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

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

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Data Science & Analytics Constituent Committee (DSA)

professional development programs, and like the currentconversation on GAI, potentially help inform policies on adoption and usage [5], [10]. This isour next step to proceed with a broader study.Another important research direction can be investigating the impact of GAI on learningoutcomes and student engagement in engineering education. As GAI tools become moreprevalent, it is crucial to understand how they influence not just assessment practices but alsostudents' learning processes and outcomes.Finally, interdisciplinary collaborations involving educators, technologists, and ethicists areessential to address the complex challenges posed by GAI in education. Such collaborations canlead to the development of ethical guidelines, effective pedagogical

Conference Session

DSA Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Galen I. Papkov, Florida Gulf Coast University; Jiehong Liao, Florida Gulf Coast University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

Paper ID #42267Effectiveness of a Semi-Mastery-Based Learning Course DesignDr. Galen I. Papkov, Florida Gulf Coast University Dr. Galen Papkov is a Professor of Statistics at Florida Gulf Coast University where he founded the minor in statistics and currently serves as the Graduate Program Coordinator for the M.S. Program in Applied Mathematics. His collaborations have resulted in publications in engineering education, agriculture, and health sciences. Originally from New York, he earned his Ph.D. in Statistics from Rice University. Galen’s research interests include experimental design, survey design and data analysis

Conference Session

DSA Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Duncan Davis, Northeastern University; Nicole Alexandra Batrouny, Northeastern Univeristy; Adetoun Yeaman, Northeastern University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

engineering design, collaboration in engineering, decision making in engineering teams, and elementary engineering education.Dr. Adetoun Yeaman, Northeastern University Adetoun Yeaman is an Assistant Teaching Professor in the First Year Engineering Program at Northeastern University. Her research interests include empathy, design education, ethics education and community engagement in engineering. She currently teaches Cornerstone of Engineering, a first-year two-semester course series that integrates computer programming, computer aided design, ethics and the engineering design process within a project based learning environment. She was previously an engineering education postdoctoral fellow at Wake Forest University

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Tony Maricic, New York University Tandon School of Engineering; Nisha Ramanna, New York University Tandon School of Engineering; Alison Reed, New York University Tandon School of Engineering; Rui Li, New York University; Jack Yang, New York University Tandon School of Engineering

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Data Science & Analytics Constituent Committee (DSA)

user experience survey. The survey results gave some constructivefeedback for the developers. Overall, the project can deliver a feasible solution for courseinstructors to handle many student project teams. In the future, a generative AI feature -CHATME will also be available on the front end to help the user check the status of each studentgroup, which is built using NLTK and TensorFlow. Moreover, if a team issue arises, theplatform will alert the users, and provide constructive suggestions on how to improve the groupperformance.IntroductionIn engineering education, fostering collaborative skills [1] among students is crucial, and team-based learning has become the primary approach. It is an approach particularly prevalent infoundational

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

. ©American Society for Engineering Education, 2024 Bridging Theory and Practice: Building anInclusive Undergraduate Data Science Program Mehmet Ergezer, Mark Mixer, Weijie Pang Wentworth Institute of Technology Boston MA, 02115 USA {ergezerm, mixerm, pangw}@wit.edu Abstract As the field of Data Science (DS) continues to evolve, institutions of higher education face the challenge of developing curricula that prepare students for the industry’s rapidly changing landscape. In this paper, we will present a case study of the development and

Conference Session

DSA Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Fengbo Ma, Northeastern University; Xuemin Jin, Northeastern University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

actors and is categorizedwith labels such as anger, happiness, sadness, neutral, surprise, fear, frustration, and excitement.Each entry, typically a few seconds long, is an utterance annotated by 3 reviewers.In this study, we select only utterances that are classified as anger and neutral, totaling 3411 audioclips. Here anger is the class of interest and set as class 1 and neutral as class 0. This selectionaligns with our goal of examining transitions from a neutral state to a negativity state, simulatingscenarios where, detection is crucial for an AI's planning and reaction in collaboration with humanresponders. An application in engineering education is to detect students' negative feedback duringa lecture.The audio clips from the IEMOCAP are

Conference Session

DSA Technical Session 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Betul Bilgin, The University of Illinois at Chicago; Naomi Groza, The University of Illinois at Chicago

