embrace it in their academic and professional journey. This study engages a diverse group ofchemical engineering students across different academic levels to explore their viewpoints ondata science and its potential integration into the academic curriculum.The instrument assesses four crucial constructs: interest, career aspirations, perceived value, andself-efficacy regarding data science. The study delves into students' prior exposure to datascience

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Nicolas Leger, Florida International University; Maimuna Begum Kali, Florida International University; Stephanie Jill Lunn, Florida International University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Opportunities; 2) Ongoing Professional Development and Upskilling; and 3)Practical Applications. As such, it can provide opportunities for career preparedness, fosteringnew competencies, and a need to gain hands-on experience using data science to create value andsolve problems. The results of this work can have important implications for educators,administrators, and professionals looking to incorporate data science into engineering praxis.Keywords: Data Science, Non-Computing Engineers, Technology Acceptance Model, Reddit,LDA, Web Scraping1. IntroductionData science is an interdisciplinary field that involves extracting knowledge and insights fromdata (i.e., a collection of information or facts) using scientific methods, algorithms, and tools [1].It

Conference Session

DSA Technical Session 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Xiang Zhao, Alabama A&M University; Mebougna L. Drabo, Alabama A&M University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

;M University © American Society for Engineering Education, 2024is presented. The findings and lessons learned from this study are also presented with theintention to share our experience with the instructors and administrators to advance data scienceeducation at MSIs/HBCUs.Related WorkIn the past decade, educators and researchers realized the importance of data analytics intransforming STEM education. It was shown by Maier-Hein et al. [6] that incorporating dataanalytics and exposing students to real-world datasets improved their critical thinking. Moreimpressively, data science education encourages students to explore STEM careers and alsoprovides a strong foundation for further education and future employment

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Emily Nutwell, The Ohio State University; Thomas Bihari, The Ohio State University; Thomas Metzger, The Ohio State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

scienceconcepts in both didactic and experiential settings. Students appreciate the need to successfullycommunicate with data and be effective data storytellers but will often feel frustrated that datastorytelling skills are not “real data science.” An analysis of LinkedIn profiles indicates that over60% of graduated learners secured new employment in data careers since starting the program.To build on this success, further curriculum development should more explicitly connectfundamental data science concepts and broader concepts such as creative problem-solving anddata storytelling.KeywordsGraduate education, data analytics, distance learning, life-long learning, adult learning1. IntroductionWe are living in an era where the Volume, Velocity, Veracity

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Karl D. Schubert FIET, University of Arkansas; Shantel Romer, University of Arkansas; Stephen R. Addison, IEEE Educational Activities; Tina D Moore; Laura J Berry, North Arkansas College; Jennifer Marie Fowler, Arkansas State University; Lee Shoultz, University of Arkansas; Christine C Davis

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

use the phrase “Start Anywhere, Finish Anywhere” to help people internalizethe vision. Over the next year, as we began to build the ecosystem, we formalized our vision andmission as part of an NSF EPSCoR (Established Program to Stimulate Competitive Research)Grant (NSF EPSCoR, 2020): Vision A statewide educational ecosystem where learners receive a designed, consistent, scaffolded education in data science with further educational and/or job opportunities available at appropriate points in their careers. Mission Create a model Data Science and Analytics program for our state’s schools to promote problem-based and experiential-based pedagogy in critical

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Isil Anakok, Virginia Polytechnic Institute and State University; Kai Jun Chew, Embry-Riddle Aeronautical University, Daytona Beach; Holly M Matusovich, Virginia Polytechnic Institute and State University; Andrew Katz, Virginia Polytechnic Institute and State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Supervisor Spotlight Award in 2014, received the College of Engineering Graduate Student Mentor Award in 2018, and was inducted into the Virginia Tech Academy of Faculty Leadership in 2020. Dr. Matusovich has been a PI/Co-PI on 19 funded research projects including the NSF CAREER Award, with her share of funding being nearly $3 million. She has co-authored 2 book chapters, 34 journal publications, and more than 80 conference papers. She is recognized for her research and teaching, including Dean’s Awards for Outstanding New Faculty, Outstanding Teacher Award, and a Faculty Fellow. Dr. Matusovich has served the Educational Research and Methods (ERM) division of ASEE in many capacities over the past 10+ years including

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Mehmet Ergezer, Wentworth Institute of Technology; Mark Mixer, Wentworth Institute of Technology; Weijie Pang, Wentworth Institute of Technology

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

courses, such as Analysis ofAlgorithms, will not only enrich the curriculum but also pave the way for students to explore softwareengineering career paths. Simultaneously, Math courses, including Multivariable Calculus, are strategicallyincorporated to enhance quantitative reasoning skills and provide a solid foundation for advanced DataScience concepts. Furthermore, students delve into domain-specific courses, such as Machine Learning,and See it and Say it with Data Viz, to acquire specialized knowledge that aligns with the evolvinglandscape of Data Science applications. This multifaceted approach reflects our commitment to equippingBSDS students with a diverse skill set, ensuring they are well-prepared for the intricacies of the field

