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

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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 10: Research Infrastructure and Institutional Insights

Collection

2025 ASEE Annual Conference & Exposition

Authors

Pallavi Singh, University of South Florida; Joel Howell; Joshua Karl Thomas Ranstrom, University of South Florida; Wilfrido A. Moreno P.E., University of South Florida

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Paper ID #49477Data Analysis: Evaluating the Impact of the Professional Formation of EngineersProgram on Career DevelopmentPallavi Singh, University of South Florida Pallavi Singh received a bachelor’s degree in Electronics and Communication Engineering from Guru Nanak Dev Engineering College (GNDEC), Bidar, in 2016 and a master’s degree in Electrical Engineering from University of South Florida, Tampa, FL, USA, in 2019. Pallavi worked as a data science engineer, embedded system engineer, computer vision engineer, system engineer, project manager, and systems engineer, In addition, Pallavi, has also served as a

Conference Session

DSAI Technical Session 8: Learning Analytics and Data-Driven Instruction

Collection

2025 ASEE Annual Conference & Exposition

Authors

Clara Fang, University of Hartford

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

futureengineering professionals. The research has shown that data-driven research experiencescontribute to both academic growth and the development of lifelong learning skills.Linn et al. (2015) [2] emphasize the transformative impact of undergraduate research onstudents’ career trajectories and academic growth. They argue that these experiences createopportunities for students to engage with real-world challenges, build collaborative skills, anddevelop an appreciation for the research process.Maybee et al (2015) [3] discussed the integration of data informed learning within disciplinarycontexts. By building upon students' prior experiences and aligning data usage with subject-specific learning, the framework fosters both academic development and the

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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 4: Workshops, Professional Development, and Training

Collection

2025 ASEE Annual Conference & Exposition

Authors

Olatunde Olu Mosobalaje, Covenant University; Moses Olayemi, The University of Oklahoma

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

pedagogy and the contextualization and validation of measurement instruments with a keen interest in sub-Saharan Africa. ©American Society for Engineering Education, 2025WIP: The Design of a Professional Development Program for PetroleumEngineering Educators Towards Integrating Data Analytics and MachineLearning into Petroleum Engineering Curriculum AbstractThe petroleum industry is increasingly embracing digital transformation, enabled by data analytics,machine learning and other data-driven innovations. The proliferation of oilfield data as well asthe availability of open-source data mining softwares is opening up career frontiers in petroleumdata analytics and machine

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xiang Zhao, Alabama A&M University; Mebougna Drabo, Alabama A&M University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

al. [4] that incorporating dataanalytics and exposing students to real-world datasets improved their critical thinking. Moreimpressively, data science education encourages students to explore STEM careers and alsoprovides a strong foundation for further education and future employment opportunities asstudied by Marques et al. [5].Data Analytics in STEM EducationBrown et al. [6] integrated data analytics in engineering education to address technical require-ments from a multicomplex environment perspective concept using data analytics tools such asIBM Watson Analytics. The results obtained from a multi-complex environment have aided stu-dents and improved their decision approach to quantify data accuracy and project requirements.The

Conference Session

DSAI Technical Session 4: Workshops, Professional Development, and Training

Collection

2025 ASEE Annual Conference & Exposition

Authors

yilin zhang, University of Florida; Bruce F. Carroll, University of Florida; Jinnie Shin, University of Florida; Kent J. Crippen, University of Florida

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

%, an F1-score of 0.982, precision of0.982. These results demonstrate the model’s potential to accurately and systematically analyzementoring dialogues, providing a reliable foundation for further development of AI-poweredmentor training tools.keywordsDiscourse Analysis, Peer Mentoring, RoBERTa, Talk-Move Framework, Transformer1 IntroductionPeer mentoring, where one person (i.e., the mentor) provides practical advice to the other (i.e., thementee) given that they both are similar in age and share characteristics or experiences, hasemerged as a cornerstone of engineering education, providing crucial academic, career, andemotional support to students navigating complex technical curricula. In engineering programsspecifically, where students

Conference Session

DSAI Technical Session 3: Integrating Data Science in Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Elizabeth Milonas, New York City College of Technology; Qiping Zhang, Long Island University; Duo Li, Shenyang Institute of Technology

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

, 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 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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

.” Entity Verdict CSTA Standard Human different none ChatGPT different none Llama different none Claude similar to Compare tradeoffs associated with computing technologies that affect people’s everyday activities and career options.Table 6: Classification for Arkansas standard CSRB.Y1.10.7: “Research and identify diverse ca-reers and career opportunities (e.g., accessibility, availability, demand) that are influenced by com-puter science and the technical and soft skills needed for each.”there does not appear to be a close match to this standard in any of the CSTA standards. However,Claude categorized it as based on CSTA 3B-AP-4: “Compare multiple

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

? 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

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

providing adequate academic advising and counseling, such asstaffing shortages and limited resources [1,2]. With advisors often managing large caseloads,students sometimes struggle to receive the personalized guidance they need to succeedacademically, plan their careers, and navigate personal obstacles [3]. Similarly, while counselorsare available to offer emotional and mental health support, the availability of these services isoften limited, leaving students without timely assistance [1,2]. To address these gaps, AI-powered tools present a potential solution. While AI has been increasingly integrated intoeducational settings [4], its use for enhancing academic advising and counseling services remainsrelatively novel [5,6]. AI platforms can offer

