Displaying all 12 results
- Conference Session
- DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction
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- 2025 ASEE Annual Conference & Exposition
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- Alyson Grace Eggleston, Pennsylvania State University; Robert J. Rabb P.E., The Pennsylvania State University; Eric Donnell, The Pennsylvania State University
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- Data Science and Artificial Intelligence (DSAI) Constituent Committee
Paper ID #48634Data Analytics for Faculty Success and Career DevelopmentDr. Alyson Grace Eggleston, Pennsylvania State University Alyson Eggleston is an Associate Professor in the Penn State Hershey College of Medicine and Director of Evaluation for the Penn State Clinical and Translational Science Institute. Her research and teaching background focus on program assessment, STEM technical communication, industry-informed curricula, and educational outcomes veteran and active duty students.Dr. Robert J. Rabb P.E., The Pennsylvania State University Robert Rabb is the associate dean for education in the College of
- Conference Session
- DSAI Technical Session 6: Academic Success, Performance & Complexity
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- 2025 ASEE Annual Conference & Exposition
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- Michael T Johnson, University of Kentucky; Johné M Parker, University of Kentucky
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graduatesis not keeping up with this demand [2]. One significant factor in this gap is the number of students who leaveengineering before earning a degree, more than 40% [3]. As a result, student retention and graduation ratesin engineering have received considerable study in recent years, in hopes of identifying ways to improvestudent persistence and help students obtain their educational and career goals.There are a wide range of factors correlated with student retention and graduation in engineering, includingacademic preparedness, financial stability, student belonging and engagement, quality of advising, andsupport systems for developing time management and study skills [4-7]. It is well known that math readinessin particular is one of the most
- Conference Session
- DSAI Technical Session 3: Integrating Data Science in Curriculum Design
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- 2025 ASEE Annual Conference & Exposition
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- Elizabeth Milonas, New York City College of Technology; Qiping Zhang, Long Island University; Duo Li, Shenyang Institute of Technology
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tools, the demand for highly skilled datascientists has also grown exponentially [1]. According to Indeed Career Guide, data sciencerelated jobs were on the list of top 20 jobs in the United States in 2023[2]. These highly skilledprofessionals are responsible for complex tasks and have a pivotal role in organizations. Theireffectiveness depends on technical skill, analytical proficiency and foundational understandingof all aspects related to the data science domain [1]. To meet the demand of training highlyskilled and specialized Data Science professionals, many colleges have revised their existingmajors to include Data Science related topics or created new Data Science related majors tofocus on providing the Data Science knowledge and skills
- 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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Institute and State University Dr. Vinod K. Lohani is a Professor of Engineering Education at Virginia Tech. He served as a Program Director in the Division of Graduate Education, NSF for 4 years (2020-24). In this capacity, he was deeply engaged with the NSF Research Traineeship (NRT), Innovations in Graduate Education (IGE) and CAREER programs and also participated in several NSF-wide working groups on semiconductors and quantum information science and engineering (QISE).Dr. Manoj K Jha P.E., North Carolina A&T State University Dr. Manoj K Jha is an associate professor in the Civil, Architectural, and Environmental Engineering department at the North Carolina A&T State University. His research interests include
- Conference Session
- DSAI Technical Session 3: Integrating Data Science in Curriculum Design
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- 2025 ASEE Annual Conference & Exposition
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- 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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, workforce development, and student success initiatives. Dr. Gattis has secured and managed over $6.9 million in competitive NSF and ADHE grants, supporting student retention, innovation in STEM education, and workforce-aligned pathways. Her work focuses on increasing diversity, improving STEM career readiness, and strengthening industry collaboration.Dr. Stephen R. Addison, University of Central Arkansas Dr. Stephen R. Addison is a Professor of Physics and Dean of the College of Science and Engineering at the University of Central Arkansas. Dr. Addison joined the faculty of the University of Central Arkansas in 1984, and has previously served as Dean and Associate Dean of the College of Natural Sciences and
- Conference Session
- DSAI Technical Session 6: Academic Success, Performance & Complexity
