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)

. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology, with a focus on engineering education. Her research interests span the fields of computing and engineering education, human-computer interaction, data science, and machine learning. ©American Society for Engineering Education, 2024 Data Science Perceptions: A Textual Analysis of Reddit Posts from Non-Computing EngineersAbstractNational reports in the United States regularly emphasize the need for qualified engineers toenter the workforce to solve present and future challenges for society. Such advancements oftenencourage an understanding and application of data science, a field that combines

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

Paper ID #41462An Interactive Platform for Team-based Learning Using Machine LearningApproachTony Maricic, New York University Tandon School of EngineeringNisha Ramanna, New York University Tandon School of Engineering Nisha Ramanna is a student at New York University, pursuing her Bachelor’s and Master’s in Computer Science with a concentration in Machine Learning and Artificial Intelligence. She is passionate about all areas of Machine Learning, including Natural Language Processing.Alison Reed, New York University Tandon School of EngineeringDr. Rui Li, New York University Dr. Li earned his master’s degree in Chemical

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)

. ©American Society for Engineering Education, 2024 Continuous Speech Emotion Recognition from Audio Segments with Supervised Learning and Reinforcement Learning Approaches1. IntroductionEmotion plays an important role in communications, conveying essential information beyondwords. This is particularly evident in enhancing Human-Computer Interaction (HCI) and SpeechEmotion Recognition (SER). The latter is a specialized area within Automatic SpeechRecognition (ASR) and focuses on identifying human emotions, which is crucial to advancingHCI. Recognizing emotions in speech, such as anger or joy, allows AI systems to interpret andrespond more effectively to human expressions.Emotion recognition technology can be integrated into engineering

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)

Online Learning in the United States,” Curriculum and Teaching Dialogue, vol. 17, no. 1 & 2, pp. 21–34, 2015. 4. “Statement on Online and Distance Education,” American Association of University Professors, 07-Apr-2016. [Online]. Available: https://www.aaup.org/report/statement-online-and-distance-education. [Accessed: 03-Feb-2024]. 5. Kizilcec, René F., and Dan Davis. "Learning Analytics Education: A case study, review of current programs, and recommendations for instructors." Practicable Learning Analytics. Cham: Springer International Publishing, 2023. 133-154. 6. S. Ranjeeth, T. . Latchoumi, and P. V. Paul, “A Survey on Predictive Models of Learning Analytics,” Procedia computer science, vol. 167

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

Kappan, 91:53–58.Guskey, T. (2007). Closing achievement gaps: Revisiting benjamin s. bloom’s “learning for mastery”. Journal of Advanced Academics, 19(1):8–31.R Core Team (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.Winget, M. and Persky, A. (2022). A practical review of mastery learning. American Journal of Pharmaceutical Education, 86:8906.

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)

connections between them areessential to understand, and we applied this thinking to our coding scheme so machine learningmodels could be trained effectively.When using generative AI within discipline-based education research, we take a human-computer partnership approach. Both humans and computers can provide unique skills and inputinto the qualitative coding and analysis process, as seen by others who have implementedmachine learning in various qualitative coding processes [59], [60], [61]. When human codersinteract with computers as coding partners rather than as tools designed to automate the processcompletely, both can work towards bettering a machine learning model that enriches theanalytical process by improving scalability and abstraction [61

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 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

long, students are offeredthe opportunity to work on research projects with the mentors at national labs. Students learnedto use the lab equipment to collect data, and then used the computer tools to analyze andvisualize the data. Figure 3 includes an example of the student project that tackles the technicalchallenges in room-temperature radiation detectors. Figure 3. An example of student project: Problem Description(left); Examples of the data visualization results (middle and right)In summary, the typical ProjBL activities follow a three-phase pedagogical approach: 1)Conceptual Phase (Learning): the students are first introduced to data analytics fundamentals,including concepts and theory. Then the students are exposed to the computing

