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)

success outcomes. However, finding efficient and effectivetransfer pathways between institutions is challenging, particularly when accounting for programrequirements that are constantly changing, students changing their major plans, the creation of newcourses, etc. Crafting a suitable plan for transfer students demands expert knowledge, effort, andsometimes collaboration among multiple institutions. Managing all of this complexity manuallyis partly accountable for the credit loss issue mentioned above. In this paper we consider the rolethat data and analytics can play in addressing this problem.To gain a deeper understanding of this challenge, we first formally define the Optimal TransferPathway (OTP) problem, which involves finding a two-year to

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 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

plan for their capstone project.The first semester also introduces an Agile project management flow adapted for data science.Rather than a traditional waterfall approach which requires significant upfront planning, Agileallows for iterations and adaptive solutions [27], [28]. For the capstone class, the project teamconsists of the analysts (the learners), a process expert which serves as a coach and facilitator(the instructor), and the product owners who represent the stakeholders (the sponsors) [27]. Oncethe project plan is defined, the project is broken down into 3-week long sprints where learnersdefine short-term sprint goals, evaluate the sprint results, and then plan the next sprint [28]. Theidea is that the project plan will serve as the

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)

empirical information from this platform assists advisors in aiding students in creatingacademic plans that provide students with the best chance for success while maximizing their credithour efficiency. In this paper, the architecture and the visual display of the cohort tracking analyticsplatform are briefly discussed. Then we pivot to focus on the results of the analyses, comparingand contrasting three groups that consist of engineering disciplines within a department, depart-ments within engineering colleges, and engineering colleges to other colleges at the institution. Weconclude with a discussion of the potential actionable changes dictated by these results.Keywords: progress analytics, student success, learning analytics, program curriculum

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)

have initiated a study delving into the mental constructs that engineeringfaculty hold about evaluation, focusing on their evolving attitudes and responses to GAI, asreported in the Fall of 2023. Adopting a long-term data-gathering strategy, we conducted a seriesof surveys, interviews, and recordings targeting the evaluative decision-making processes of avaried group of engineering educators across the United States. This paper presents the datacollection process, our participants’ demographics, our data analysis plan, and initial findingsbased on the participants’ backgrounds, followed by our future work and potential implications.The analysis of the collected data will utilize qualitative thematic analysis in the next step of ourstudy. Once

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)

planning and evaluating student successstrategies. The potential of curricular analytics lies in directly linking interventions to studentsuccess outcomes, acknowledging the importance of understanding the larger educational con-text to maximize the effectiveness of interventions. We view the university as a complex systemcomprising interacting subcomponents that collectively influence the success of improvementefforts 5,6 . Each university’s system properties vary, necessitating tailored models to predict im-provements from specific reforms. In this paper, we compile recent developments in curricularanalytics, organizing them to support practical applications and further theoretical advances inthis field

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)

many different people are classifying thesetraining examples, there is bound to be some level of bias when it comes to which categoriespeople feel a particular sentence belongs to. A stricter rubric for classification should bedeveloped for the future. Although the hyperparameters is good, there is always room forimprovement.Potential benefits and risksThere are many benefits this system could offer. The instructor could monitor the entire class viathe pie chart shown in Figure 4. The virtual assistant offers direct support to the instructor to lookinto the performance of specific project groups. On the other hand, the student could use thevirtual assistant to plan out project ideas, time management, meeting schedule and personalresearch

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)

[7], the SCCT can be used to select predictorsfrom the High School Longitudinal Study of 2009-2014 in a candidate variable subset [8], andthe EST can be applied to independent variables from the NCES Education Longitudinal Study1988-2000 dataset to study the gender differences and determined the role of the high schoolcontext in STEM majors’ plans [9]. However, when using the theory-driven approach with large-scale dataset, challenges emerge. Many studies tend to rely on one theory to identify predictors,potentially missing out on the rich insights these datasets offer. Yet, employing multiple theoriesfor predictor identification can lead to an overwhelming number of predictors. This is where thedata-driven approach becomes beneficial. We

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

transdisciplinarycollaboration, and allows for communication with non-experts. As a next step I plan toimplement data physicalization in a bio-inspired design course where comparative data betweenvarious species could be meaningful to visualize as physical objects as it relates to designabstraction and creating a connection to other species. Our classes are moving further and furtherfrom physical reality and have become digital and virtual. If we agree that making stuff is specialfor us, data physicalization could be an avenue to get back to making, to increasing sensoryavenues, to including other ways of knowing, to spatial perception, to embodied cognition. Apaper from 2013 discussed design for emotional attachment and technological adaptability as anapproach 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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

programming, intelligence design, data warehousing),programming (problem-solving, languages such as Python, Java), project management (planning,project analysis, risk reporting), data analytics (computer learning, programming, statisticalmodeling), and business impact (consulting, market delivery, strategic management). Results [7]from an analysis of 1050 unique records of Data Science job requirements showed that technicalskills are in high demand when seeking Data Scientists. These skills include proficiency in BigData Technologies, software development, data management, analytic methods, algorithms,programming languages, and analytic tools. In addition, the study findings [7] showed demandfor soft skills (non-technical and interpersonal skills