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

demonstrate the tangible benefits of data science in chemical engineering can make the subject matter more compelling and relevant to students' future careers. 4. Bridging the Skills Gap: Acknowledging and acting upon students' perceptions and willingness to learn data science can lead to the development of targeted programs that bridge the gap between traditional chemical engineering education and the emerging needs of the industry. This involves not only imparting technical data science skills but also fostering a mindset oriented towards innovation, continuous learning, and adaptability. 5. Facilitating Industry-Academia Collaboration: Understanding student perspectives can

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)

Paper ID #41210Data-Science Perceptions: A Textual Analysis of Reddit Posts from Non-ComputingEngineersMr. Nicolas Leger, Florida International University Nicolas L´eger is currently an engineering and computing education Ph.D. student in the School of Universal Computing, Construction, and Engineering Education (SUCCEED) at Florida International University. He earned a B.S. in Chemical and Biomolecular Engineering from the University of Maryland at College Park in May 2021 and began his Ph.D. studies the following fall semester. His research interests center on numerical and computational methods in STEM education and in

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Harpreet Auby, Tufts University; Namrata Shivagunde, University of Massachusetts, Lowell; Anna Rumshisky, University of Massachusetts, Lowell; Milo Koretsky, Tufts University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Paper ID #43642Using Machine Learning to Analyze Short-Answer Responses to ConceptuallyChallenging Chemical Engineering Thermodynamics QuestionsHarpreet Auby, Tufts University Harpreet is a graduate student in Chemical Engineering and STEM Education. He works with Dr. Milo Koretsky and helps study the role of learning assistants in the classroom as well as machine learning applications within educational research and evaluation. He is also involved in projects studying the uptake of the Concept Warehouse. His research interests include chemical engineering education, learning sciences, and social justice.Namrata

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

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

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Data Science & Analytics Constituent Committee (DSA)

University of New Mexico and brings over fifteen years of professional experience as a technology engineer, including significant roles in cloud computing and infrastructure development at a big technologies company and financial services industry.Roxana Sharifi, The University of Arizona ©American Society for Engineering Education, 2024 Paper ID #42637 Roxana Sharifi is a second-year master’s student in Electrical and Computer Engineering at the University of Arizona, where she also serves as a Graduate Research Assistant in the Curricular Analytics Lab. She holds a bachelor’s degree in Software

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)

©American Society for Engineering Education, 2024 Envisioning and Realizing a State-wide Data Science EcosystemAbstractThis paper describes the vision, strategy, plan, and realization of a state-wide rigorous datascience educational ecosystem. The need for developing data science degree programs andeducation has been well-established and, in our state, a blue-ribbon panel with industry,academic, and government representatives defined the needs of the state. Additionally, a well-established “think and do tank” published several reports on the importance of data scienceeducation and graduates. As we began to develop our programs separately, it occurred to us thatwe were in a small enough state that, if we chose to do so, we could work

Conference Session

DSA Technical Session 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Emma Fox, Franklin W. Olin College of Engineering; Zachary del Rosario, Olin College of Engineering

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

. As shown above, linguistic differences between engineering andstatistics can have potentially deadly consequences. Ideally, practitioners on interdisciplinaryteams would work openly and clarify all terminology to minimize miscommunication.As engineering educators, we can encourage a more “open stance” by exposing our students todifferent interpretations of terms. This can seed a more open view of terminology by showingthat terms are used differently across an increasingly interdisciplinary workplace. Additionally,we can model a productive set of behaviors where collaborators ask “This is what error means tome, how do you interpret this term?” In this way, we can (hopefully) train engineers to have suchdiscussions in their professional

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Emily Nutwell, The Ohio State University; Thomas Bihari, The Ohio State University; Thomas Metzger, The Ohio State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

. She holds a BS in mechanical engineering, MA in educational studies, and a PhD in Engineering Education where her research focuses on digital learning environments for the STEM workforce.Thomas Bihari, The Ohio State UniversityThomas Metzger, The Ohio State University ©American Society for Engineering Education, 2024 An Online Interdisciplinary Professional Master’s Program in Translational Data AnalyticsAbstractThis paper describes an interdisciplinary data analytics professional master’s program whichincludes courses from the disciplines of computer science, statistics, and design. The onlinecurriculum structure specifically addresses the needs of working professionals

