Conference Session

DSA Technical Session 7

Collection

2024 ASEE Annual Conference & Exposition

Authors

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

developed for accurate counting of grapheneflakes on transparent bulk substrates using optical reflection microscopy measurements [3]. Theuniversal optical conductance model matches reflection data for graphene flakes up to ninelayers thick. However, achieving maximum sensitivity at the desired wavelength necessitatesprecise control over oxide thickness and oxide index of refraction. Another proposed methodutilizes transmission or reflection optical microscopy to determine the number of graphenemonolayers on various substrates [4]. Image modification through software analysis yields a 3Dmodel of few layers of graphene on any substrate. However, this method relies on classical, time-consuming techniques like AFM and Raman spectroscopy for

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

Table 1 (the full codebook can be found in Appendix A). We also generated acount of each code based on the full data set, shown in Figure 2.Table 1: Codes for survey responses with short definitions. The definitions represent the stancetaken by the student in their response. The full codebook including further clarification on thedefinition and representative examples for each code can be found in Appendix A. Code Short Definition (tool) AI is a useful tool for students. (crutch) AI has the potential to replace learning. (tutor) AI can be used to learn a specific concept. (reflect) AI can help or hinder learning depending on who uses it and how. (speed) AI can

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

valuable guidance forfuture educational strategies and policies.keywords: curricular complexity, causal inference, student success, graduation rates, educationaldata mining1 IntroductionCurriculum complexity, an intrinsic characteristic of educational programs, has increasingly be-come a focal point of academic research due to its presumed impact on student performance. Thearchitecture of a curriculum – encompassing the breadth and depth of content, the sequencingof subjects, and the interplay of various pedagogical approaches – directly influences the learningenvironment. This influence is often reflected in key educational outcomes such as student engage-ment, comprehension, retention, and graduation rates. The complexity of a curriculum

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

answers." This statement reflects the idea that data science involves more than just numerical analysis; it requires an integration of subject matter expertise to ensure meaningful interpretations. • Another perspective offered was, "Data is in sensors and economics in chemical engineering; data science is interpreting these values and creating a story." This view emphasizes the narrative aspect of data science, where data from diverse sources is synthesized into coherent stories that inform decision-making processes.Unsure What Data/Data Science IsA segment of the participants expressed uncertainty about the precise definitions of data and datascience, reflecting a perception of these concepts as

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

curriculum incorporates design and design thinking concepts to emphasizecreative problem-solving skills and the importance of data storytelling.There is a need for educators to understand how to develop a curriculum for workingprofessionals which introduces novice programmers to 1) core data and computational concepts;2) tools and techniques; 3) data-driven problem-solving workflows; and 4) data storytelling. Thispaper presents these four “swim lanes” to define a framework for describing a cohesiveinterdisciplinary curricular experience for an applied master’s program.Through reflection, the authors conclude that learners initially struggle with new concepts, butwith sufficient support, they successfully learn and apply data science and computer

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)

competence and foster a positive learning experience [6]. High-confidencestudents typically demonstrate a strong belief in their abilities and may seek out challenges orleadership roles. However, excessive confidence without corresponding competence can leadto overestimation of skills and performance [7]. The Zone of Proximal Developmentsuggested that learning occurs most effectively within the “zone” where tasks are challengingyet achievable with appropriate support. Educators can support high-confidence students byproviding opportunities for intellectual challenge and promoting metacognitive skills, such asself-reflection and self-regulation. Encouraging collaboration and peer feedback can also helphigh-confidence students develop a more accurate

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

can be effectively andresponsibly integrated into different types of engineering courses.Regarding specific courses, we found that people teaching first-year courses reported GAI wasinfluencing thinking about assessment. This relatively high recognition might reflect thefoundational nature of these courses, where incorporating innovative technologies could play asignificant role in shaping early educational experiences. In contrast, Capstone Courses, oftenbeing the culmination of academic programs, showed a notable number of acknowledgments.This suggests that even in advanced stages of education, where comprehensive projects andpractical applications are prevalent, the potential of GAI to influence and enhance educationalpractices is widely

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 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

Millennium Scholars. Before joining FGCU, she was a visiting Assistant Professor of Biotechnology in the Division of Science and Technology at the United International College (UIC) in Zhuhai China. She has trained with ASCE’s Excellence in Civil Engineering Education (ExCEEd) initiative, been exploring and applying evidence-based strategies for instruction, and is a proponent of Learning Assistants (LAs). Her scholarship of teaching and learning interests are in motivation and mindset, teamwork and collaboration, and learning through failure and reflection. Her bioengineering research interests and collaborations are in the areas of biomaterials, cellular microenvironments, and tissue engineering and regenerative

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

entity recognition [33]. While early automated feedback systemsrelied on domain-expert rules and were limited in addressing the diversity of open-endedassignments [34-36], data-driven approaches, though promising in highly semantically diverseresponses, often face challenges due to the lack of extensive training datasets [4, 37, 38].AFS based on LLMs holds the potential for a more effective and efficient solution. Applicationsrange from personalized hints for programming assignments [39] to reflective writing [40],including feedback on the appropriateness of the topic of a data science project proposal and thedescription clarity of goals, benefits, novelty and overall clarity of the report [41]. Despite thepromising results from studies like Dai

