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)

practice of undergraduate education and student success. In addition, he is a fellow at the John N. Gardner Institute for Excellence in Undergraduate Education. Professor Heileman’s work on analytics related to student success has led to the development of a theory of 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.Ahmad Slim, The University of Arizona Dr. Ahmad Slim is a PostDoc researcher at the University of Arizona, where he specializes in educational data mining and machine learning. With a Ph.D. in Computer Engineering from the University of New Mexico, he leads

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Amirreza Mehrabi, Purdue Engineering Education; Jason Morphew, Purdue University, West Lafayette

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Data Science & Analytics Constituent Committee (DSA)

provides the potential to customize and personalize educational interventions to helplower-performing students as well as challenge higher-performing students. However, the benefitspromised by these cutting-edge technologies depends on the ability to assess student performanceaccurately and equitably. With the rise of artificial intelligence and adaptive and personalizedassessment, such as within CAT, the impact of CF and item order on the accuracy of assessmentsis critical to explore. Additionally, the IRT parameters used for ability estimation within CAT arealso related to item order. Additionally, the effect of CF is individual (e.g., [28]) meaning that therelationship between CF and other traits needs to be explored to ensure equity in

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)

ensure students have access to efficient, seamless, and simple ways to transfer from a community college to a university in Arizona. He serves on the board of the Association for Undergraduate Education at Research Universities, a consortium that brings together research university leaders with expertise in the theory and practice of undergraduate education and student success. In addition, he is a fellow at the John N. Gardner Institute for Excellence in Undergraduate Education. Professor Heileman’s work on analytics related to student success has led to the development of a theory of curricular analytics that is now being used broadly across higher education in order to inform improvement efforts related to

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

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Data Science & Analytics Constituent Committee (DSA)

University, where she also serves as director of the Usability Lab. Dr. Zhang holds a Ph.D. and an M.S. in informatio ©American Society for Engineering Education, 2024 Preparing Undergraduate Data Scientists for Success in the Workplace: Aligning Competencies with Job Requirements1. Introduction The increased use of Data Science technologies, particularly artificial intelligence andmachine learning has caused an increase in demand for skilled Data Science professionals[1,2,3]. This demand is driven by the rising dependence of businesses on these technologies toinform strategic decisions [1,2,3]. The Data Science domain is multidisciplinary, encompassingskill sets, including statistics

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

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Data Science & Analytics Constituent Committee (DSA)

], andprevious work where writing short-answer explanations in response to conceptually challengingquestions in STEM classrooms has found that writing improves student confidence, chances ofpicking a correct answer and better prepare students for group and larger class discussions [15],[16], [35], [36]. Additionally, written responses give instructors insight into student thinking andhow they utilize pieces of knowledge to construct explanations. Thus, these responses provide awealth of information. However, it is often difficult for instructors and researchers to read andanalyze large amounts of text to find information about trends and patterns in student thinking.Machine Learning Applications to Education ResearchMachine learning has been used in

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Smitesh Bakrania, Rowan University

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Data Science & Analytics Constituent Committee (DSA)

://doi.org/10.1007/s10639-022-11323-x12. C. Herodotou, B. Rienties, A. Boroowa, Z. Zdrahal, and M. Hlosta, “A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective,” Educational technology research and development, vol. 67, no. 5, pp. 1273–1306, 2019, https://doi.org/10.1007/s11423-019-09685-0.13. Suzanne L. Dazo, Nicholas R. Stepanek, Aarjav Chauhan, and Brian Dorn. 2017. Examining Instructor Use of Learning Analytics. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '17). Association for Computing Machinery, New York, NY, USA, 2504–2510. https://doi.org/10.1145/3027063.305325614. Knight, David B.; Brozina

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 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

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Data Science & Analytics Constituent Committee (DSA), Diversity

and curriculum designers as they strive to create edu-cational experiences that are both challenging and supportive, thereby enhancing student learningoutcomes and overall academic success. As we explore these dimensions of curriculum complex-ity, it becomes apparent that traditional methods of analysis, particularly those used in machinelearning and data analytics, need to be revised to fully capture the dynamic nature of educationalenvironments. While adept at identifying patterns and correlations, these conventional methodsoften need help understanding the complex causal relationships in academic settings. This leadsto a critical analysis of these limitations and the need for more nuanced approaches in educationalresearch. In educational

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

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Data Science & Analytics Constituent Committee (DSA)

research.BackgroundNLP is an interdisciplinary field encompassing machine translation, text processing, andartificial intelligence. It has emerged as a powerful tool for automating the evaluation of textualdata in educational settings [5]. Research in the use of NLP in education has delved into thecomparative analysis of NLP coding techniques with traditional manual coding methods, aimingto assess the reliability, validity, and efficiency of automated approaches [6] - [10]. Thesecomparative studies have sought to identify the strengths and limitations of NLP technologies incapturing the nuances of student language expression, as well as their potential to replace orcomplement human expertise in data analysis processes [11].A majority of this research has

