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)

Engineering in 2009 from the Imperial College of London and his doctoral degree in 2020 from the University of Georgia, College of Engineering.Jack Yang, New York University Tandon School of Engineering ©American Society for Engineering Education, 2024 An Interactive Platform for Team-based Learning Using Machine Learning ApproachAbstractThis complete evidence-based paper explores the feasibility of developing an interactiveplatform with chatbot feature to facilitate project-based learning. Teamwork pedagogy is widelyused in engineering courses, particularly in first year (cornerstone) and senior-year (capstone)design courses, but also across the curriculum. Faculty have several

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)

higher education.Asma WasfiMohammad HayajnehBisni Fahad Mon, United Arab Emirates UniversityAmeer Slim, University of New Mexico ©American Society for Engineering Education, 2024 Enhancing Academic Pathways: A Data-Driven Approach to Reducing Curriculum Complexity and Improving Graduation Rates in Higher Education Ahmad Slim† , Gregory L. Heileman† , Husain Al Yusuf† , Ameer Slim‡ , Yiming Zhang† , Mohammad Hayajneh• , Bisni Fahad Mon• , Asma Wasfi Fayes• {ahslim@arizona.edu, heileman@arizona.edu, halyusuf@arizona.edu, ahs1993@unm.edu, yimingzhang1@arizona.edu, mhayajneh@uaeu.ac.ae

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

Paper ID #44170Causal Inference Networks: Unraveling the Complex Relationships BetweenCurriculum Complexity, Student Characteristics, and Performance in HigherEducationDr. 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

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

of toolsin their curriculum. One has to subscribe or buy Excel or Tableau, and Orange is free opensource. Tableau provides all the capabilities demonstrated in the other two tools, but it is also anexpensive tool2.- Here one can change visualization options dynamically without having toreload datasets (e.g., Tableau by default knows all the world region data to do Geo Maps).With the fast-changing technology, industries are demanding data analysis skill(s) from theiremployees. Teaching the use of three tools with a case study to engineering students asdemonstrated here, can help meet that demand. The approach taken in this case-study research isto bring home the two key areas to engineering students – the knowledge and importance

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

particularly important in this context [2]. Furthermore, the use ofcomputer-aided modeling and simulation (CAMS) can enhance the problem-solving capabilitiesof chemical engineering graduates [10]. Such studies are instrumental in guiding the formulationof an academic curriculum designed to furnish chemical engineering students with the essentialcompetencies and knowledge requisite for navigating a future dominated by data.Instructors endeavoring to introduce new pedagogical approaches encounter several well-documented challenges, including constraints on time, diversity in student academicbackgrounds, their own limited training in new domains, insufficient institutional support, thenecessity to adhere to extensive curricular content, and a scarcity of

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)

an M.Sc. in Mining Engineering (Geostatistics) from the University of Alberta (Canada).Prof. Brian M Frank P.Eng., Queen’s University Brian Frank is the DuPont Canada Chair in Engineering Education Research and Development, and the Director of Program Development in the Faculty of Engineering and Applied Science at Queen’s University where he works on engineering curriculum development,Julian Ortiz, Queen’s University Dr. Ortiz is a Mining Engineer from Universidad de Chile and Ph.D. from University of Alberta. Currently, he is Professor and Mark Cutifani / Anglo American Chair in Mining Innovation at University of Exeter - Camborne School of Mines, in the United Kingdom, where he conducts research related to

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)

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 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)

we complete our study, we believe our findings will sketch the early stages of thisemerging paradigm shift in the assessment of undergraduate engineering education, offering anovel perspective on the discourse surrounding evaluation strategies in the field. These insightsare vital for stakeholders such as policymakers, educational leaders, and instructors, as they havesignificant ramifications for policy development, curriculum planning, and the broader dialogueon integrating GAI into educational evaluation.1. IntroductionThe advent of generative artificial intelligence (GAI) has heralded a new era in higher education,prompting extensive research and discussions, particularly concerning its impact on traditionalassessment practices. Recent

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

elements withinthe system, connected by lines that represent a variety of relationships. Given its usefulness inunderstanding intricate systems, it should be helpful in mapping the engineering educationprocess. A huge number of factors affect the education of new engineers. From elementaryschool to graduate school, students are exposed to STEM curriculum, experiential learning,career development, and other external factors that contribute to them becoming an engineer.Having a systemogram that compiles this information could be used by students, teachers,professors, and administrators to refine the system for everyone’s benefit. The systemogram ofthe engineering education system is shown below in Figure 6.Figure 6: Systemogram of student flow