Conference Session

DSA Technical Session 6

Collection

2024 ASEE Annual Conference & Exposition

Authors

Smitesh Bakrania, Rowan University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

teaching. 3. I use informal interactions with students to inform my teaching. 4. I use attendance to assess engagement. 5. I use the student evaluations at the end of the term to plan my teaching. 6. I use traditional (direct) assessments to inform my teaching. 7. I rarely use any indirect feedback mechanisms.Once faculty completed the pre-LA part, key outcomes of LA generated from the iTFS andNanotechnology courses were shared. The goal with this step was to provide examples of whatcould be obtained from program-specific courses through LA.(b) post-LA surveyAfter this brief presentation, faculty completed the post-LA part of the survey. The first sectionasked about faculty’s thoughts on how useful LA would be to them and how

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)

within the curriculum. The former category isreferred to as the structural complexity, and the latter as the instructional complexity of the curricu-lum. For computing structural complexity, a useful representation of a curriculum is as a directedacyclic graph (dag), where the vertices of the graph represent the courses in the curriculum, andthe directed correspond to the prerequisites relationships among the courses in the curriculum. Anexample degree plan for an electrical engineering program, created using this dag representationis shown in FIgure 1. The structural complexity measure we will use is based upon the proper-ties of this dag, and it has been shown that it directly relates to a student ability to complete thecurriculum.4

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)

expose interesting credit accumulation patterns that can help us discover causes ofexcess credits [18]. A secondary aim of this study is to compare the excess credit accumulationtrends between Transfer students and Non-Transfer students, to explore the idea that transfercredit loss plays a role in the accrual of excess credits. This analysis may also help estimate howmany usable credits Transfer students enter with. Let us point out here that we are using theexcess (or usable) credits accumulated against the student’s degree program of graduation for thisanalysis. A more direct method (which we plan to implement in the future) would be to perform acredit hour decomposition on a semester-by-semester basis, measured against the program inwhich

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

computerscience, mechanical engineering and electrical engineering at Alabama A&M Universitydiligently designed and implemented the summer enrichment programs by integrating dataanalytics skills into nuclear energy and security projects. This pilot study has focused on: (1)designing the summer programs by adding data analytics components; (2) incorporating ProjBLthat promotes active student interaction, critical thinking, and problem-solving; and (3)conducting assessment and surveys to gather feedback from students. This section mainlydescribes the details of this pilot study.The team has followed the logic model in Figure 1 that has been established in year one in thisproject. First, the team plans and designs the major components surrounding our

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

that encouraged participants to express their thoughts andexperiences freely and openly.The structure of the interview was meticulously designed to evaluate four critical constructs thatare pivotal in understanding students' engagement with data science within the chemicalengineering curriculum [20]. These constructs are: 1. Interest: Participants' enthusiasm and curiosity about data science. 2. Career Aspirations: How participants see data science fitting into their future professional plans. 3. Perceived Value: The importance participants attribute to data science skills in the context of their education and future career in chemical engineering. 4. Self-Efficacy: Participants' confidence in their ability to learn and apply

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

advanced topics, such as support vector machines, and unsupervised learning. 4 + 1 graduatestudents with a strong statistical background have the option to waive these courses based on their priorknowledge. This flexibility allows students to tailor their study plans according to their specificrequirements and backgrounds, ensuring a customized and effective educational experience in the realm ofData Science.Moreover, instructors employ a multifaceted approach by providing diverse examples and projects toinspire students with varied backgrounds. Our faculty members, hailing from diverse disciplines such asComputer Science, Mathematics, Statistics, Engineering, and Communications, contribute to the richnessof perspectives. This diversity enables

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

for all new construction of single-family homes, townhomes, and low-rise multi-family homes (CA Solar Mandate. 2020) o Other aspects that help CA include the state having plenty of sunlight (estimated at 284 days in a year) that helps solar generation. The State also has large desert land where currently a solar farm is in operation and others are planned to produce 550 megawatts (Nextera Energy, 2011). CA also has encouraged community solar farms which benefits homeowners to use solar energy without solar panels on their roofs (Livermore Community Solar, 2020). CA’s solar generation in 2022 accounts for 26.8% of the

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)

door to miscommunication and an increase in potentially fatal risks.In a less fraught outcome, many participants (11/24) after the “error” prompt began to distrustthe data. Their responses ranged from requesting a repeat of the experiment, consulting externalresources, or formulating a plan to re-analyze the data themselves. This highlights a practicalissue communicating between statistical and engineering audiences: Statisticians accept andexpect that variation will enter into data analysis, and normatively refer to certain variations as“error.” However, the term “error” may erode an engineers’ trust in a dataset.ImplicationsThese different interpretations of “error” encourage drastically different approaches toengineering design decisions

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

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)