Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

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

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #46030Shaping Future Innovators: A Curriculum Comparison of Data Science Programsin Leading U.S. and Chinese InstitutionsDr. Elizabeth Milonas, New York City College of Technology Elizabeth Milonas is an Associate Professor with the Department of Computer Systems at New York City 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

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Karl D. Schubert FIET, University of Arkansas; Carol S Gattis, University of Arkansas; Stephen R. Addison, University of Central Arkansas; Tara Jo Dryer, University of Arkansas; Adam Musto, Arkansas Department of Education

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

across institutions.As an example, the 8-semester degree plan below illustrates a specific articulation agreementbetween North Arkansas College (NorthArk), a 2-year institution, and the UA, a 4-year institution.Students complete their first four semesters at NorthArk, fulfilling foundational data science,mathematics, programming, and general education requirements, before transferring to UA for thefinal four semesters to complete advanced coursework and capstone experiences.To support seamless integration, instructors at participating 2-year colleges can utilize existing UAteaching materials, ensuring consistency in course delivery. Additionally, faculty trainingworkshops are conducted to close knowledge gaps among instructors at 2-year colleges

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Md. Yunus Naseri, Virginia Polytechnic Institute and State University; Vinod K. Lohani, Virginia Polytechnic Institute and State University; Manoj K Jha P.E., North Carolina A&T State University; Gautam Biswas, Vanderbilt University; Caitlin Snyder; Steven X. Jiang, North Carolina A&T State University; Caroline Benson Sear, Virginia Polytechnic Institute and State University

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

. Darner Gougis, “Analysis of high‐ frequency and long‐term data in undergraduate ecology classes improves quantitative literacy,” Ecosphere, vol. 8, no. 3, p. e01733, 2017.[6] A. Bundy, “Australian and New Zealand Information Literacy Framework,” Australian and New Zealand Institute for Information Literacy, Adelaide, 2004.[7] D. Deb, M. Fuad, and K. Irwin, “A Module-based Approach to Teaching Big data and Cloud Computing Topics at CS Undergraduate Level,” in Proceedings of the 50th ACM Technical Symposium on Computer Science Education, ACM, 2019. doi: 10.1145/3287324.3287494.[8] K. Hunt, “The challenges of integrating data literacy into the curriculum in an undergraduate institution,” IASSIST Q., vol. 28, no. 2–3, p

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Michael T Johnson, University of Kentucky; Johné M Parker, University of Kentucky

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

, advising, and success coaching. Given the extremely low 4-yeargraduation rate for students who start their academic careers in pre-calculus, there is also clear support fordevelopment of 5-year curriculum plans for this group of students, which at UK PCOE makes up about one-fourth of the overall incoming freshman class each year, and nearly 35% of URM and first-generationstudents. Finally, the indicators from Table 13 can be used for early identification of students who may havea higher chance of academic success in fields outside of engineering, to make sure they are either fullycommitted to pursuing an academic career in engineering or provided with good information for consideringalternative career directions.References[1] U. S. D. o. Labor

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Cristian Saavedra-Acuna, Universidad Andres Bello, Concepcion, Chile; Monica Quezada-Espinoza, Universidad Andres Bello, Santiago, Chile; Danilo Alberto Gomez, Universidad Andres Bello, Concepcion, Chile

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

students as of the first semester of2024, underwent rigorous preprocessing. This included the normalization and transformationof 36 predictive variables (detailed in Appendix A) to ensure data quality and homogeneitybefore integrating them into the predictive models.The models selected for evaluation, Gradient Boosting Regressor (GBR), Random ForestRegressor (RF), AdaBoost Regressor (ADA), K-Neighbors Regressor (KNN), and LinearRegression (LR), were chosen for their flexibility in capturing non-linear relationships andtheir adaptability to various data patterns. The methodology involved an initial split of thedata into training (80%) and testing (20%) sets, along with a 10-fold cross-validation schemeto ensure stability and representativeness of

Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ibukun Samuel Osunbunmi, Pennsylvania State University; Taiwo Raphael Feyijimi, University of Georgia; Lexy Chiwete Arinze, Purdue University at West Lafayette (COE); Viyon Dansu, Florida International University; Bolaji Ruth Bamidele, Utah State University; Yashin Brijmohan, Utah State University; Stephanie Cutler, The Pennsylvania State University

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

become an essential toolfor academic and professional growth. Over the past couple of years, the use of GenerativeArtificial Intelligence (GAI) in academia has been the subject of several debates, with discussionsfocusing on its ethical implications and how to use it to aid teaching and learning effectively. AsGAI technologies become increasingly prevalent, raising awareness about their potential uses andestablishing clear guidelines and best practices for their integration into academic settings isessential. Without proper understanding and frameworks in place, the misuse or over-reliance onthese tools could undermine the educational goals they aim to support. Workshops and seminarsplay a critical role in addressing these concerns by not only

Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Indu Varshini Jayapal, University of Colorado Boulder; James KL Hammerman; Theodora Chaspari, University of Colorado Boulder

