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)

dichotomy enables an articulation of the consequences of variation.Figure 1. Examples of real and erroneous sources of variability. Imperfections in a material leadto real variability, while slippage during mechanical testing leads to erroneous variability. Image drawn by Alana Huitric.Further complicating the interpretation of variation is the behavior of reification. Gould [18]defines reification as “the mental conversion of a person or abstract concept into a thing.”Originally introduced in Marxist theory by Georg Lukács [20], reification describes a kind of“forgetting” where the reified interpretation precludes other interpretations. Reification istherefore considered problematic: Gould treats it as the

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)

Paper ID #41938Paradigm Shift? Preliminary Findings of Engineering Faculty Members’Mental Models of Assessment in the Era of Generative AIMs. Isil Anakok, Virginia Polytechnic Institute and State University Ms.Anakok is Ph.D. candidate in the Department of Engineering Education at Virginia Tech. She has a Ms. degree in Mechanical Engineering at Virginia Tech, and Bs. in Mechatronics Engineering from Kocaeli University, Turkey.Kai Jun Chew, Embry-Riddle Aeronautical University, Daytona Beach Kai Jun ”KJ” Chew is an assistant professor in the Engineering Fundamentals department at Embry-Riddle Aeronautical University. He is

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)

Paper ID #41136The Value and Instructor Perceptions of Learning Analytics for Small ClassesDr. Smitesh Bakrania, Rowan University Dr. Smitesh Bakrania is an associate professor in Mechanical Engineering at Rowan University. He received his Ph.D. from University of Michigan in 2008 and his B.S. from Union College in 2003. His technical focus area is nanomaterials research. He is primarily involved in educational research with educational app development and instructional tools to engage students, including online learning and instructional video production. ©American Society for Engineering Education

Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

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

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Data Science & Analytics Constituent Committee (DSA)

green channel correlation method for versatile identification.Miah Abdullah Sahriar1†, Mohd. Rakibul Hasan Abed1†, Ratchanok Somphonsane2, Houk Jang3,Chang-Yong Nam3, Saquib Ahmed5,6*1 Department of Materials and Metallurgical Engineering (MME), Bangladesh University ofEngineering and Technology (BUET), East Campus, Dhaka-1000, Bangladesh2 Department of Physics, School of Science, King Mongkut’s Institute of TechnologyLadkrabang, Bangkok 10520, Thailand3 Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York11973, USA5 Department of Mechanical Engineering Technology, SUNY – Buffalo State University, 1300Elmwood Avenue, Buffalo, NY 14222, USACenter for Integrated Studies in Nanoscience and Nanotechnology

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

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Data Science & Analytics Constituent Committee (DSA)

further expand somefields as we know them.There is also a growing body of work looking at data science applications in engineering [6].Although we know it may be applied or beneficial for the broader field and its subfields (e.g.,mechanical, industrial, chemical), we are limited in our understanding of how non-computingengineers may apply it in their work or practice. With that said, it is necessary to understand hownon-computing engineers may apply data science in their work, as this remains a challenge in thefield. In the context of engineering education and practice, Beck et al.’s article suggests addingdata science as a “competency” in chemical engineering both in “the university curriculum or ina professional development context.” They also

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)

racial demographics that were not necessarilyrepresentative of engineering student populations at other universities [21]. The under-representation and overrepresentation of certain groups of students in this study may impact thegeneralization of the results regarding TA support and is acknowledged as a limitation of thissingle institution study.Additionally, the scope was confined to two engineering disciplines (mechanical and electricalengineering). Therefore, the themes and their relative importance may not translate directly toother engineering fields, especially those with a higher percentage of female students. While thethemes of TA support identified in this study may resonate with other engineering studentpopulations, their

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Marjan Eggermont, University of Calgary

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Data Science & Analytics Constituent Committee (DSA), Diversity

Paper ID #42654Let’s Get Physical: From Data Visualization to Data PhysicalizationDr. Marjan Eggermont, University of Calgary Marjan Eggermont is a Professor (Teaching), Associate Dean (Sustainability) and faculty member at the University of Calgary in the Mechanical and Manufacturing department of the Schulich School of Engineering. She co-founded and designs Zygote Quarterly, an online bio-inspired design journal (zqjournal.org). ©American Society for Engineering Education, 2024 Work in progress Let’s get physical: from data visualization to

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Neha Kardam, University of Washington; Denise Wilson, University of Washington

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Internal Review Board (IRB) under the code STUDY00000378.The study recruited undergraduate students from 21 courses in mechanical and electrical engineering,but the researchers did not engage directly with the students. All participants were informed that theirresponses would be kept confidential. Additional academic incentives, in the form of extra credit,were provided to students to support increased survey participation and all surveys were conductedelectronically.Data AnalysisRaw data from student responses was initially processed using Term Frequency-Inverse DocumentFrequency (TF-IDF) Vectorizer to convert the unstructured data into structured format [12]. TheTF-IDF Vectorizer provided by Sklearn.org calculates a score that reflects the

