Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Ashraf Badir, Florida Gulf Coast University; Ahmed S. Elshall, Florida Gulf Coast University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

, Florida Gulf Coast University Ahmed S. Elshall (https://orcid.org/0000-0001-8200-5064) is an Assistant Professor in the Department of Bioengineering, Civil Engineering, and Environmental Engineering at Florida Gulf Coast University, with a joint appointment at The Water School. His research focuses on sustainable groundwater management under uncertainty. He teaches courses in groundwater hydrology and environmental data science. ©American Society for Engineering Education, 2025 Data Science in Environmental Engineering CurriculumAbstractData science is increasingly integral to various STEM domains, offering promising careeropportunities across diverse engineering applications. Several

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

integration of analytics tools fostered the engineering students the ability to forecast require-ments and create new methods critical to their engineering design.Data analytics was also added to a core course on product manufacturing in the industrial engi-neering curriculum [7]. The pedagogical method was developed by first analyzing and compar-ing product manufacturing processes and data analytics techniques. Then the result of this anal-ogy was used to develop a teaching and learning method for data analytics. For implementationand validation purposes, a Project Based Learning (ProjBL) approach was adopted, in which stu-dents used the methodology to complete real-world data analytics projects. Data from students'grades shows that this approach

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

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Diversity

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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

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Diversity

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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 5: Educational Technology and Innovative Tools

Collection

2025 ASEE Annual Conference & Exposition

Authors

Handan Liu, Northeastern University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #46592A Unique Course Designed for Graduate Students: Integrating High-PerformanceParallel Computing into Machine Learning and Artificial IntelligenceDr. Handan Liu, Northeastern University Handan Liu is a Full Teaching Professor of Multidisciplinary Master of Science (MS) programs (Software Engineering, Data Architecture, Information Systems) in the College of Engineering at Northeastern University. Her research interests include heterogeneous high-performance computing, programming structure and algorithms, machine learning and AI, NLP research and development, LLM reasoning and AI agent in engineering courses

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

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Diversity

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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

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Diversity

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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

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

Collection

2025 ASEE Annual Conference & Exposition

Authors

Carrie Grace Aponte, Kansas State University; Safia Malallah, Kansas State University; Lior Shamir, Kansas State University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

science courses bring them together and show the connections betweenthe concepts. Many new practices are also introduced in these data science courses, includingdata scraping, data cleaning, unsupervised machine learning, writing functions, and chainingfunctions. This shows that data science holds value as a standalone subject, separate fromstatistics, mathematics, or other subjects.Integration into Existing Courses The nature of K-12 curriculum and schooling does not easilyallow for the creation of an entirely new course focused on data science, largely due to timelimitations. The integration of data science into existing courses can be an efficient way to botheducate students about data science and show practical applications for the concepts

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

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Diversity

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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

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

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Diversity

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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

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Diversity

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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

Paromita Nath, Rowan University; Melanie Amadoro, Rowan University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

general structure and the different components of a concept map. The node ”central concept”in Figure 1 represents the main concept and the surrounding nodes represent the relatedsub-concepts 1 .Concept maps have been shown to be an effective tool in the learning process, allowing studentsto connect related ideas and increasing student understanding of a topic 3,4 . Concept mapping hasbeen demonstrated to be beneficial in higher education. The use of concept maps to help studentsunderstand the structure of a mechanical engineering curriculum and the relationships betweensequences of courses has been investigated 5 . This study discussed concept mapping as a tool toimprove the effectiveness of lectures and to help students achieve a greater depth

Conference Session

DSAI Technical Session 10: Research Infrastructure and Institutional Insights

Collection

2025 ASEE Annual Conference & Exposition

Authors

Pallavi Singh, University of South Florida; Joel Howell; Joshua Karl Thomas Ranstrom, University of South Florida; Wilfrido A. Moreno P.E., University of South Florida

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

the needs of government andmilitary research initiatives [6][4][3]. While this transition addressed critical national priori-ties, it also introduced a gap between academic preparation and the practical expectations ofindustry. In response, there is presently a renewed emphasis on developing industry-readyengineers by integrating experiential learning and professional competency development intothe curriculum. The Professional Formation of Engineers (PFE) program at the University ofSouth Florida (USF) aligns with this contemporary shift by equipping students with real-worldskills, ethical foundations, and structured career development practices rooted in experientiallearning. Model-Based Systems Engineering (MBSE) approaches further support

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

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Diversity

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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

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Kristina A Manasil, The University of Arizona; Gregory L. Heileman, The University of Arizona; Melika Akbarsharifi, The University of Arizona; Roxana Akbarsharifi, The University of Arizona; Aryan Ajay Pathare, The University of Arizona

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

reputation, accreditation, and the ability to secure fund-ing. These rates also have a broader socioeconomic implication as they impact an individual’spotential financial stability, as well as strengthen the general labor market by providing an influxof qualified professionals. However, many students fail to achieve graduation in a timely mannerdue to a multitude of factors that extend beyond repeating courses or poor academic performance.Although changes in curriculum and program requirements often require significant time to designand implement, this study adopts a more student-focused approach to provide immediate inter-ventions aimed at supporting successful student outcomes. Recognizing the importance of timelygraduation, this study aims to