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

, collaborative filtering, latent treegraphical models, student success, graduation rates, educational data mining1 IntroductionIn our study, we explore analytics focusing on a crucial aspect of student success: the curriculumpathways that lead students toward achieving their learning outcomes and ultimately earningtheir degrees. In the realm of higher education, the role of analytics is increasingly recognizedas a tool for decision-making that enhances student success outcomes. For example, various ini-tiatives have used student demographics and prior academic performance to guide interventionssuch as counseling, mentoring, and tutoring to improve retention and graduation rates 1,2,3 . Ourperspective emphasizes that the core of student academic

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 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

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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 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)

, Baker University .Zahraa Marafie, Kuwait UniversityPatricia Henriquez-CoronelLior Shamir, Kansas State University Associate professor of computer science at Kansas State University.Ella Lucille Carlson, Kansas State UniversityJoshua Levi Weese, Kansas State University Dr. Josh Weese is a Teaching Assistant Professor at Kansas State University in the department of Computer Science. Dr. Weese joined K-State as faculty in the Fall of 2017. He has expertise in data science, software engineering, web technologies, computer science education research, and primary and secondary outreach programs. Dr. Weese has been a highly active member in advocating for computer science education in Kansas including PK-12 model standards

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)

future strategies to investigate future strategies andpolicies for integrating GAI into engineering education, paving the way for a more informed andadaptive approach to technology in assessment.7. AcknowledgmentWe thank all research participants who shared their time and experiences with us. This material isbased upon work supported by the National Science Foundation (NSF) under Grant No. 211363.Any opinions, findings, conclusions, or recommendations expressed in this material are those ofthe authors and do not necessarily reflect the views of the NSF.References[1] T. K. F. Chiu, “The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney,” Interact. Learn. Environ., vol

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Paula Francisca Larrondo, Queen's University; Brian M Frank P.Eng., Queen's University; Julian Ortiz, Queen's University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

geostatistical ore body estimation and simulation, and geometallurgical modeling using statistical learning. Dr. Ortiz’s previous roles include Head of Department at Queen’s University and Universidad de Chile. ©American Society for Engineering Education, 2024 Work-in-Progress: Fine-Tuning Large Language Models for Automated Feedback in Complex Engineering Problem-SolvingAbstractThis paper presents work in progress (WIP) toward using artificial intelligence (AI), specificallythrough Large Language Models (LLM), to support rapid quality feedback mechanisms withinengineering educational settings. It describes applying to LLMs to improve the feedbackprocesses by providing information directly to 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

-transfer-students-earn-bachelors-degrees- excess-credits.pdf.[10] J. J. Giesey and B. Manhire. An analysis of bsee degree completion time at ohio university. Journal of Engineering Education, 92(3):275–280, 2003.[11] S. K. Hargrove and D. Ding. An Analysis of B.S.I.E. Degree Completion Time at Morgan State University. In International Conference on Engineering Education. International Network for Engineering Education and Research, October 2004.[12] M. M. Hossain and M. G Robinson. How to motivate us students to pursue stem (science, technology, engineering and mathematics) careers. Online Submission, 2012.[13] D. R. Hush, E. S. Lopez, W. Al-Doroubi, T. Ojha, B. Santos, and K. Warne. Analyzing student credits. 2022

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

. Furthermore, the hierarchical gradingscale provides more pathways for students to successfully pass a course. Educators interestedin using this mixed course design should consider the suggestions mentioned above in orderto more effectively run a flipped classroom and ensure proficiency, if not mastery, is achievedby all across all attempted modules.ReferencesAnderson, L. W. (1975). Major assumptions of mastery learning. In Annual Meeting of the Southeast Psychological Association.Bergmann, J. and Sams, A. (2012). Flip Your Classroom: Reach Every Student in Every Class Every Day. Flipped Learning Series. International Society for Technology in Education.Deddeh, H., Main, E., and Fulkerson, S. (2010). Eight steps to meaningful grading. The Phi Delta

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)

Opportunities; 2) Ongoing Professional Development and Upskilling; and 3)Practical Applications. As such, it can provide opportunities for career preparedness, fosteringnew competencies, and a need to gain hands-on experience using data science to create value andsolve problems. The results of this work can have important implications for educators,administrators, and professionals looking to incorporate data science into engineering praxis.Keywords: Data Science, Non-Computing Engineers, Technology Acceptance Model, Reddit,LDA, Web Scraping1. IntroductionData science is an interdisciplinary field that involves extracting knowledge and insights fromdata (i.e., a collection of information or facts) using scientific methods, algorithms, and tools [1].It

