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Integrating Data Science into the Pipeline Building Toward a Diversified Workforce in Nuclear Energy and Security

Conference Session: DSA Technical Session 2
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Xiang Zhao, Alabama A&M University; Mebougna L. Drabo, Alabama A&M University
Tagged Topics: Data Science & Analytics Constituent Committee (DSA), Diversity

include STEM education, Additive Manufacturing, Thermoelectric Devices for Energy Harvesting, Digital Twinning Technology, Nuclear Radiation Detectors, Nuclear Security and Safety, Small Nuclear Modular Reactors (SMR), Material Characterization (X-ray Photoelectron Spectroscopy & Infrared Microscopy), Nanotechnology, Data Analytics and Visualization, Biofuels Applications, Computational Fluid Dynamics analysis, Heat Transfer, Energy Conservation in building, and Multi Fuel Optimization. ©American Society for Engineering Education, 2024 2024 ASEE Annual Conference and Exposition Integrating Data Analytics into the Pipeline Building toward a

Relevance Score: 0.03261097

Data-Science Perceptions: A Textual Analysis of Reddit Posts from Non-Computing Engineers

Conference Session: DSA Technical Session 5
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Nicolas Leger, Florida International University; Maimuna Begum Kali, Florida International University; Stephanie Jill Lunn, Florida International University
Tagged Topics: Data Science & Analytics Constituent Committee (DSA)

Relevance Score: 0.021899508

Preparing Undergraduate Data Scientists for Success in the Workplace: Aligning Competencies with Job Requirements

Conference Session: DSA Technical Session 5
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Duo Li, Shenyang Institute of Technology; Elizabeth Milonas, New York City College of Technology; Qiping Zhang, Long Island University
Tagged Topics: Data Science & Analytics Constituent Committee (DSA)

IEEE Global Engineering Education Conference (EDUCON), pp. 1329-1336, IEEE, 2021.[2]. B. A. Quismorio, M. A. D. Pasquin, and C. S. Tayco, "Assessing the alignment of Philippine higher education with the emerging demands for data science and analytics workforce," PIDS Discussion Paper Series, 2019, no. 2019-34.[3]. M. Almgerbi, A. De Mauro, A. Kahlawi, and V. Poggioni, "A systematic review of data analytics job requirements and online-courses," Journal of Computer Information Systems, vol. 62, no. 2, pp. 422-434, 2022.[4]. E. Milonas, Q. Zhang, and D. Li, "Do Undergraduate Data Science Program Competencies Vary by College Rankings?" In Proceedings of the 2022 ASEE Annual Conference & Exposition, Minneapolis, MN

Relevance Score: 0.021477971

A Hybrid Approach to Natural Language Processing for Analyzing Student Feedback about Faculty Support

Conference Session: DSA Technical Session 8
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Neha Kardam, University of Washington; Denise Wilson, University of Washington
Tagged Topics: Data Science & Analytics Constituent Committee (DSA)

Text as Social and Cultural Data," Frontiers in Artificial Intelligence, vol. 3, p. 62, 2020. [Online]. Available: https://www.frontiersin.org/articles/10.3389/frai.2020.00062. doi: 10.3389/frai.2020.00062.[2] N. Kardam, S. Misra, and D. Wilson, "Is Natural Language Processing Effective in Education Research? A case study in student perceptions of TA support," presented at the 2023 ASEE Annual Conference & Exposition, 2023. [Online]. Available: https://peer.asee.org/43887[3] L. Fesler, T. Dee, R. Baker, and B. Evans, "Text as Data Methods for Education Research," Journal of Educational and Behavioral Statistics, vol. 44, no. 6, pp. 709-732, 2019. doi: 10.1080/19345747.2019.1634168.[4

Relevance Score: 0.020594798

Minimizing Curricular Complexity through Backwards Design

Conference Session: DSA Technical Session 1
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Gregory L. Heileman, The University of Arizona; Yiming Zhang, The University of Arizona
Tagged Topics: Data Science & Analytics Constituent Committee (DSA)

/1811.09676.5 Gregory L. Heileman, William G. Thompson-Arjona, Hayden W. Free, and Orhan Abar. Does curricular complexity imply program quality? In 2019 ASEE Annual Conference & Exposition, Tampa, FL, June 2019. ASEE Conferences. peer.asee.org/32677.6 Nathan W. Klingbeil and Anthony Bourne. The Wright State model for engineering mathematics education: Lon- gitudinal impact on initially underprepared students. In Proceedings of the 122nd ASEE Annual Conference & Exposition, Seattle, WA, June 14–17, 2015.7 Michael R. Garey and David S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York, NY, 1979.8 Patrick Healy and Nikola S. Nikolov. How to layer a