Collection

2025 ASEE Annual Conference & Exposition

Authors

Xiaoning Jin; Sagar Kamarthi, Northeastern University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

career planning tools to provide end-to-end solutions.ConclusionDeveloping a recommendation engine leveraging GPT-4 and the RAG method the authorsdemonstrated a significant advancement in personalized learning solutions. By utilizingOpenAI’s text-embedding-3-large model and Pinecone’s vector database, the system efficientlyaddresses the challenges of personalization, scalability, and accuracy in courserecommendations. Integrating OpenAI's assistant API further enhances its capabilities, offeringseamless interactions and context-aware suggestions.Our results highlight the potential of LLMs to transform how individuals discover and engagewith learning opportunities. The positive outcomes underline the benefits of adopting cutting-edge AI

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

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∗ hall.carrie98@gmail.com, Safia@ksu.edu, lshamir@ksu.edu Kansas State UniversityAbstractData science careers are projected to grow by more than 30% by 2032, yet data science academicsare lacking and cannot satisfy the growing market demand for qualified data scientists.Additionally, K-12 data literacy rates are declining, introducing a gap between moderndata-driven society and the ability of members of society to understand data. Early experienceswith STEM subjects have been shown to influence and predict students’ long-term careeroutlooks and outcomes. In the context of data science, this means that early introduction at theK-12 level is crucial in order to develop and maintain the data science workforce. Although

Conference Session

DSAI Technical Session 9: Student Reflections, Metacognition, and Competency Mapping

Collection

2025 ASEE Annual Conference & Exposition

Authors

Taiwo Raphael Feyijimi, University of Georgia; VARUN KATHPALIA, University of Georgia; Sarah Jane Bork, University of Georgia

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

implications for electrical engineering curriculumdesign and teaching practices, providing a data-driven foundation for ensuring alignment withcurrent industry needs in the southeastern United States. The identified KSADs can guideeducators in developing targeted courses, workshops, and learning experiences that equip studentswith the specific skills and attributes sought by employers in the region. Additionally, the study'soutcomes can inform career counseling efforts, enabling students to make more informed decisionsabout specialization and professional development opportunities.Conclusion: This study underscores the value of integrating NLP and thematic analysis to extractcomprehensive competency information from job postings, advancing data

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

topic in engineering. Additionally, they were encouraged to think beyond justthe technical aspects by incorporating social, ethical, and personal implications. Students wereprompted to explore how these concepts intersect with their career aspirations, societal impact,and everyday life. The concept map had to be created digitally and clearly structured, showingdifferent levels of hierarchy and connections.The survey employed a Likert scale 10 to measure students’ perceptions of the assignment’simpact. Specifically, it assessed whether the assignment helped students understand how thecourse connected to other topics, with response options ranging from Strongly agree (5) toStrongly disagree (1). The scale was also used to evaluate whether the

Conference Session

DSAI Technical Session 5: Educational Technology and Innovative Tools

Collection

2025 ASEE Annual Conference & Exposition

Authors

D. Matthew Boyer, Clemson University; Lukas Allen Bostick, Clemson University; Ibrahim Demir, The University of Iowa; Bijaya Adhikari; Krishna Panthi, Clemson University; Vidya Samadi, Clemson University; Mostafa Saberian, Clemson University; Carlos Erazo Ramirez, The University of Iowa

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members introduced participants to tools and techniquessuch as data retrieval, programming with JavaScript, and hydrological modeling, culminating ina hackathon-style capstone project. The design for content delivery emphasizes interdisciplinarycollaboration, enabling participants to work in teams with diverse expertise. Instructionalmaterials included detailed tutorials, datasets for hands-on practice, and video lectures to supportself-paced learning. The hackathon challenged teams to apply their skills to real-world problems,fostering innovation and teamwork under time constraints.Participant ProfileThe workshop attracted graduate and undergraduate students, early career researchers and facultymembers from various disciplines, including civil

Conference Session

DSAI Technical Session 5: Educational Technology and Innovative Tools

Collection

2025 ASEE Annual Conference & Exposition

Authors

Handan Liu, Northeastern University

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

five years, it isshown that the students have a very high evaluation of my teaching, which can be confirmed bythe following TRACE data analysis: Semesters TRACE Teaching Effectiveness 2020-Spring 4.6 2020-Summer 5.0 2020-Fall 3.8 2021-Spring 4.9 2021-Fall 5.0 2022-Spring 4.9 2022-Fall 4.5 2023-Fall 4.8 2024-Spring 4.8 2024-Fall 4.6Furthermore, graduates have reported success in applying the skills learned to: • Research career paths in academia: for graduate students, this can make them stand out when

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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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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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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

platform with which admissions data may be used to predict student suc-cess and better track student progress over their academic career through data driven analysis.IntroductionThe graduate admissions committee at a top-ranked university reviews over 1,000 applicants an-nually and is a cornerstone of academic excellence, but the admissions process remains labor-intensive. Staff are required to manually review numerous student documents, particularly aca-demic transcripts, which contain essential data such as grades and course credits that must bemeticulously analyzed to ensure fair and consistent decisions. Since transcripts hail from a vari-ety of institutions globally, each with different formatting nuances as well as curriculum, difficul-ties

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

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Data Science and Artificial Intelligence (DSAI) Constituent Committee

student wished the course was over two semesters, and (4) What are otherthing(s) you would prefer/like to change about the way the course has been taught? What wouldyou like the instructor to do differently? Additional comments/concerns/suggestions/compliments,etc.” One student wished for longer class time, two hours dedicated to each lesson, and addingsteps to troubleshoot the code with respect to common errors. Based on the students’ response, itwould be essential to hire a TA and/or a Learning Assistant (LA) in this course.ConclusionsEnvironmental data science is an emerging field that encompasses several STEM domains andoffers exciting career prospects in a wide range of engineering applications. This paper presentsthe unique components of a

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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Data Science and Artificial Intelligence (DSAI) Constituent Committee

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