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Data Science in Environmental Engineering Curriculum

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
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

Paper ID #45388Data Science in Environmental Engineering CurriculumProf. Ashraf Badir, Florida Gulf Coast University Dr. Badir is a Professor in the Bioengineering, Civil Engineering, and Environmental Engineering Department at the U.A. Whitaker College of Engineering in Florida Gulf Coast University. He earned his B.Sc. (1982) in Civil Engineering and M.Sc. (1985) in Structural Engineering. He also holds a M.Sc. (1989) and a Ph.D. (1992) in Aerospace Engineering from Georgia Institute of Technology. Dr. Badir is a licensed Professional Engineer in Florida, and a civil engineering program evaluator for ABET.Ahmed S. Elshall

Relevance Score: 0.5058043

A Review of K-12 Data Science Education in the United States: Trends, Tools, and Gaps

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
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

work and do notreflect the views of the NSF.References [1] World Economic Forum, “How to address disinformation,” October 2022. Accessed: 2025-01-13. [2] C. Engledowl and T. Weiland, “Data (Mis)representation and COVID-19: Leveraging Misleading Data Visualizations For Developing Statistical Literacy Across Grades 6–16,” Journal of Statistics and Data Science Education, vol. 29, pp. 160–164, Aug. 2021. Publisher: Taylor & Francis eprint: https://doi.org/10.1080/26939169.2021.1915215. [3] S. Yeom, “Teaching and assessing data literacy for adolescent learners,” in Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies, pp. 93–123, IGI Global, 2021. [4] K. Janacsek, J. Fiser, and D. Nemeth

Relevance Score: 0.025258001

Bringing Artificial Intelligence (AI) and Machine Learning (ML) into Elementary Classrooms

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

-González, M., & Robles, G. (2020b). LearningML: A Tool to Foster Computational Thinking Skills through Practical Artificial Intelligence Projects; Revista de Educación a Distancia; 20(63).[5] Rodríguez-García, J. D., Moreno-León, J., Román-González, M., & Robles, G. (2021). Evaluation of an online intervention to teach artificial intelligence with learningML to 10-16-year-old students. In Proceedings of the 52nd ACM technical symposium on computer science education (pp. 177–183).[6] Sakulkueakulsuk, B. S.;Witoon, P. Ngarmkajornwiwat, P. Pataranutaporn, W. Surareungchai, P. Pataranutaporn, P. Subsoontorn (2018). “Kids making AI: Integrating Machine Learning, Gamification, and Social Context in STEM

Relevance Score: 0.018707491

Deepening Insights from Learning Analytics through Student Perspectives

Conference Session: DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Selena Johnson, Rowan University; Paromita Nath, Rowan University; Smitesh Bakrania, Rowan University
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

083–12 121, 2022. [4] C. Guzm´an-Valenzuela, C. G´omez-Gonz´alez, A. Rojas-Murphy Tagle, and A. Lorca-Vyhmeister, “Learning analytics in higher education: a preponderance of analytics but very little learning?” International Journal of Educational Technology in Higher Education, vol. 18, pp. 1–19, 2021. [5] B. Rienties, Q. Nguyen, W. Holmes, and K. Reedy, “A review of ten years of implementation and research in aligning learning design with learning analytics at the open university uk,” Interaction Design and Architecture (s), vol. 33, pp. 134–154, 2017. [6] A. S. Alzahrani, Y.-S. Tsai, S. Iqbal, P. M. M. Marcos, M. Scheffel, H. Drachsler, C. D. Kloos, N. Aljohani, and D. Gasevic, “Untangling connections between challenges

Relevance Score: 0.017077526

Can LLMs Assist with Education Research? The Case of Computer Science Standards Analysis

Conference Session: DSAI Technical Session 10: Research Infrastructure and Institutional Insights
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Julie M. Smith; Jacob Koressel; Sofia De Jesus, Carnegie Mellon University; Joseph W Kmoch; Bryan Twarek
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

provided (incorrect and incomplete) Python code todetermine the categorization of the standards instead of performing the categorization itself. WeFigure 2: Llama performance. Key: H → human, AI → Llama, I → identical, D → different, S →similar, B → based on.reprompted it to generate usable results.Figures 2, 3, and 4 summarize the performance of each LLM. Mismatches with the human coderoccurred in about half of the instances: Llama (n = 46), Claude (n = 52), and ChatGPT(n = 41). However, when the LLM had a match with the human coder, it usually had the sameverdict as the human coder because it categorized the level of similarity in the same way as thehuman expert. However, and perhaps surprisingly, there were some instances where the

Relevance Score: 0.012808144

Automating Structured Information Extraction from Images of Academic Transcripts Using Machine Learning

Conference Session: DSAI Technical Session 6: Academic Success, Performance & Complexity
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Declan Kirk Bracken, University of Toronto; Sinisa Colic Ph.D., University of Toronto
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

, noisy text, and reorganize large sequences of strings into a columnar struc-ture. These results suggest that with more data and continuous improvement, these systems couldbe implemented to greatly support the admissions process in the future.References [1] R. Avyodri, S. Lukas, and H. Tjahyadi, “Optical character recognition (ocr) for text recogni- tion and its post-processing method: A literature review,” in 2022 1st International Confer- ence on Technology Innovation and Its Applications (ICTIIA), 2022, pp. 1–6. [2] S. Paliwal, V. D, R. Rahul, M. Sharma, and L. Vig, “Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images,” CoRR, vol. abs/2001.01469, 2020. [Online

