Displaying all 9 results
- 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
by helping develop the next generation of STEM workforce. He has patents in various technology areas and is the author and co-author of several books. Dr. Schubert is a Senior Member of the IEEE, Senior Member of ACM, and Senior Member of IISE. He is also Vice Chair of the Ozark Section of the IEEE Computer Society and is the ASEE Data Science & Artificial Intelligence (DSAI) Constituent Delegate to the Commission on P-12 Engineering Education (CP12) and the DSAI Delegate to the Interdivisional Town Hall.Dr. Carol S Gattis, University of Arkansas Carol S. Gattis is an Associate Dean Emeritus and Adjunct Associate Professor at the University of Arkansas. She has over 34 years of experience in STEM education
- 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
- 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
responsible AI more effectively, the complexity and rigor of participants’ discussionson each theme–referred to as theme depth–were also assessed. Theme depth was measured usinga 4-point scale adapted from Baker-Brown et al.’s conceptual/integrative complexity framework[30]. This scale assesses the sophistication of students’ engagement with ethical considerations inAI: • No mention (0): The ethical theme is completely absent from the response. • Superficial mention (1): The ethical issue is briefly acknowledged without substantive dis- cussion. For example, a student might simply state “privacy is a concern” without explaining why or how it applies to the AI system in question. • Detailed description (2): The ethical issue is
- 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
- 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
- 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
- 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 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
- Conference Session
- DSAI Technical Session 7: Natural Language Processing and LLM Applications
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Kaiwen Guo, New York University Tandon School of Engineering; Malani Snowden, New York University Tandon School of Engineering; Rui Li, New York University
- Tagged Topics
- Diversity
- Tagged Divisions
- Data Science and Artificial Intelligence (DSAI) Constituent Committee
.[2] Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals ofartificial intelligence, 603-649.[3] Schank, R. C. (1972). Conceptual dependency: A theory of natural language understanding.Cognitive psychology, 3(4), 552-631.[4] Paris, C. L., Swartout, W. R., & Mann, W. C. (Eds.). (2013). Natural language generation inartificial intelligence and computational linguistics (Vol. 119). Springer Science & BusinessMedia.[5] Loria, S. (2022) Text-Blob: Simplified Text Processing.https://textblob.readthedocs.io/en/dev[6] Hazarika, Ditiman & Konwar, Gopal & Deb, Shuvam & Bora, Dibya. (2020). SentimentAnalysis on Twitter by Using TextBlob for Natural Language Processing. 63-67.10.15439/2020KM20.[7] H