Conference Session

DSA Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Duncan Davis, Northeastern University; Nicole Alexandra Batrouny, Northeastern Univeristy; Adetoun Yeaman, Northeastern University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

ChatGPT by first year studentson coding assessments, as well as students’ reasoning about why they choose to use (or not use)AI within an introductory engineering course. Future work could continue to characterizeproductive and unproductive usage of AI by first year students, or by students throughout theiracademic careers. As AI becomes increasingly prevalent in our daily lives, student proficiencywith the tool will likely change, inviting more study. Additionally, research could explorecurricular interventions to teach students how to use AI as a learning aid. We are also curiousabout the long-term impacts of ChatGPT usage on student learning trajectories (e.g. do studentsthat use ChatGPT for coding get hired at the same rate, how do they

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Abdulrahman Alsharif, Virginia Polytechnic Institute and State University; Andrew Katz, Virginia Polytechnic Institute and State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

Katz et al. [20] combined textembedding models and generative text models to analyze over 1,000 career interest essays fromundergraduate engineering students. They found that their model could self-evaluate theaccuracy of its cluster labeling, with 86-93% agreement with human raters. Their results showNLP and LLM methods can automatically analyze unstructured text to gain insights into studentexperiences [20]. Another application that applied GAI in clustering labels after coupling it withNLP. The approach followed an NLP traditional method which was applied to make theclustering process of students’ responses and then GAI model (GPT-3.5) labeled these clusters[21]. This approach resulted in more concise cluster labeling in comparison to other

Conference Session

DSA Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Aidan Kenny, Northeastern University; Andrew L Gillen, Northeastern University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

elements withinthe system, connected by lines that represent a variety of relationships. Given its usefulness inunderstanding intricate systems, it should be helpful in mapping the engineering educationprocess. A huge number of factors affect the education of new engineers. From elementaryschool to graduate school, students are exposed to STEM curriculum, experiential learning,career development, and other external factors that contribute to them becoming an engineer.Having a systemogram that compiles this information could be used by students, teachers,professors, and administrators to refine the system for everyone’s benefit. The systemogram ofthe engineering education system is shown below in Figure 6.Figure 6: Systemogram of student flow

Conference Session

DSA Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Tushar Ojha, University of New Mexico; Don Hush, University of New Mexico

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

-transfer-students-earn-bachelors-degrees- excess-credits.pdf.[10] J. J. Giesey and B. Manhire. An analysis of bsee degree completion time at ohio university. Journal of Engineering Education, 92(3):275–280, 2003.[11] S. K. Hargrove and D. Ding. An Analysis of B.S.I.E. Degree Completion Time at Morgan State University. In International Conference on Engineering Education. International Network for Engineering Education and Research, October 2004.[12] M. M. Hossain and M. G Robinson. How to motivate us students to pursue stem (science, technology, engineering and mathematics) careers. Online Submission, 2012.[13] D. R. Hush, E. S. Lopez, W. Al-Doroubi, T. Ojha, B. Santos, and K. Warne. Analyzing student credits. 2022

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Neha Kardam, University of Washington; Denise Wilson, University of Washington

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

tocontribute to the development of educational research methodologies. It emphasizes the potentialcollaboration between automated coding systems and human expertise in interpreting studentfeedback data.Literature ReviewOver 16 million people are enrolled as undergraduates in colleges and universities in the US [6].Understanding the lived experiences of these students on a broad scale including their satisfactionwith their education, learning outcomes, and intentions to persist in their careers requireseducation-based research that extends beyond the standard Likert-scale questions on surveys andstudent evaluations of teaching [1]. Augmenting surveys with short answer questions allowsresearchers and instructors to more effectively and more thoroughly

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Safia Malallah, Kansas State University; Ejiro U Osiobe, Baker University; Zahraa Marafie, Kuwait University; Patricia Henriquez-Coronel; Lior Shamir, Kansas State University; Ella Lucille Carlson, Kansas State University; Joshua Levi Weese, Kansas State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Conference Session

DSA Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Yiming Zhang, The University of Arizona; Gregory L. Heileman, The University of Arizona; Ahmad Slim, The University of Arizona; Husain Al Yusuf, The University of Arizona

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

education, many students are choosing to transfer between institutions due to variousreasons, such as financial considerations, educational goals, career aspirations and so on. It hasbeen widely acknowledged that providing academic support to transfer students is a challengingtask due to the complexity of the transfer process and students’ background. Based on the study ofthe mechanics of the transfer process, we have gained a better understanding of the root cause ofthe challenge.As the transfer process can be treated as a computable problem, we proposed a tree data structurethat can represent the degree requirements of academic program. To study the complexity of thetransfer process, we formally defined the Optimal Transfer Pathway (OTP) Problem