Relevance Score: 0.020177007

Envisioning and Realizing a Statewide Data Science Ecosystem

Conference Session: DSA Technical Session 5
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Karl D. Schubert FIET, University of Arkansas; Shantel Romer, University of Arkansas; Stephen R. Addison, IEEE Educational Activities; Tina D Moore; Laura J Berry, North Arkansas College; Jennifer Marie Fowler, Arkansas State University; Lee Shoultz, University of Arkansas; Christine C Davis
Tagged Topics: Data Science & Analytics Constituent Committee (DSA)

together for consistentdegree programs. Because we were a small state, we needed to work together to develop qualitydata science education at all levels. In late 2019, we co-hosted a workshop with representativesfrom industry, academia, state government, and students at all levels, to explore the potential fordeveloping a state-wide data science educational ecosystem. The response was overwhelmingly“yes.” As a result, and with the collaboration of our state’s division of higher education, wedeveloped a vision, strategy, and plan to do so with an “opt-in” approach and this paper presentsthe vision, strategy, plans, results, and experiences and continuous improvement over more thanfour years of collaboration. These include the development of A.S

Relevance Score: 0.01999551

Bridging Theory and Practice: Building an Inclusive Undergraduate Data-Science Program

Conference Session: DSA Technical Session 5
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Mehmet Ergezer, Wentworth Institute of Technology; Mark Mixer, Wentworth Institute of Technology; Weijie Pang, Wentworth Institute of Technology
Tagged Topics: Data Science & Analytics Constituent Committee (DSA), Diversity

-enabled adaptive learning systems: A systematic mapping of the literature,” Computers and Education: Artificial Intelligence, vol. 2, p. 100017, 2021.[16] P. Johanes and L. Lagerstrom, “Adaptive learning: The premise, promise, and pitfalls,” in 2017 ASEE Annual Conference & Exposition, 2017.[17] C. Richardson, “Council post: The next revolution in global e-learning,” Jul 2018. [Online]. Available: https://www.forbes.com/sites/forbestechcouncil/2018/07/25/the-next-revolution-in-global-elearning/?sh=66f38d357c25[18] Y. Niu, L. Ying, J. Yang, M. Bao, and C. Sivaparthipan, “Organizational business intelligence and decision making using big data analytics,” Information Processing & Management, vol. 58, no. 6, p. 102725, 2021

Relevance Score: 0.017532004

Using Cohort-Based Analytics to Better Understand Student Progress

Conference Session: DSA Technical Session 6
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Kristina A Manasil, The University of Arizona; Gregory L. Heileman, The University of Arizona; Bhavya Sharma, The University of Arizona; Ahmad Slim, The University of Arizona; Aryan Ajay Pathare, The University of Arizona; Husain Al Yusuf, The University of Arizona; Roxana Sharifi, The University of Arizona; Rohit Hemaraja, The University of Arizona; Melika Akbarsharifi, The University of Arizona
Tagged Topics: Data Science & Analytics Constituent Committee (DSA)

-Miguel. Implementing the sustainable development goals in university higher education: A systematic review. International Journal of Sustainable Development and Planning, 18(6):1769–1776, 2023. 4 S. Lee and D. Shapiro. Completing college: National and state report with longitudinal data dashboard on six- and eight-year completion rates. (signature report 22),. Technical report, National Student Clearinghouse Research Center, Herndon, VA, 2023. 5 A. Valero and J. Van Reenen. The economic impact of universities: Evidence from across the globe. Economics of Education Review, 68:53–67, 2019. 6 M. Hanson. College Graduation Statistics. 2023. EducationData.org. 7 B. Yoder. ASEE Retention and Time-to-Graduation

Relevance Score: 0.015122199

From Manual Coding to Machine Understanding: Students' Feedback Analysis

Conference Session: DSA Technical Session 7
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Abdulrahman Alsharif, Virginia Polytechnic Institute and State University; Andrew Katz, Virginia Polytechnic Institute and State University
Tagged Topics: Data Science & Analytics Constituent Committee (DSA), Diversity

identified themes in this study. Future research couldexplore alternative approaches (e.g., GPT-4) to streamline the clustering and code generationprocesses, potentially leveraging advanced natural language processing techniques to automatethe identification and consolidation of overlapping themes.References[1] A. Alsharif, A. Katz, D. Knight, and S. Alatwah, “Using Sentiment Analysis to Evaluate First-year Engineering Students Teamwork Textual Feedback,” in 2022 ASEE Annual Conference & Exposition, 2022. Accessed: Nov. 28, 2023. [Online]. Available: https://peer.asee.org/41460.pdf[2] R. S. Baker and P. S. Inventado, “Educational Data Mining and Learning Analytics,” in Learning Analytics: From Research to Practice, J. A. Larusson

Relevance Score: 0.015092198

Paradigm Shift? Preliminary Findings of Engineering Faculty Members’ Mental Models of Assessment in the Era of Generative AI