Relevance Score: 0.012502071

Leveraging Large Language Models for Early Study Optimization in Educational Research

Conference Session: DSAI Technical Session 7: Natural Language Processing and LLM Applications
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Mikayla Friday, University of Connecticut; Michael Thomas Vaccaro Jr, University of Connecticut; Arash Esmaili Zaghi P.E., University of Connecticut
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

that this paper serves as apractical guide for using LLMs for the simulation and early optimization of experimental designsacross disciplines.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under MCAGrant No. 2120888. The first and second authors (MF and MV) were supported by an NSFResearch Traineeship (TRANSCEND) under Grant No. 2152202 at the time this research wasconducted. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.References[1] B. Dong, J. Bai, T. Xu and Y. Zhou, "Large Language Models in Education: A Systematic Review," in 2024 6th International

Relevance Score: 0.012502071

A Predictive Model for Academic Performance in Engineering Students

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

-Aguilar, P. R. Álvarez-Pérez, and P. A. Toledo- Delgado, "Dropping out of higher education: Analysis of variables that characterise students who interrupt their studies," Acta Psychologica, vol. 252, p. 104669, Feb. 2025, doi: 10.1016/j.actpsy.2024.104669.[2] Cruz L., Li T., Ciner L., Douglas K., Greg C., (2022) Predicting learning outcome in a first-year engineering course: a human-centered learning analytics approach. Recuperado de: https://peer.asee.org/predicting-learning-outcome-in-a-first-year- engineering-course-a-human-centered-learning-analytics-approach.pdf[3] G. Bilquise, S. Abdallah, and T. Kobbaey, "Predicting Student Retention Among a Homogeneous Population Using Data Mining," in Proceedings

Relevance Score: 0.011455951

Can AI Transform Graduate Computer Science Admissions? Preemptive Bias Detection in Automated Review Systems

Conference Session: DASI Technical Session 2: Artificial Intelligence in Higher Education
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Ananya Prakash, Virginia Polytechnic Institute and State University; Mohammed Seyam, Virginia Polytechnic Institute and State University
Tagged Topics: Diversity
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

Relevance Score: 0.011194413

Quantifying the Effects of Concept Maps on Student Learning

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
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

highest level of hierarchy (HH), and (iii) knowledge connectedness, indicated bythe number of cross-links (NCL). Using this method, the total score (TS) of the concept map iscalculated as follows: T S = N C + HH × 5 + N CL × 10 (1)Using Eq. 1, the total score for the concept map illustrated in Fig. 1 is calculated to be 40, whereN C = 10, HH = 4, and N CL = 1. In this study, we employ the traditional scoring method toevaluate the concept maps.Data AnalysisThe data collected from the concept maps and survey were analyzed using the followingmethods:Concept Map Iteration AnalysisThe concept map scores were systematically examined to assess their evolution over multipleiterations

Relevance Score: 0.010800775

Data Analytics for Engineering Student Success and College Operations

Conference Session: DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Robert J. Rabb P.E., Pennsylvania State University; Ivan E. Esparragoza, Pennsylvania State University; Jennifer X Wu
Tagged Topics: Diversity
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

Used in Years of Data Data Trend Description Rationale Data TypeAnalyze Prediction Analyzed Source A Which junior course(s) We mostly used the list of courses you Yes 6 academic Student Class offered by your suggested. This process ensures that we years Credit Hour Enrollment department have the don't leave out any highly enrolled dashboard highest course enrollment. courses in case there is increased Fall and Spring trends were

Relevance Score: 0.009450678

Engineering Student Success based on Performance in First Semester Foundational 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
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

Relevance Score: 0.008184528

A Unique Course Designed for Graduate Students: Integrating High-Performance Parallel Computing into Machine Learning and Artificial Intelligence

Conference Session: DSAI Technical Session 5: Educational Technology and Innovative Tools
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Handan Liu, Northeastern University
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

one to provide aprogressive learning experience.Part 1: High-Performance Computing (HPC) Architectures and Parallel ProgrammingModelsHigh-performance computing (HPC) architectures are designed to solve complex computationalproblems by leveraging the parallel processing capabilities of multiple computing resources.These architectures typically consist of clusters of interconnected nodes, each with its ownprocessor(s), memory, and storage, working collaboratively to execute tasks simultaneously.HPC systems rely on efficient interconnect networks for high-speed communication betweennodes and often utilize specialized hardware, such as GPUs or accelerators, to enhanceperformance for specific workloads. Software frameworks like MPI (Message

Relevance Score: 0.007637301

Data Analytics for Faculty Success and Career Development

Conference Session: DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Alyson Grace Eggleston, Pennsylvania State University; Robert J. Rabb P.E., The Pennsylvania State University; Eric Donnell, The Pennsylvania State University
Tagged Topics: Diversity
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

whilearming leaders with the information they need to adapt programs to shifting needs of theinstitution, funding agency, or national security priorities. With these tools, researchadministrators are better equipped to steer innovation and maximize the impact of early-stagefunding.References[1] 2024 EDUCAUSE Horizon Action Plan: Unified Data Models. https://library.educause.edu/resources/2024/3/2024-educause-horizon-action-plan-unified- data-models[2] W. Strielkowski, A. Samoilikova, L. Smutka, L. Civín, and S. Lieonov, “Dominant trends in intersectoral research on funding innovation in business companies: A bibliometric analysis approach,” in Journal of Innovation & Knowledge, 7(4), 100271, 2022.[3] A. Molnar, A. F. McKenna, Q Liu, M