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 sup-port mechanisms integrated within the curriculum. This dimension concerns the depth and varietyof teaching methods, the rigor of content delivered, and the support systems in place to guide stu-dents through their educational journey. Studies have indicated that while a richly diverse instruc-tional approach can enhance learning, it may also lead to challenges if not adequately supported.The survey by Hiebert et al. highlights the potential negative impact of high instructional com-plexity on student engagement and comprehension 16 . When students are confronted with overlycomplex instructional methods with sufficient support, they can significantly improve their abil-ity to engage and understand the material. The delicate balance

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)

anticipatedgraduation rates. An examination reveals the Industrial Engineering program as having an exem-plar performance, evidencing a noteworthy 43% of students positioned for a 4-year graduation,coupled with a 6-year graduation rate at an impressive 77%. This attainment surpasses the over-all college average by a substantial margin, registering an increase of 20 percentage points andsurpassing the national average by 10 percentage points.Conversely, the Materials Science and the Environmental Engineering degree programs exhibit thelowest graduation rates within the purview of the College. A mere 6% of students in these programsare on track for a 4-year graduation. This constitutes a decrement of 17 percentage points from thecollege-wide average and a more

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)

] respectively, to conduct automatic grading of short-answers. Previously, we [1] finetuned T5 [48] and compared its results in assessing short studentresponses with GPT-3 [49]. However, we [1] only worked with one coded dataset. Most of thesestudies focused on small encoder-only or sequence-to-sequence Transformer models. They didnot train the state-of-the-art decoder-only Large Language Model's performance in assessingstudents' written explanations in science education.The state-of-the-art decoder-only transformer models are multi-layer neural networks withattention mechanisms. These state-of-the-art models have billions of parameters and are trainedon huge corpora of free text with the causal language modeling objective, which involvespredicting the

Conference Session

DSA Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Aidan Kenny, Northeastern University; Andrew L Gillen, Northeastern University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

engineering is often associated with innovation and advancement. However, apervasive challenge within this discipline is the sex imbalance of its institutions and workforce.Despite recent societal efforts to promote gender equality, engineering continues to exhibitdrastic underrepresentation of women. This carries issues related to equity as well as thediversity and innovation potential of engineering professions. Women comprise only 29% of thescience and engineering workforce and the ratio of men to women varies widely based onspecific fields. For example, in 2013, only 15% of engineers were women. This figure drops to8% for mechanical engineers and 11% for electrical engineers [5, 6]. Science and engineering arenecessary contributors for

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

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Data Science & Analytics Constituent Committee (DSA), Diversity

funded by DOE, USED, NASA and NSF.Dr. Mebougna L. Drabo, Alabama A&M University Dr. Mebougna L. Drabo is currently a professor of Mechanical Engineering at Alabama A&M University (AAMU). He is the chair of the department of Mechanical & Civil Engineering and Construction Management at AAMU. He is also serving as the director of the Alabama EPSCoR Agency for the Department of Energy. He joined AAMU in 2012, leveraging his expertise in teaching and mentoring STEM students while fostering on-campus research and DOE Lab internships. Currently, he directs the DOE/NNSA’s Consortium, SPINS and is working on integrating radiation detection systems into cyber manufacturing environments. His research interests

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

a long history, with roots dating backto the 1950s [4]. Early NLP systems were limited in their capabilities and largely relied onrule-based approaches, but the development of machine learning algorithms in the 1980s and1990s led to significant advances in the field [5]. Nowadays, NLP is a rapidly growing field thathas the potential to revolutionize the way we teach and learn [6]. By enabling computers tounderstand and process human language, NLP can help educators identify patterns and trends instudent learning, facilitate more personalized and effective instruction, and provide students withnew ways to interact with educational materials [6], [7]. NLP has a wide range of applications,including language translation, text summarization, and

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 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

whenstudents explained things thoroughly, were specific about what exactly they asked for examplesof, and exactly how AI helped them. Most students (87% - 90%) either did not use ChatGPT orused it in a productive manner (Figure 1).A student who uses ChatGPT to start a problem is missing the process of converting a wordproblem into a coding problem. Comparing their code to ChatGPT, deciding ChatGPT did abetter job, and submitting the AI code prevents us from seeing what the student built and howclose their code was to success. Lastly, students that replaced their effort with heavily usingChatGPT failed to show a mastery of the material. Each of these examples were deemedUnproductive for Learning how to code.In some cases, students made claims that were

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)

understanding of their strengths and areasfor improvement [8].Data Science Assessment PathwayVinay proposed a nine-step assessment pathway to create a customized data scienceassessment aligned with organizational goals using these competencies. These steps includeidentification of key competencies; categorization and prioritization; definition ofcompetency levels; development of assessment tools; scoring and evaluation rubrics;integration with organizational goals; feedback mechanisms; implementation and training;and iterative refinement. We incorporated steps first five steps to develop our survey, as theywere relevant to our goal of creating an assessment process for academia [3].MethodDesignThis study employed a quantitative approach to develop a