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

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

Paper ID #43073An Online Interdisciplinary Professional Master’s Program in TranslationalData AnalyticsDr. Emily Nutwell, The Ohio State University Dr. Emily Nutwell is currently serving as the Program Director of the Masters in Translational Data Analytics at the Ohio State University. This applied program, designed for working professionals, focuses on the foundation of data analysis, computing, machine learning, data visualization, and information design. Prior to joining Ohio State, Dr. Nutwell worked at Honda R&D Americas for close to twenty years as a vehicle crash analysts specializing in computational techniques

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

their curriculum,through one or multiple courses (see Figure 1). Figure 1. Sample PopulationKeywords, Database, and CriteriaThe literature reviews were conducted using specific keywords tailored to each investigationarea. The first literature review searched the keywords “assessment||self-efficacy” + “datascience.” The second literature review used the keywords “knowledge ||skills” + “literaturereview” + “data science ||data science education ||teaching ||learning ||teaching and learning.”The third literature review utilized the keywords “data science||statistic|| mathematics||computer Science ||business” + “life cycle.” Searches were conducted in Google, GoogleScholar, and ScienceDirect. Various source types

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)

curricular analytics that is now being used broadly across higher education in order to inform improvement efforts related to curricular efficiency, curricular equity, and student progression.Dr. Yiming Zhang, The University of Arizona Yiming Zhang completed his doctoral degree in Electrical and Computer Engineering from the University of Arizona in 2023. His research focuses on machine learning, data analytics, and optimization in the application of higher education. ©American Society for Engineering Education, 2024 Minimizing Curricular Complexity through Backwards Design Gregory L. Heileman and Yiming Zhang {heileman, yimingzhang1

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

a long history, with roots dating backto the 1950s [4]. Early NLP systems were limited in their capabilities and largely relied onrule-based approaches, but the development of machine learning algorithms in the 1980s and1990s led to significant advances in the field [5]. Nowadays, NLP is a rapidly growing field thathas the potential to revolutionize the way we teach and learn [6]. By enabling computers tounderstand and process human language, NLP can help educators identify patterns and trends instudent learning, facilitate more personalized and effective instruction, and provide students withnew ways to interact with educational materials [6], [7]. NLP has a wide range of applications,including language translation, text summarization, and

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)

feedback: Supporting revision of scientific arguments involving uncertainty," Science Education, vol. 103, no. 3, pp. 590-622, 2019.[5] H. Zhang et al., "eRevise: Using natural language processing to provide formative feedback on text evidence usage in student writing," in Proceedings of the AAAI Conference on Artificial Intelligence, Jul. 2019, vol. 33, no. 01, pp. 9619-9625, doi: https://doi.org/10.1609/aaai.v33i01.33019619.[6] M. Zhu, O. L. Liu, and H.-S. Lee, "The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing," Computers & Education, vol. 143, p. 103668, 2020.[7] U. Maier and C. Klotz, "Personalized feedback in digital

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)

Paper ID #44064A Hybrid Approach to Natural Language Processing for Analyzing StudentFeedback about Faculty SupportNeha Kardam, University of Washington Neha Kardam is a fourth-year Ph.D. student in Electrical and Computer Engineering at the University of Washington, Seattle. She is an interdisciplinary researcher with experience in statistics, predictive analytics, mixed methods research, and machine learning techniques in data-driven research.Dr. Denise Wilson, University of Washington Denise Wilson is a professor and associate chair of diversity, equity, and inclusion in electrical and computer engineering at the

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

engineering design, collaboration in engineering, decision making in engineering teams, and elementary engineering education.Dr. Adetoun Yeaman, Northeastern University Adetoun Yeaman is an Assistant Teaching Professor in the First Year Engineering Program at Northeastern University. Her research interests include empathy, design education, ethics education and community engagement in engineering. She currently teaches Cornerstone of Engineering, a first-year two-semester course series that integrates computer programming, computer aided design, ethics and the engineering design process within a project based learning environment. She was previously an engineering education postdoctoral fellow at Wake Forest University