Conference Session

DSA Technical Session 5

Collection

2024 ASEE Annual Conference & Exposition

Authors

Safia Malallah, Kansas State University; Ejiro U Osiobe, Baker University; Zahraa Marafie, Kuwait University; Patricia Henriquez-Coronel; Lior Shamir, Kansas State University; Ella Lucille Carlson, Kansas State University; Joshua Levi Weese, Kansas State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

in 2019 with an implementation guide the following year. Work on CS teacher endorsement standards are also being developed. Dr. Weese has developed, organized and led activities for several outreach programs for K-12 impacting well more than 4,000 students. ©American Society for Engineering Education, 2024 Developing an Instrument for Assessing Self-Efficacy Confidence in Data Science Safia Malallah, Kansas State University, safia@ksu.edu Ejiro Osiobe, Baker University's, Jiji.osiobe@bakerU.edu Zahraa Marafie, Kuwait University, Zahraa.Marafie@ku.edu.kw Patricia Coronel, ULEAM, patricia.henriquez@uleam.edu.ec Lior Shamir, Kansas State

Conference Session

DSA Technical Session 2

Collection

2024 ASEE Annual Conference & Exposition

Authors

Ben D Radhakrishnan, National University; James Jay Jaurez, National University; Nelson Altamirano, National University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

includes application of AI for project management, sustainability and data center energy.Mr. James Jay Jaurez, National University Dr. Jaurez is a dedicated Academic Program Director and Associate Professor in Information Technology Management at National University where he has served since 2004. Dr. Jaurez is also a FIRST Robotics Head Coach since 2014 and leads outreach in robotiNelson Altamirano, National University ©American Society for Engineering Education, 2024Application of Data Analysis and Visualization Tools for US Renewable SolarEnergy Generation, its Sustainability Benefits, and Teaching In Engineering Curriculum Ben D Radhakrishnan, M.Tech., M.S

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; Husain Al Yusuf, The University of Arizona; Yiming Zhang, The University of Arizona; Asma Wasfi; Mohammad Hayajneh; Bisni Fahad Mon, United Arab Emirates University; Ameer Slim, University of New Mexico

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Data Science & Analytics Constituent Committee (DSA)

higher education.Asma WasfiMohammad HayajnehBisni Fahad Mon, United Arab Emirates UniversityAmeer Slim, University of New Mexico ©American Society for Engineering Education, 2024 Enhancing Academic Pathways: A Data-Driven Approach to Reducing Curriculum Complexity and Improving Graduation Rates in Higher Education Ahmad Slim† , Gregory L. Heileman† , Husain Al Yusuf† , Ameer Slim‡ , Yiming Zhang† , Mohammad Hayajneh• , Bisni Fahad Mon• , Asma Wasfi Fayes• {ahslim@arizona.edu, heileman@arizona.edu, halyusuf@arizona.edu, ahs1993@unm.edu, yimingzhang1@arizona.edu, mhayajneh@uaeu.ac.ae

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)

curricular analytics that is now being used broadly across higher education in order to inform improvement efforts related to curricular efficiency, curricular equity, and student progression.Dr. Yiming Zhang, The University of Arizona Yiming Zhang completed his doctoral degree in Electrical and Computer Engineering from the University of Arizona in 2023. His research focuses on machine learning, data analytics, and optimization in the application of higher education. ©American Society for Engineering Education, 2024 Minimizing Curricular Complexity through Backwards Design Gregory L. Heileman and Yiming Zhang {heileman, yimingzhang1

Conference Session

DSA Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Yiming Zhang, The University of Arizona; Gregory L. Heileman, The University of Arizona; Ahmad Slim, The University of Arizona; Husain Al Yusuf, The University of Arizona

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

outcomes and optimize educational processes. Husain Al Yusuf holds an M.Sc in Computer Engineering from the University of New Mexico and brings over fifteen years of professional experience as a technology engineer, including significant roles in cloud computing and infrastructure development at a big technologies company and financial services industry. ©American Society for Engineering Education, 2024 Optimizing Transfer Pathways in Higher Education Yiming Zhang, Gregory L. Heileman, and Ahmad Slim {yimingzhang1, heileman, ahslim}@arizona.edu Department of Electrical & Computer Engineering

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

Information at the University of Arizona. She received her bachelor’s degree in Computer Science from the University of Arizona. She is interested in data visualization, machine learning, human computer interaction, learning analytics and educational data mining.Ameer Slim, University of New Mexico ©American Society for Engineering Education, 2024 Causal Inference Networks: Unraveling the Complex Relationships Between Curricular Complexity, Student Characteristics, and Performance in Higher Education Ahmad Slim† , Gregory L. Heileman† , Ameer Slim‡ , Kristi Manasil† , Melika Akbarsharifi† {ahslim