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

many instructors feeling unprepared to teachethics-related concepts. This gap risks fostering a workforce that develops AI technologies with-out adequately considering responsible and ethical practices, potentially leading to serious societalconsequences. Here, we present results from a pilot curriculum that integrates various ethical top-ics related to AI into a graduate-level machine learning course. Activities include a combination ofcase studies, project-based learning, and critical classroom discussions on the ethical implicationsof AI systems design and deployment.Two research questions guided the study: (RQ1) How do computer science graduate studentsperceive ethical issues in AI design and implementation before taking the class? (RQ2

Conference Session

DASI Technical Session 2: Artificial Intelligence in Higher Education

Collection

2025 ASEE Annual Conference & Exposition

Authors

Lauren Singelmann, Minnesota State University, Mankato; Jack Elliott, Minnesota State University, Mankato; Yuezhou Wang, Minnesota State University, Mankato; Jacob John Swanson, Minnesota State University, Mankato

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

Rishi Sunak. Similarly, onestudent group tested ChatGPT’s ability to solve an integral by simply asking it, “Can you takethe integral of a function?” and trusting it when it responded that it could. Some students didparticipate in more thorough fact checking, but a lack of fact checking was more widespread inthe student group than in the other groups. Industry professionals, on the other hand, seemedmore likely to verify their evaluation using primary sources such as Wolfram Alpha to check theintegral and Google to fact check information.3. Hallucinations, especially in the context of citations - Overall, participants from all groupswere more skeptical about the citations ChatGPT was providing – with a handful of students andindustry

Conference Session

DSAI Technical Session 4: Workshops, Professional Development, and Training

Collection

2025 ASEE Annual Conference & Exposition

Authors

Melika Akbarsharifi, The University of Arizona; Ahmad Slim; Gregory L. Heileman, The University of Arizona; Roxana Akbarsharifi, The University of Arizona; Kristina A Manasil, The University of Arizona

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

currently serves as the Associate Vice Provost for Academic Administration and Professor of Electrical and Computer Engineering at the University of Arizona, where he is responsible for facilitating collaboration across campus tRoxana Akbarsharifi, The University of Arizona Roxana Akbarsharifi is a PhD student in Software Engineering at the University of Arizona. Her research focuses on educational analytics and developing tools to improve student outcomes and support academic success. Her research interests include software engineering, data analytics, and data visualization, with an emphasis on applying these technologies to solve educational challenges and enable data-driven decision making in higher

Conference Session

DSAI Technical Session 1: K–12 and Early Exposure to Data Science and AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Faiza Zafar, Rice University; Carolyn Nichol, Rice University; Matthew Cushing, Rice University

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

participants collaborate with graduate studentmentors, engage in discussions with faculty members engaged in digital health research, explorereal datasets, and create grade-appropriate lesson plans. This paper focuses on the overallprogram design and the experiences of an elementary STEM teacher who participated in theprogram and implemented the lesson with her students. Literature ReviewArtificial Intelligence (AI) and Machine Learning (ML) in Elementary Curriculum The integration of AI and ML into elementary education is an emerging area of interestthat has the potential to equip young learners with foundational skills critical for the future [1].As technology continues to evolve, it is becoming

Conference Session

DSAI Technical Session 10: Research Infrastructure and Institutional Insights

Collection

2025 ASEE Annual Conference & Exposition

Authors

Jordan Esiason, SageFox Consulting Group; Talia Goldwasser, SageFox Consulting Group; Rebecca Zarch, SageFox Consulting Group; Alan Peterfreund, SAGE

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #46083Providing engineering education researchers and stakeholders with easy accessto granular, disparate data sourcesJordan Esiason, SageFox Consulting Group Jordan Esiason has been working in STEM education research since 2018. He has been awarded an NSF CSGrad4US Fellowship and is currently pursuing a doctorate in computer science. Jordan’s current work includes developing data visualization tools for researchers, as well as tools for affect-responsive game-based learning environments. His interests broadly involve applying data mining and machine learning techniques such as natural language processing and

Conference Session

DSAI Technical Session 9: Student Reflections, Metacognition, and Competency Mapping

Collection

2025 ASEE Annual Conference & Exposition

Authors

Majd Khalaf, Norwich University; Toluwani Collins Olukanni, Norwich University; David M. Feinauer P.E., Virginia Military Institute; Michael Cross, Norwich University; Ali Al Bataineh, Norwich University

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

worked remotely on an inter-university team design project. Theproject was implemented in Spring 2023 and repeated in Spring 2024. At the end of theendeavor, the students completed an end-of-project survey and wrote a reflection about theexperience.Following the initial project offering, the authors employed Natural Language Processing (NLP)techniques to analyze the student reflections. Three unsupervised learning techniques (K-meansclustering, Latent Dirichlet Allocation (LDA), and Non-Negative Matrix Factorization) wereutilized to identify key themes in the student responses and categorize the topics or themescommon among the responses. Preliminary findings based on the Spring 2023 data revealed a setof five common and distinctive themes or