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- 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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? RQ3: What socio-demographic factors most determine a student's academic performance?This research aims to establish the foundation for designing and developing predictivemodels that enable the early identification of socio-demographic and academic factors withthe greatest impact on student performance upon entering the Faculty of Engineering.Implementing these models aims to detect students at higher risk of dropout and understandtheir specific needs. This will allow the implementation of personalized support strategies,which may include financial aid, flexible work schedules, study methodology reinforcementactivities, or academic and career guidance programs. By anticipating potential causes ofdropout, institutions can strengthen
- Conference Session
- DASI Technical Session 2: Artificial Intelligence in Higher Education
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- 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
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Large Language Models (LLMs). Taiwo is known for his ability to collaborate effectively within and across organizations to meet project goals and drive transformative results. He excels in leading technical teams, offering strategic IT consultations, and implementing solutions that enhance productivity.Lexy Chiwete Arinze, Purdue University at West Lafayette (COE) Lexy Arinze is a first-generation PhD student in the School of Engineering Education at Purdue University and a Graduate Research Assistant with the Global Learning Initiatives for the Development of Engineers (GLIDE) research group. Lexy’s research interests include early career engineers, Artificial Intelligence, experiential learning, and global
- Conference Session
- DASI Technical Session 2: Artificial Intelligence in Higher Education
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- 2025 ASEE Annual Conference & Exposition
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- Indu Varshini Jayapal, University of Colorado Boulder; James KL Hammerman; Theodora Chaspari, University of Colorado Boulder
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California (USC). Theodora’s research interests lie in human-centered machine learning, affective computing, and biomedical health informatics. She is a recipient of the NSF CAREER Award (2021). She is serving as an Associate Editor of the Elsevier Computer Speech & Language and the IEEE Transactions on Affective Computing. Her work is supported by federal and private funding sources, including the NSF, NIH, NASA, IARPA, AFOSR, General Motors, and the Engineering Information Foundation. ©American Society for Engineering Education, 2025 Expanding AI Ethics in Higher Education Technical Curricula: A Study on Perceptions and Learning Outcomes of College Students
- Conference Session
- DSAI Technical Session 7: Natural Language Processing and LLM Applications
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- 2025 ASEE Annual Conference & Exposition
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- Kaiwen Guo, New York University Tandon School of Engineering; Malani Snowden, New York University Tandon School of Engineering; Rui Li, New York University
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Interactions Using Natural Language ProcessingAbstractThis study looks into the use of team evaluation software, incorporating peer ratings, peercomments, and machine-learning-based analysis, to assess the project performance of studentproject teams. Teamwork is an essential competency for students. The early development ofcollaborative skills is critical for academic success and future career success. Previous studieshave suggested that the data-driven team evaluation could help with team performanceevaluation. However, most of the team-based software will provide peer rating without detailedfeedback of student team performance. CATME (Comprehensive Assessment of Team MemberEffectiveness) greatly facilitates peer
- 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
13.9% STEM tutoring 26 Female 11.2% STEM club or other STEM organization 25 Non-traditional students 4.4% Career counseling and awareness 24 Students with disabilities 4.8% STEM Professional guest speaker sessions 24 Students with low socioeconomic status 9.2% Academic advising 23 No specific population 46.6% Undergraduate internships 23
- 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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Paper ID #46681Future-Ready Students: Validating the Use of Natural Language Processingto Analyze Student Reflections on a Remote Learning Group ProjectMajd Khalaf, Norwich University Majd Khalaf recently graduated from Norwich University with a Bachelor’s degree in Electrical and Computer Engineering, along with minors in Mathematics and Computer Science. He is passionate about DevOps, embedded systems, and machine learning. Throughout his academic career, Majd contributed to various projects and research in natural language processing (NLP) and computer vision. He served as a Senior AI Researcher at Norwich University’s
- 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
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since applicants may pursue graduateeducation directly after their undergraduate education or at any stage of their career. Graduateadmissions data also has a significantly lower volume of data per admissions cycle, owing to itssignificantly lower intake compared to undergraduate programs. In addition to this, the processof admission review varies not only between different universities but also between theundergraduate and graduate programs in the same university. Undergraduate applications aretypically reviewed centrally by the university whereas graduate admission review may beconducted by a specific department's professors and staff since essays can be specific to the field.Therefore, it is difficult to generalize decision-making criteria