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)

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 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)

Paper ID #42637Using Cohort-Based Analytics to Better Understand Student ProgressKristina A Manasil, The University of Arizona Kristi Manasil is a first-year doctoral student within the School of Information at the University of Arizona. Having obtained her bachelor’s degree in Computer Science from the University of Arizona, she subsequently garnered valuable industry experience as a Data Quality Specialist and Developer, contributing to the implementation of the student CatCloud platform for the institution. Her scholarly pursuits are centered around the interdisciplinary domains of data visualization, machine learning

Conference Session

DSA Technical Session 4

Collection

2024 ASEE Annual Conference & Exposition

Authors

Sami Khorbotly, Valparaiso University; Daniel White, Valparaiso University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

, 2011.[5] R. Alvarez-Valdes, E. Crespo, and J. Tamarit, “Design and implementation of a course scheduling system using Tabu Search,” European Journal of Operational Research, vol. 137, 2002.[6] M. Badri, D. Davis, D. Davis, and J. Hollingsworth, “A multi-objective course scheduling model: Combining faculty preferences for courses and times,” Computers & Operations Research. Vol. 25, 1998, pp. 303-316.[7] M. Wyne, A. Farahani, E. Atashpaz-Gargari, and L. Zhang, “Optimal Faculty Staffing Using Depth-First Search,” ASEE Annual Conference and Exposition. Baltimore, MD. 2023.[8] L. Kyriakides, C. Christoforou, and C. Charalambous, “What matters for student learning outcomes: A meta-analysis

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)

Paper ID #44069A Comparative Analysis of Natural Language Processing Techniques for AnalyzingStudent Feedback about TA SupportNeha Kardam, University of Washington Neha Kardam is a PhD candidate in Electrical and Computer Engineering at the University of Washington, Seattle. She is an interdisciplinary researcher with experience in statistics, predictive analytics, mixed methods research, and machine learning techniques in data-driven research.Dr. Denise Wilson, University of Washington Denise Wilson is a professor and associate chair of diversity, equity, and inclusion in electrical and computer engineering at the

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

the importantvariables to predict the students’ performance [12]. Random forest algorithm to analyze the HighSchool Longitudinal Study of 2009 data to identify the important variables which impact theengineering major choice [13]. The Boruta algorithm is a high-performance FS that employs a novel feature selectionalgorithm based on the random forest (RF) classification learning method. It is available as an Rpackage [14]. RF combines the predictions of multiple individual models to predict outcomes. Itis better than the outcomes computed by a single learning model. Typically, RF constructsmultiple decision tree models, and each tree runs a random subset of the features in the trainingdataset independently. In the final step, the RF

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)

Education,” in Proceedings of the Tenth ACM Conference on Learning @ Scale, in L@S ’23. New York, NY, USA: Association for Computing Machinery, Jul. 2023, pp. 378–382. doi: 10.1145/3573051.3596191.[5] T. Wang, “Navigating Generative AI (ChatGPT) in Higher Education: Opportunities and Challenges,” in Smart Learning for A Sustainable Society, C. Anutariya, D. Liu, Kinshuk, A. Tlili, J. Yang, and M. Chang, Eds., in Lecture Notes in Educational Technology. , Singapore: Springer Nature Singapore, 2023, pp. 215–225. doi: 10.1007/978-981-99-5961-7_28.[6] T. Farrelly and N. Baker, “Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice,” Educ. Sci., vol. 13, no. 11, p. 1109, Nov. 2023, doi

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

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 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)

