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)

Paper ID #42646Enhancing Academic Pathways: A Data-Driven Approach to Reducing CurriculumComplexity and Improving Graduation Rates in Higher EducationDr. 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 initiatives to develop analytics solutions that support strategic decision-making in academic and administrative domains. His work includes the creation of predictive models and data visualization tools that aim to

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

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Data Science & Analytics Constituent Committee (DSA)

used to satisfy both the Math General Education as well the Required Major Courses re-quirements.The formal framework we will use to represent a set of degree requirements in a requirements treeinvolves two types of structures, one for storing a collection of course requirements, i.e., coursesets, and the other for storing a collection of requirements, i.e., requirement sets, as shown inFigure 2. A course set is simply a set of course requirements, along with the minimum number ofcredit hours required to satisfy the course set requirement. More specifically, a single course-setrequirement, csj , consists of two elements: csj = (ρj , θj )where ρj is a list of the |ρj | courses in

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

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 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

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

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Data Science & Analytics Constituent Committee (DSA), Diversity

initial stage of the research involved a literature review to gain more understanding of datavisualization techniques, engineering education topics, and visual design principles. Academicdatabases, journals, and research papers were consulted to develop case studies that investigatedrelevant engineering education topics. While exploring data visualization methods, we made noteof techniques that resulted in more creative final products. Simultaneously, publicly availableengineering education data was procured from sources such as the National Center for EducationStatistics (NCES). Engineering education is a colorful, dynamic, and deep field that addresses awide range of topics. Thus, data was narrowed to focus on insights into student demographics

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

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Data Science & Analytics Constituent Committee (DSA)

from previous terms is underway and will be presented in futurepublications.ConclusionOur study provides valuable insights into the application of LLM models to engineering designeducation. We show that open-source distilBERT models, when fine-tuned with small datasets of400 sentences on the problem definition dimensions of a generic problem-solving process usedin engineering design, can achieve an accuracy of approximately 80%. This performancesuggests that, as shown by Zhao et al. [50], the need for large datasets to fine-tune LLMseffectively may not be as restrictive as previously thought. These findings are particularlypromising for instructors or educational settings with limited resources for extensive datasetdevelopment who would prefer

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 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

degrees of graduated learners.Given the learner audience, the curriculum of the computer science curriculum needed to beapproached considering that these learners are new to computer science concepts. Although thisis a graduate program, the purpose of this program is to provide rigorous coursework to onboardlearners to data science and analytics methods. Rather than a program that provides a deeper diveinto a specific topic area, the goal of this professional master’s degree is to provide anopportunity for individuals with expertise in diverse areas such as business, education, and alliedhealth fields to incorporate data analytics into their professional practice.3. Curriculum OverviewAs a professionalmaster’s degree, thecurriculum focuses

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)

a shortage offreshly graduated, qualified data scientists, raising concerns for both academia and industries[1, 2]. Additionally, research on data science education assessments lacks, leaving manyuncertainties surrounding students’ pre-graduation skills. This paper addresses this limitationand develops a data science self-efficacy survey to evaluate and quantify individuals’confidence levels in applying data science skills to build data-driven solutions, with the goalto enhance the learning experience within data science education. Also, remedial activitieswere proposed to boost students’ confidence based on individual confidence levels. Surveydevelopment followed a modified Vinay approach, which guided construction of customizedassessments

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

Diversified Workforce in Nuclear Energy and SecurityAbstractA workforce equipped with essential data analytics skills is crucial to maintaining the UnitedStates' economic growth and security, especially for nuclear energy industries and non-proliferation. Data analytics skills are in high demand in order to generate data-driven, robustsolutions to solve global challenges and support decision-making for stakeholders in nuclearenergy and security areas. This paper presents the technical approach that facilitates theintegration of fundamental data analytics skills into pipeline building toward a diversifiedworkforce through a suite of well-designed, comprehensive summer enrichment programs forhigh school, undergraduate and graduate students. The summer

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

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Data Science & Analytics Constituent Committee (DSA)

that integrated data-driven and multiple-theory-driven approaches.This blended approach can help researchers reduce predictors and retain interpretability. Basedon that, we conducted a study to analyze the Education Longitudinal Study of 2002 (ELS:2002)dataset to establish a procedure for identifying predictors. Multilevel modeling was then utilizedto study high school students’ persistence in STEM career aspirations.KEYWORDS:feature selection, STEM career aspirations, career theory, large scale survey, multilevel moldingIntroduction High school students’ aspirations for STEM occupations can significantly influence theirdecisions to pursue a STEM track in college or as a career. In 2016, there were only 568,000STEM graduates in the U.S

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

also address the educational andmotivational needs of their students. Research on the topic of student perceptions towards datascience has predominantly focused on the K-12 education sector. The significance ofcomprehending these perceptions is underlined by Kross, who advocates for educators,especially those with practical industry experience, to cultivate empathy towards the diversebackgrounds and expectations of students [16]. This aspect gains particular importance in thecontext of secondary education, for the integration of data science as a key interdisciplinary skill[17]. The selection of educational tools in teaching data science is another critical consideration,with Israel-Fishelson offering an insightful review of tools tailored for

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

underrepresented and marginalized groups in computing, data analysis, and artificial intelligence. Our Data Science program offers a pathway for community college graduates to complete the program in a short time window. In particular, we are developing a “2 + 2” option for students, where 2-year associate degrees from various local community colleges transfer effectively, only leaving 2 years left for completion of the BSDS degree for the students at our university. This initiative is not just about accessibility but is a deliberate strategy to welcome individuals from diverse educational backgrounds, thereby enriching the learning environment with a multiplicity of perspectives. Additionally, we focus on diversity and inclusion at

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)

,” “Continuous Education and CareerUpskilling,” and “Multiple Applications,” as seen in Figure 6. Figure 6. Keywords mapping based on topic modeling performed on the dataThe first topic is more career-oriented and contains these specific keywords such as work, time,engineer, position, experience, company, interview, school, internship, project, program, job,skill, application, software, automation. Overall, the keywords seem to capture potential attitudesrelating to the role and fit of data science in engineering work, required abilities, education andtraining, career implications, and general considerations around its perceived usefulness andapplicability.Words like “work,” “time,” “experience,” “position,” “company,” “project,” “job” might

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)

, 2024The Value and Instructor Perceptions of Learning Analytics for Small ClassesAfter the majority of education moved online during the COVID-19 pandemic, it becameincreasingly critical to gauge student learning and engagement without in-person interactions.Without the visual cues present in classrooms, instructors were blind to the nuances ofengagement afforded by face-to-face instructions. Instead, instructors relied on studentperformances on assessments as the proxy or the lagging indicator for engagement. Learninganalytics, on the other hand, provides an additional window into student engagement that isfrequently underutilized. Learning analytics uses the data generated as the students interact withthe learning management system (LMS) to

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