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Expanding a State-wide Data Science Educational Ecosystem to Meet Workforce Development Needs

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

$80,000 to$120,000/year, making it an attractive career for both new graduates and those seekingadvancement. [2, 3] There are over 100 data science/analytics roles currently open in Arkansasaccording to the U.S. Bureau of Labor Statistics, and employment in this field is projected to grow36% from 2021 to 2031 [4].The Arkansas Economic Development Commission’s Science Advisory Committee submitted theupdated Arkansas Science & Technology Plan 2024, approved by Secretary of Commerce HughMcDonald. This plan aims to “enable the crystallization of focused research and innovationplanning and provide a focus for the Arkansas scientific community.” Key strategies includealigning research and education with the state’s key industries and expanding both

Relevance Score: 0.02193974

Future-Ready Students: Validating the Use of Natural Language Processing to Analyze Student Reflections on a Remote Learning Group Project

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

responses for two cohorts from the Spring of 2023 andSpring of 2024, focusing on the themes of collaboration and planning (teamwork), as well asproblem solving. Lessons learned about the process of applying the techniques, as well asinsights gained about the student experience as captured in their reflections, are shared in theconclusions section, along with the authors’ recommendations for the use of the AI-assistedprocess to analyze qualitative data as a means of better understanding the students’ projectexperience.This work advances the subject of engineering education by showing how automated naturallanguage processing (NLP) techniques may be used to evaluate student reflections, offering ascalable and effective substitute for conventional

Relevance Score: 0.020418832

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

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

Relevance Score: 0.0152768735

Providing engineering education researchers and stakeholders with easy access to granular, disparate data sources

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

education equity, to workforce data. Pre-college summer bridge STEM programs 17 Diversity action plans 15 B. Initial Tool Development Near peer mentoring 15 Entrepreneurial programs (at any level) 14 To meet these needs and facilitate discussion among stake- Reforming curriculum and teaching practices 14 Collaborative learning / living environments 11 holders, the CIDER team began the development of the Institutional leadership engagement 11 Engineering Education Ecosystem Landscape Framework 3 . Mentoring with peers of color

Relevance Score: 0.0133672645

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

Engineering Education, 2025 Data Analytics for Engineering Student Success and College OperationsAs resource constraints have driven calls for more transparency and accountability in highereducation, high demand disciplines like engineering are using data sets to justify decisions andshape strategic planning goals. However, engineering is also well-poised to employ data in visualand useful ways to analyze and synthesize years of data and trends. Serving a largeundergraduate engineering student body across multiple campuses and encompassing multipleengineering disciplines, the Penn State University’s College of Engineering can gain insightsinto the student population, faculty, and departments’ needs. The

Relevance Score: 0.012202587

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

also providing valuable insights to faculty and their mentors asthey plan for continued career development. Moving toward predictive models sets the stage forkey insights that are sensitive to an institutional context—in this case, the primacy ofinterdisciplinary teams for securing initial seed funding.Moving forward, BI dashboards also allow decision makers to steer pilot funding priorities toclosely track with the changing goals of federal funding agencies. By integrating key metrics,such as team strengths and applicant funding histories, a higher resolution footprint of researchimpact against particular grant funding mechanisms can be established.Finally, research administration BI dashboards facilitate continuous evaluation processes

Relevance Score: 0.01080238

Integration of Data Science Modules Across Interdisciplinary Courses at Multiple Institutions: Analysis of Students’ and Faculty Perspectives

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

potential of LLMs in enhancing data scienceeducation and plans several expansions incorporating these tools. Both students and instructorshave identified a significant need for personalized learning experiences due to varying levels ofdata science expertise and different learning pace requirements among students. Instructorsbelieve LLMs can help address these challenges by providing customized support for conceptunderstanding and a smooth introduction to data analysis tools such as coding, particularly forstudents with limited prior exposure to data science. However, instructors emphasize theimportance of treating LLMs as assistive tools rather than authoritative sources, encouragingstudents to maintain critical thinking and responsibility for

Relevance Score: 0.008101785

Explore the Possibility of Monitoring Project Member Interactions Using Natural Language Processing

