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)

]. The existing literature, though in its nascent stage,has started to uncover several dimensions of GAI’s influence on assessment, highlighting thetransformative potential of GAI in education alongside ethical considerations and the necessityfor responsible implementation strategies [6], [7], [8].Herein, we present a portion of a larger study on engineering faculty members’ mental models ofassessment in the era of GAI. The overarching question for this study is:RQ: How do engineering faculty members’ responses to the arrival of GAI in their assessmentpractices vary based on their demographics?By answering this research question, we aimed to explore if there are trending responses acrosscertain demographics as a start of our study. The findings

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Duo Li, Shenyang Institute of Technology; Elizabeth Milonas, New York City College of Technology; Qiping Zhang, Long Island University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

College of Technology - City University of New York (CUNY). She currently teaches relational and non-relational databases and data science courses to undergraduate students. She holds a BA in Computer Science and English Literature from Fordham University, an MS in Information Systems from New York University, and a Ph.D. from Long Island University. Her research interests focus on three key areas: data science curriculum and ethics, retention of minority students in STEM degree programs, and organization and classification of big data.Dr. Qiping Zhang, Long Island University Dr. Qiping Zhang is an Associate Professor in the Palmer School of Library and Information Science at the C.W. Post Campus of Long Island

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

engineering design, collaboration in engineering, decision making in engineering teams, and elementary engineering education.Dr. Adetoun Yeaman, Northeastern University Adetoun Yeaman is an Assistant Teaching Professor in the First Year Engineering Program at Northeastern University. Her research interests include empathy, design education, ethics education and community engagement in engineering. She currently teaches Cornerstone of Engineering, a first-year two-semester course series that integrates computer programming, computer aided design, ethics and the engineering design process within a project based learning environment. She was previously an engineering education postdoctoral fellow at Wake Forest University

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)

The ability to formulate well-defined questions, Domain Knowledge - Q1-Q6, Q19-Q20, creating a road map for successful project execution, Scientific Research Q34, Q42, Q47 while incorporating critical thinking, strategic Knowledge & Ethic Researching and 8 Knowledge. Planning Skill reasoning, and the ability to navigate, follow, and evaluate both the process and the outcome The capability to comprehend and utilize statistical Statistical Proficiency Q16, Q18, Q20-Q23, Analysis

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Paula Francisca Larrondo, Queen's University; Brian M Frank P.Eng., Queen's University; Julian Ortiz, Queen's University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

differentproblem scenarios: model and construction of a scale prototype of a Hyperloop vehicle [46] runin the 2019 academic year and create a working prototype of an assistive robotic arm, done in the2022 academic year. After ethics approval, text was extracted from PDF and Word documents,followed by minor preprocessing and splitting using the spaCy library 1.Annotation and Dataset Preparation:Manual annotation was initially performed by one of the authors, and ambiguous cases werediscussed with the course instructor. Human annotation is a time and resource-consuming taskthat can limit the size of the fine-tuning dataset. To expedite annotation validation, we employedGPT version 4.0 for comparison, addressing discrepancies between human and GPT

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)

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

coding and thematic analysis. BothNMF and PCA demonstrated high levels of agreement with domain expert coding, as indicatedby Cohen’s Kappa analysis. Additionally, NMF exhibited higher recall rates in capturing positiveinstances, while PCA showed better precision and overall balance between precision and recall.Moving forward, further research is necessary to refine these NLP techniques for educationalcontexts and to optimize the role of the domain expert in the hybrid approach. Additionally,ethical considerations surrounding the use of NLP in educational research, such as studentprivacy and potential biases within algorithms, should be addressed in future work. This paper,however, has laid additional groundwork for implementing NLP techniques

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

, andinterpreting the findings in the context of existing literature and the study's objectives. The studyadhered to ethical guidelines, ensuring the confidentiality and anonymity of participants.Informed consent was obtained from all participants, and they were informed of their right towithdraw from the study at any time without penalty.Results and DiscussionsTo analyze the qualitative data obtained from the interviews, a coding system was established tocategorize responses according to the four constructs outlined in the study: Interest, CareerAspirations, Perceived Value, and Self-Efficacy regarding data science. Beyond exploring thefour primary constructs, students were also queried about their understanding of definition ofdata science, the current

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

and experience. While this can create challenges for all participants, the typicallearner has strong expertise in their chosen field, strong work ethic, and considerable maturity asadult learners which serves them well in graduate programming [29], [30]. This leads toproductive discussions of practical applications of data analytics, learners helping other learners,and unexpected insights.The expectation that a learner who has never written code in any programming language canlearn Python in one semester is a high bar. For example, a learner might feel that code that is“almost” right (e.g., using a semi-colon instead of a colon in a Python “if” statement) should“almost” work. This can lead to frustration.Given this frustration, it is