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)

speeches. It involves analyzing a speaker's tone, pitch, tempo, andvolume to determine their emotional state. This process is complex as it requires not only wordrecognition but also an understanding of the delivery that reflects various emotional states [1].In utterance-level SER, emotions are classified for an entire spoken utterance, typically acomplete thought or statement. Here the emotions are considered as attributes of the wholeutterance, disregarding the temporal variations within it. The goal is to identify the dominantemotion conveyed in the utterance.Frame-level SER delves into a more detailed analysis by breaking the speech into smallersegments, often milliseconds long [2]. This approach allows the detection of emotional changeswithin

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

,“Assignments explanations” emerged as a recurrent theme in the human-generated codes, thoughin a slightly varied form in the generated codes. The GAI method consistently reflected athematic focus on “Feedback” with several sub-topics identified under this umbrella, includingfrequent and timely feedback. Upon reviewing the labels generated by the generative model,frequent labels were assigned to a main topic. Subsequently, the process of developing thesemain topics entailed utilizing GPT-3.5, with humans reviewing the main labels to ensure theiraccuracy and alignment with the original labels. The main topics from questions 1 and 2 arepresented in Table 2.Table 2. Main topics for Q1 and Q2Q1 Main Topics (n=8) Q2 Main topics (n=9)Online

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

than 0.4 were considered poor agreement. These ranges are consistent with current conventions for assessing interrater reliability [31]. Cohen’s Kappa was calculated for each theme in the data, using 2X2 contingency tables that evaluated how well a particular theme identified by the domain expert agreed with the theme assigned by top NLP modelling techniques classification models.ResultsIn our study sample, initial topic modeling revealed the emergence of four topics (also referred toas codes). Table 2 displays the most frequently appearing words linked with each of these fourtopics. Topic 1 reflected students’ desire for greater practice with solving problems associatedwith engineering content including but not

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

Calculus 1A Calculus 2A Calculus 1B Calculus 2B English Sequence English Sequence Computer Science 1 Computer Science 2financial savings, this option facilitates swifter access to advanced degrees, reflecting the program’scommitment to flexibility, excellence, and academic prowess.A. First-Year CurriculumThe first-year curriculum for our Data Science program was designed to closely match both the first yearsof our current Computer Science and Applied Mathematics programs. At Wentworth, students choosetheir major before matriculation. This synchronization of the programs allows students the flexibility toswitch between programs seamlessly

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

-generated codes. Recall is the percentage of human codes that themodel was able to generate correctly. The F1 score is the harmonic mean of the precision andrecall [1]. We also performed qualitative analysis for model-generated codes for ten testinstances for both thermodynamics datasets. We report the number of codes that are semanticallyrelevant to the student’s narrative but not an exact match under “misses but makes sense”),semantically irrelevant codes with “does not make sense,” and the number of codes missed bythe model with “code missed.”Researcher PositionalityOur strength as researchers improves as we acknowledge and reflect upon the backgrounds andexperiences of ourselves and others in our team [69]. As this project is a collaboration

Conference Session

DSA Technical Session 8

Collection

2024 ASEE Annual Conference & Exposition

Authors

Amirreza Mehrabi, Purdue Engineering Education; Jason Morphew, Purdue University, West Lafayette

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

,possibly reflecting the multifaceted nature of educational environments, research designs, andmethods of inducing CF. This study aims to build on the foundation laid by previous research,offering new insights into the nuances of CF's effects in educational settings and its implicationsfor both theory and practice. The variability of results from studies of CF can be partially attributed to differences inresearch paradigms and how CF is induced. In the first paradigm, CF is induced through therepetition of different tasks. The impact of CF is then measured by comparing performance on thefirst task to later tasks. For example, Ackerman and Kanfer [12] examined the effects of CF bycomparing final scores on the SAT for groups who took the exam

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)

the problems are solvedby the instructor to assist their own attempt at similar homework problems. It is also possible thatstudents review the example videos that are directly connected to the upcoming exams. In Figure2(b) for Nano, there is a gradual decrease in the number of views as the videos cover thefundamentals of material science up to video number 15. The views climb immediately whenmore applied content is discussed, ranging from materials characterization to synthesis. ForNano, there are no example videos where students have to refer back to how the instructorapproached a problem. Therefore, the patterns observed here reflect the type of course content.Figure 3 presents similar data but now with views based on a unique viewer. These

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Engineering from the University of Science and Culture in Tehran, Iran. Her research interests include software engineering, cloud computing, data visualization, and Machine learning.Mr. Rohit Hemaraja, The University of Arizona Rohit Hemaraja is a Master’s student in Data Science at the School of Information at the University of Arizona. He is a Graduate Research Assistant with the Analysis of Higher Education Research Group. He earned his Bachelor of Engineering degree in Computer Science. His research focuses on machine learning, large language models and data management. His academic and professional interests lie at the intersection of these disciplines, reflecting his commitment to advancing the capabilities and

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)

other available courses listed under course sets that interest students provides theopportunity to further customize the degree plan.It is worth noting that changing a major can be a normal part of the college experience, as itmay reflect a student’s growth, self-discovery, and a deeper understanding of their academic andprofessional desires. To demonstrate the efficacy of our algorithm that works in this scenario,another example is considered for creating a transfer plan from the Associate of Arts program atPima Community College to the Biochemistry program at the University of Arizona. The structureof the degree requirement tree is provided in Figure 5, and the descriptions of the requirements arelisted in Table 4. The two-year to four-year

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