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

Kubernetes cluster. We never transmit student data to external APIs or third-partyservices, thus minimizing any risk of leakage. The script evaluate_llama.py encapsulatesthis offline inference process by loading the final JSON (produced by csv_to_json.py),using a local Llama installation for text generation, and then saving the results into a CSV.This approach gives us full control over data handling: ● Immediate Anonymization – Before or during the CSV-to-JSON conversion, identifiable student fields (e.g., names, emails) are replaced or hashed (planned for the next iteration) to ensure no personally identifiable information is exposed to the language model. ● GPU Acceleration – We execute the model on an NVIDIA A100 GPU, making it

Relevance Score: 0.007717593

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

. Differentialcollege/university graduation retention numbers suggest that there are a small number of moderately-lowperformance indicators which are able to identify students who are much more likely to have academicsuccess in fields outside of engineering.Outcomes from these analyses include new mechanisms for early identification of at-risk students, for whomspecialized advising and success coaching would be beneficial, as well as the development of new curricularplanning options for students who are not yet calculus ready in their first semester and would benefit fromcustomized curricular planning to support better first-year performance.1 IntroductionThe demand for engineers in the workforce continues to grow [1], but the number of engineering

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Expanding AI Ethics in Higher Education Technical Curricula: A Study on Perceptions and Learning Outcomes of College Students

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

equation: Factor Score ∼ Survey Time (pre/post) + Demographic Variable (1)3.5 Analysis of responses to open-ended essay questionsThe three open-ended essay questions asked participants to discuss their questions and concernsabout the design and implementation of AI systems across different domains in healthcare andemployment selection (Table 3). ID Question A machine learning (ML) algorithm has been designed to assist radiologists with estimating the level of damage COVID-19 has caused to patients’ lungs. This can help the physician in prescribing an appropriate medication and treatment plan for the patients. The ML

Relevance Score: 0.0055125663

Are Engineering Degrees Really More Complex? Characterizing the Complexities of Academic Programs by Discipline

Conference Session: DSAI Technical Session 6: Academic Success, Performance & Complexity
Collection: 2025 ASEE Annual Conference & Exposition
Authors: Gregory L. Heileman, The University of Arizona; Chaouki T Abdallah, Georgia Institute of Technology; Kristina A Manasil, The University of Arizona; Melika Akbarsharifi, The University of Arizona; Roxana Akbarsharifi, The University of Arizona
Tagged Topics: Diversity
Tagged Divisions: Data Science and Artificial Intelligence (DSAI) Constituent Committee

(a) (b)Figure 1: (a) An example electrical engineering program curriculum, organized as a degree plan over eightterms. The courses in the curriculum are shown as vertices, and the prerequisites are shown as directededges. (b) Highlighting the Calculus I course in this curriculum shows that Calculus I blocks 15 other coursesin the curriculum (shown in green), and the longest path in the curriculum that includes Calculus I (shownas a blue dashed line) has length 8. 20 300 number of curricula number of curricula

Relevance Score: 0.0054571624

The role of professional development in enhancing AI literacy competence development and participation in the Intelligence Age

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

., Jun. 2024.[24] D. Ng, W. Luo, H. Chan, and S. Chu, “An examination on primary students’ development in AI literacy through digital story writing,” Ng T K Luo W Chan H M Chu K W 2022 Exam. Prim. Stud. Dev. AI Lit. Digit. Story Writ. Comput. Educ. Artif. Intell. 100054, vol. 4, 2022, Accessed: Jan. 14, 2025. [Online]. Available: https://www.researchgate.net/publication/358460136_An_examination_on_primary_student s'_development_in_AI_literacy_through_digital_story_writing[25] I. Ajzen, “From Intentions to Actions: A Theory of Planned Behavior,” in Action Control, J. Kuhl and J. Beckmann, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 1985, pp. 11– 39. doi: 10.1007/978-3-642-69746-3_2.[26] D. Cetindamar, K

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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

the 2024 IEEE International Technology Conference (OTCON), 2024. DOI: 10.1109/OTCON60325.2024.10688123.[11] L. M. Cruz Castro, T. Li, L. Ciner, K. A. Douglas, and C. G. Brinton, "Predicting Learning Outcome in a First-Year Engineering Course: A Human-Centered Learning Analytics Approach," presented at the ASEE Annual Conference & Exposition, 2022.[12] C. Burgos, M. L. Campanario, D. D. L. Peña, J. A. Lara, D. Lizcano, and M. A. Martínez, "Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout," Computers & Electrical Engineering, vol. 66, pp. 541–556, Feb. 2018, doi: 10.1016/j.compeleceng.2017.03.005.[13] P. B. Thomas, C. R. Bego, and A. D. Piemonte