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

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Data Science & Analytics Constituent Committee (DSA), Diversity

scienceconcepts in both didactic and experiential settings. Students appreciate the need to successfullycommunicate with data and be effective data storytellers but will often feel frustrated that datastorytelling skills are not “real data science.” An analysis of LinkedIn profiles indicates that over60% of graduated learners secured new employment in data careers since starting the program.To build on this success, further curriculum development should more explicitly connectfundamental data science concepts and broader concepts such as creative problem-solving anddata storytelling.KeywordsGraduate education, data analytics, distance learning, life-long learning, adult learning1. IntroductionWe are living in an era where the Volume, Velocity, Veracity

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)

Paper ID #44344Developing an Instrument for Assessing Self-Efficacy Confidence in Data ScienceDr. Safia Malallah, Kansas State University Safia Malallah is a postdoc in the computer science department at Kansas State University working with Vision and Data science projects. She has ten years of experience as a computer analyst and graphic designer. Besides, she’s passionate about developing curriculums for teaching coding, data science, AI, and engineering to young children by modeling playground environments. She tries to expand her experience by facilitating and volunteering for many STEM workshops.Dr. Ejiro U Osiobe

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

opportunities asstudied by Marques et al. [7].Data Analytics in STEM EducationBrown et al. [8] integrated data analytics in engineering education to address technical require-ments from a multicomplex environment perspective concept using data analytics tools such asIBM Watson Analytics. The results obtained from a multi-complex environment have aided stu-dents and improved their decision approach to quantify data accuracy and project requirements.The integration of analytics tools fostered the engineering students the ability to forecast require-ments and create new methods critical to their engineering design.Data analytics was also added to a core course on product manufacturing in the industrial engi-neering curriculum [9]. The pedagogical method

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 1

Collection

2024 ASEE Annual Conference & Exposition

Authors

Gregory L. Heileman, The University of Arizona; Yiming Zhang, The University of Arizona

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Data Science & Analytics Constituent Committee (DSA)

}@arizona.edu Department of Electrical & Computer Engineering University of ArizonaAbstractIn this paper, we first describe the Optimal Learning Outcomes Assignment (OLOA) problem,which involves assigning learning outcomes to courses during the backwards curriculum designprocess in ways that minimize the complexity of the resulting curriculum. An approximation algo-rithm for the OLOA problem is then described that yields novel solutions to important engineeringcurricular design challenges. Reducing curricular complexity, while maintaining effective learn-ing outcomes attainment, increases the likelihood students will complete a curriculum and earn adegree. The rationale for the approach

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)

curricular efficiency, curricular equity, and student progression.Bhavya Sharma, The University of ArizonaAhmad 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 improve student recruitment, retention, and success metrics. Dr. Slim’s scholarly contributions include numerous articles on the application of data science

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

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Data Science & Analytics Constituent Committee (DSA), Diversity

, positive student feedback, and success in preparing studentsfor internships.The paper is organized as follows. Section II breaks down the curriculum development on a term-by-termbasis; Section III provides some insight into our program and what it took to establish it; and Section IVpresents how to establish an inclusive educational atmosphere, fostering diversity, equity, and inclusion(DEI) awareness among the students and inclusive curriculum design. II. CurriculumThe development of the BSDS curriculum at Wentworth was a collaborative effort led by aninterdisciplinary committee comprising faculty members from Computing, Mathematics, Sciences, andHumanities. This inclusive approach, drawing from

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)

science and coding curriculum for kindergarten through eighth grades(Arkansas General Assembly, 2021). This was followed by Act 414, which establishedcompletion of a computer science course as a graduation requirement for all Arkansas publichigh school students.The incentive for such drastic measures was undoubtedly tied to Walmart’s decision in 2012 tolocate a $100 million data center in Colorado Springs, CO (Laden 2012). This news, combinedwith the slow out-migration of operational investments by other key businesses, sparkedconcerns about the ability to retain companies that serve as pillars of the State’s economy.Arkansas has long benefitted from a handful of homegrown Fortune 500 companies likeDillard’s, Tyson Foods, and J.B. Hunt Transport

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