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

Saquib Ahmed, The State University of New York Buffalo State University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Paper ID #41278Versatile Recognition of Graphene Layers from Optical Images Under ControlledIllumination Through Green-Channel Correlation MethodProf. Saquib Ahmed, The State University of New York Buffalo State University Dr. Ahmed uses DFT, MD, and various Data Analytics tools such as ML and Neural Networks to probe atomistic, molecular, and device level phenomena within photovoltaics, battery and supercapacitors, 2D and quantum materials, and semiconductors. ©American Society for Engineering Education, 2024 Recognition of graphene layers from optical images in varied lighting conditions using the

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Marjan Eggermont, University of Calgary

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

complementary studies course. On average the class has 25 students.We start the class with a discussion of the short article What Art Unveils by Alva Noë whichusually allows us to find aspects we agree on so that we have a common understanding of art inthe course. ‘Artists make stuff’ – we were able to agree on that. Noë’s hypothesis is “that artistsmake stuff not because the stuff they make is special in itself, but because making stuff is specialfor us. Making activities — technology, for short — constitute us as a species. Artists make stuffbecause in doing so they reveal something deep and important about our nature, indeed, … aboutour biological nature [4].” He continues the article that ‘art makes things strange’: take a groupof engineering

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

tonghui xu, University of Massachusetts, Lowell; Hsien-Yuan Hsu, University of Massachusetts, Lowell

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

interest, choice, and performance,” Journal of vocational behavior, 45(1), 79-122.[4] Eccles, J. S., & Wigfield, A. (2002). “Motivational beliefs, values, and goals,” Annual review of psychology, 53(1), 109-132.[5] Ridgeway, C. L., & Correll, S. J. (2006). “Consensus and the creation of status beliefs,” Social Forces, 85(1), 431-453.[6] Qiu, D., Li, X., Xue, Y., Fu, K., Zhang, W., Shao, T., & Fu, Y. (2023). Analysis and prediction of rockburst intensity using improved DS evidence theory based on multiple machine learning algorithms. Tunnelling and Underground Space Technology, 140, 105331.[7] Mann, A., & DiPrete, T. A. (2016). “The consequences of the national math and science performance environment for gender

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

use of ChatGPT.The assessment responses revealed some student reasoning for using AI that was distinct fromthe themes in the survey responses. These reasons to use AI on an assessment includedovercoming a language barrier, feeling stuck on a problem, or having trouble getting started witha problem. Student reasoning around using AI when they “got stuck” was one that we found bothchallenging to characterize as researchers and intriguing as engineering educators. For example,one student wrote on the MATLAB quiz, I used chatGPT ALOT this exam and it shocked me how fast I was able to learn things on the go. … First I did the code on my own, then when I got stuck I used chatGPT to check the answer and see how I could adapt it to

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

. Gunasekara, J. L. Pallant, J. I. Pallant, and E. Pechenkina, “Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators,” Int. J. Manag. Educ., vol. 21, no. 2, p. 100790, Jul. 2023, doi: 10.1016/j.ijme.2023.100790.[19] A. Alsharif, D. Knight, and A. Katz, “The Evolution of Technology in Education and the Emerging Role of Generative A,” in In X. Lin, R. Y. Chan, & S. Sharma (Eds.), ChatGPT and global higher education: Using artificial intelligence in teaching and learning, STAR Scholars Press, 2024 (in Publication).[20] A. Katz, U. Shakir, and B. Chambers, “The Utility of Large Language Models and Generative AI for Education Research

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

://doi.org/10.3390/educsci904027614. Shi, Y., Yang, H., MacLeod, J., Zhang, J., & Yang, H. H. (2020). College Students’ Cog- nitive Learning Outcomes in Technology-Enabled Active Learning Environments: A Meta-Analysis of the Empirical Literature. Journal of Educational Computing Re- search,58(4), 791–817. https://doi.org/10.1177/073563311988147715. Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N.,Cintrón, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones, L., Jordt, H., Keller, M., Lacey, M. E., Littlefield, C. E., … Freeman, S.(2020). Active learning narrows achievement gaps for underrepresented students in un- dergraduate science

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)

education to improvelearning efficiency and create a more responsive learning environment. Emotion speechrecognition can be adopted to gauge students' emotional states during lectures, discussions, orassessments in a classroom environment. Immediate feedback can be provided to the instructorabout the overall emotional engagement of the class or specific students. Implementing emotionrecognition technology based on both speech and facial expression in online engineering coursescan enhance the feedback and engagement mechanisms for students. Emotion recognition can beused to identify when students are struggling, bored, or disengaged. Based on these insights, theonline learning platform can dynamically adapt the content, pace, or delivery method to

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

computer science track in the interdisciplinary curriculum, wherethe goal is to provide a foundational presentation of computer science principles within thecontext of an interdisciplinary graduate program. The courses are designed to support learners inidentifying common data structures and sources, using information technology and relevantprogramming environments to convey and retrieve information, and identifying processes andmechanisms commonly used to retrieve, assess, re-engineer, manipulate, and visualize data. Thediverse backgrounds of the learners make this an interesting challenge for curriculum designers.How can a professional master’s degree successfully introduce foundational computer scienceconcepts for adult learners from diverse