Conference Session: DSA Technical Session 7
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Isil Anakok, Virginia Polytechnic Institute and State University; Kai Jun Chew, Embry-Riddle Aeronautical University, Daytona Beach; Holly M Matusovich, Virginia Polytechnic Institute and State University; Andrew Katz, Virginia Polytechnic Institute and State University
Tagged Topics: Data Science & Analytics Constituent Committee (DSA)

serving as Chair from 2017-2019. Dr. Matusovich is currently the Editor-in-Chief of the journal, Advances in Engineering Education and she serves on the ASEE committee for Scholarly Publications.Dr. Andrew Katz, Virginia Polytechnic Institute and State University Andrew Katz is an assistant professor in the Department of Engineering Education at Virginia Tech. He leads the Improving Decisions in Engineering Education Agents and Systems (IDEEAS) Lab. ©American Society for Engineering Education, 2024Paradigm Shift? Preliminary Findings of Engineering Faculty Members’ Mental Models of Assessment in the Era of Generative AI Paradigm Shift? Preliminary Findings of Engineering Faculty Members

Relevance Score: 0.015092198

A Comparative Analysis of Natural Language Processing Techniques for Analyzing Student Feedback about TA Support

Conference Session: DSA Technical Session 8
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Neha Kardam, University of Washington; Denise Wilson, University of Washington; Sep Makhsous, University of Washington
Tagged Topics: Data Science & Analytics Constituent Committee (DSA)

Learning Analytics,” Learning Analytics, pp. 61–75, 2014, doi: 10.1007/978-1-4614-3305-7_4.[7] N. Kardam, S. Misra, and D. Wilson, "Is Natural Language Processing Effective in Education Research? A case study in student perceptions of TA support," presented at the 2023 ASEE Annual Conference & Exposition, 2023. [Online]. Available: https://peer.asee.org/43887[8] Katz, M. Norris, A. M. Alsharif, M. D. Klopfer, D. B. Knight, and J. R. Grohs, “Using Natural Language Processing to Facilitate Student Feedback Analysis,” in 2021 ASEE Virtual Annual Conference. Content Access, July 26-29, 2021. [online]. Available: https://peer.asee.org/using-natural-language-processing-to-facilitate-student-feedback

Relevance Score: 0.01412227

Causal Inference Networks: Unraveling the Complex Relationships Between Curriculum Complexity, Student Characteristics, and Performance in Higher Education

Conference Session: DSA Technical Session 1
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Ahmad Slim, The University of Arizona; Gregory L. Heileman, The University of Arizona; Melika Akbarsharifi, The University of Arizona; Kristina A Manasil, The University of Arizona; Ameer Slim, University of New Mexico
Tagged Topics: Data Science & Analytics Constituent Committee (DSA), Diversity

research at the Curricular Analytics Lab focuses on using machine learning and data analysis to enhance educational outcomes. Key contributions include developing a cohort-tracking analytics platform that assists in improving graduation rates by addressing curricular barriers. Melika has co-authored papers presented at conferences such as the ASEE Annual Conference and Exposition, exploring the intersection of curriculum complexity and student performance. Her technical proficiency spans multiple programming languages and cloud computing, furthering her research into innovative educational technologies.Kristina A Manasil, The University of Arizona Kristi Manasil is a first-year PhD student in the School of

Relevance Score: 0.012601833

Extra Credit Analysis of Undergraduate Engineering Students

Conference Session: DSA Technical Session 3
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Tushar Ojha, University of New Mexico; Don Hush, University of New Mexico
Tagged Topics: Data Science & Analytics Constituent Committee (DSA), Diversity

. Retrieved from https://digitalrepository.unm.edu/ece_rpts/55.[14] A. Karimi and R. D. Manteufel. Factors influencing student graduation rate. In 2013 ASEE Gulf-Southwest Annual Conference. American Society for Engineering Education, March 2013.[15] W. Kilgore, E. Crabtree, and K. Sharp. Excess credit accumulation: An examination of contributing factors for first-time bachelor’s degree earners. Strategic Enrollment Management Quarterly, 6(4):41–56, 2019.[16] D. A. Kramer, M. R. Holcomb, and R. Kelchen. The costs and consequences of excess credit hours policies. Educational Evaluation and Policy Analysis, 40(1):3–28, 2018. Retrieved from http://www.jstor.org/stable/44985444.[17] M. N. Kwenda. Tracking and explaining credit

Relevance Score: 0.011390414

Credit-Hour Analysis of Undergraduate Students Using Sequence Data

Conference Session: DSA Technical Session 3
Collection: 2024 ASEE Annual Conference & Exposition
Authors: Tushar Ojha, University of New Mexico; Don Hush, University of New Mexico
Tagged Topics: Data Science & Analytics Constituent Committee (DSA)

-Doroubi, T. Ojha, B. Santos, and K. Warne. Analyzing student credits. 2022. Retrieved from https://digitalrepository.unm.edu/ece_rpts/55.[11] M. Kapur. Temporality matters: Advancing a method for analyzing problem-solving processes in a computer-supported collaborative environment. International Journal of Computer-Supported Collaborative Learning, 6:39–56, 2011.[12] A. Karimi and R. D. Manteufel. Factors influencing student graduation rate. In 2013 ASEE Gulf-Southwest Annual Conference. American Society for Engineering Education, March 2013.[13] W. Kilgore, E. Crabtree, and K. Sharp. Excess credit accumulation: An examination of contributing factors for first-time bachelor’s degree earners. Strategic Enrollment