Paper ID #42426Investigating and predicting the Cognitive Fatigue Threshold as a Factor ofPerformance Reduction in AssessmentMr. Amirreza Mehrabi, Purdue Engineering Education I am Amirreza Mehrabi, a Ph.D. student in Engineering Education at Purdue University, West Lafayette. Now I am working in computer adaptive testing (CAT) enhancement with AI and analyzing big data with machine learning (ML) under Prof. J. W. Morphew at the ENE department. My master’s was in engineering education at UNESCO chair on Engineering Education at the University of Tehran. I pursue Human adaptation to technology and modeling human behavior

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

Tagged Topics

Data Science & Analytics Constituent Committee (DSA)

. degrees in Data Science. B.S.degrees in Data Science, 2+2 programs in data science (A.S. Data Science in a 2-year collegeand transfer to a 4-year university for the +2 B.S. Data Science with no loss of credits), proposedcertificates in data science, a common curriculum state-wide, a high-school data science trackbased on the common curriculum, and a vision realized of “start anywhere, finish anywhere.”Finally, we look to the future in expanding the “opt-in” academic institutions and significantlyincreasing the number of data science graduates at all levels.IntroductionThe development of a statewide ecosystem in data science builds on earlier statewide initiativesto provide access to education in computer science to all Arkansans. Former Arkansas

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)

the background and the green channel of variousgraphene layers. The method is positioned as an alternative to both deep learning-based graphenerecognition and traditional microscopic analysis.Notably, the proposed methodology performs well under conditions where the influence ofsurrounding light on the graphene-on-oxide sample is minimal. It enables the rapid identificationof various graphene layers, showcasing its feasibility for non-destructive identification. Thestudy also addresses the functionality of the methodology under nonhomogeneous lightingconditions, demonstrating successful predictions of graphene layers even in lower-quality imagescompared to those typically published in literature.In summary, the proposed methodology offers a

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)

professor in the ECE department at the University of New Mexico, a staff scientist at Los Alamos National Laboratories, and is currently a Research Professor in the ECE department at the University of New Mexico. He has a technical background in Machine Learning, Signal Processing, Theoretical Computer Science, Pattern Recognition, and Computer Vision. He is the coauthor of a 2009 text entitled ”Digital Signal Analysis with Matlab” and is the author of over 100 peer-reviewed scientific publications. ©American Society for Engineering Education, 2024 Credit Hour Analysis of Undergraduate Students using Sequence DataAbstractRepresenting credit

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

Dragicevic (under CC-BY-SA license) [1].1. Introduction and historyWhat is data physicalization?A data physicalization (or simply physicalization) according to the paper Opportunities andChallenges for Data Physicalization “is a physical artifact whose geometry or material propertiesencode data” and “a research area that examines how computer-supported, physicalrepresentations of data (i.e., physicalizations), can support cognition, communication, learning,problem solving, and decision making [2].”We can also think of these as analog data storage devices. A historical example is the Quipus – acomplex collection of knotted ropes that played an important role in Inca administration. Thevariation in the knots is thoughts to encode quantitative

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

and faculty.The insights presented in this study offer valuable guidance for educators and industryprofessionals seeking to seamlessly embed data science into the chemical engineering curriculumand better prepare students for a data-centric industry.This paper provides a comprehensive overview of interview development, data distribution, andkey findings. It underscores the urgency of further research to enhance the integration of datascience in the CHE curriculum and the essential role of preparing students for an industry thatincreasingly relies on data analytics and computational techniques.IntroductionThe integration of data science in chemical engineering is a rapidly evolving field, with a focuson data management, statistical and machine

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)

Paper ID #43321Optimizing Transfer Pathways in Higher EducationDr. Yiming Zhang, The University of Arizona Yiming Zhang completed his doctoral degree in Electrical and Computer Engineering from the University of Arizona in 2023. His research focuses on machine learning, data analytics, and optimization in the application of higher education.Prof. Gregory L. Heileman, The University of Arizona Gregory (Greg) L. Heileman currently serves as the Vice Provost for Undergraduate Education and Professor of Electrical and Computer Engineering at the University of Arizona, where he is responsible for facilitating