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)

-driven, where actual student data and actual university curricula areutilized. Five thousand seventy-three student records from the University of New Mexico (UNM)are used for this purpose. Results demonstrate the restructuring impact on an engineering cur-riculum, particularly the computer engineering program at UNM. The effect is an improvementin the graduation rates of the students attending the revised engineering programs. These resultsare validated using a Markov Decision Processes (MDP) model. Furthermore, the findings of thispaper showcase the practical benefits of our approach and offer valuable insight for future ad-vancements in curriculum restructuring methodologies.keywords: curricular complexity, Markov decision processes

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)

the institution, allowing for athorough understanding of their existing academic offerings. Our Graduate Research Assistantcollaborates with the various academic representatives to design a program that integrates theirinstitution’s offerings into the statewide ecosystem.As part of this collaborative effort, a preliminary course equivalency assessment is conducted.This involves an examination and comparison of the courses already established at theinstitutions. This initial evaluation allows us to identify potential areas of alignment and establishthe groundwork for the integration of those courses into the program.Through these engagements, we not only provide valuable insights into the benefits of optinginto the program but also actively

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

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Data Science & Analytics Constituent Committee (DSA), Diversity

-making, is one of the critical ways practical labs and hands-on experience can be facilitated (Desha et al., 2007). Creating a toolset for educational andprofessional environments requires utilizing principles related to force sustainability andanalytical tools that are specifically matched with data analysis. To demonstrate and facilitate theengineering management student experience, the researchers utilized foundational tools likeExcel, tableau, and Orange and sophisticated experiments using IBM Watson.Excel serves as a significant touch point for graduate students in analyzing data on sustainabilityconcepts. It also offers a platform for statistical viewpoints and a practical bridge to decision-making. Engineering management students could

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

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Data Science & Analytics Constituent Committee (DSA), Diversity

learning, and data visualization [1]. Thisintegration is crucial for handling the increasing complexity and size of data sets in chemicalengineering research and practice [2]. Data science has particularly impacted molecular sciencein chemical engineering, with applications in molecular discovery and property optimization [3].The development of a cyberinfrastructure for data-driven design and exploration of chemicalspace further underscores the potential of data science in transforming chemical research [4].The alignment of data analytics and strategy is transforming the chemical industry, with dataplaying a crucial role in production, research, marketing, and customer service strategies [5]. Theuse of big data and analytics in chemical

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

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Data Science & Analytics Constituent Committee (DSA)

University, lshamir@ksu.edu Ella Carlson, Kansas State University, ellacarlson23@ksu.edu Joshua Levi Weese, Kansas State University, weeser@ksu.edu Abstract The field of data science education research faces a notable gap in assessment methodologies, leading to uncertainty and unexplored avenues for enhancing learning experiences. Effective assessment is crucial for educators to tailor teaching strategies and support student confidence in data science skills. We address this gap by developing a data science self-efficacy survey aimed to empower educators by identifying areas where students lack confidence, enabling the design of targeted plans to bolster data science education

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

relationships and dependencies withinthe university setting. This approach is not limited to predictive capabilities, as seen in traditionalmachine learning models; it also enables us to engage in objective “what-if” analyses. These anal-yses delve into counterfactual reasoning, allowing us to explore hypothetical scenarios and theirpotential impacts on student outcomes. We aim to utilize this model to better understand the causalrelationships between curriculum complexity and student performance metrics. By doing so, weaim to contribute a novel perspective to educational research discourse, offering theoretical insightsand practical implications for curriculum design and student success strategies. This study not onlyseeks to fill a critical gap in

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)

engineering school). I've been interested in data science and algorithms for a while, and took a couple Python courses on Coursera before starting my latest job. While I enjoyed it, I found it somewhat challenging.The third topic centers on multiple applications, including terms such as career, problem,energy, work, computer, knowledge, machine, advice, job, design, focus, something, research,skill, level, sense, reason, entry, degree, discipline, construction, chemical, number, modeling,example, site, role, sound, tool, company. The keywords seem to indicate studying attitudesrelating to career impacts, usefulness for engineering work, required skills and training, andgeneral considerations around the role and value of data science across

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)

betweenengineering education researchers and machine learning researchers, we can work together at theintersection of machine learning and discipline-based education research. During the qualitativecoding process, we shared multiple perspectives on how students could discuss differentconcepts so that we could work towards making a more diverse codebook. When evaluating thecodes generated by machine learning analysis alongside the results from manual coding, wediscussed how to best work towards a better coding process to help train algorithms.LimitationsThis study did not factor in the differences between instructors and their context or instructionalmoves. For example, some instructors may emphasize the importance of written responsesdifferently, impacting