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

involved, in data-related problem-solving in their daily jobs. We encourage the learners to share stories about the kinds ofsituations they encounter in their jobs and to share their thoughts on how to improve theoutcomes they observe. Occasionally, learners will share their frustration with the “non-programming” steps in CRISP-DM, for example asking, “When do we get to start writing morePython code?” when working through the Business Understanding step. However, typically theother activities that are running in parallel with this thread provide some of the desiredprogramming practice.The Data Governance seminar is a one credit hour course that provides learners with a deeperunderstanding of the meaning and scope of fundamental data governance

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

cut down on the time spent on homework assignments while I chose towork through the assignments on my own.” This comparison came down to speed as a decidingfactor in favor of using AI. However, the same student continued, “I saw some of my classmatesabuse its capabilities early on and then they struggled later on to code in matlab without it,”acknowledging the potential to undercut learning as reasoning against using AI.Some students further ascribed a comparative advantage to those who choose to use AI. Onestudent described AI as a tool that enables students who know how to use it to be moresuccessful than their peers. Another student connected this to a potential classroom ban on AI,writing, “Banning something that there is little

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)

revolutionizedNLP tasks [28, 29]. Based on a “transformer” architecture, Bidirectional EncoderRepresentations from Transformers (BERT) and OpenAI’s Generative Pre-Trained Transformer(GPT) are prominent examples of these models [30, 31]. A fundamental mechanism behind thesuccess of LLM is self-attention, which allows the models to recognize relationships betweenwords regardless of their order in the textual sequence, thereby improving the ability of themodels to deal with long-term dependencies [32]. Pre-trained LLMs are originally trained inmassive text corpus prior to being fine-tuned on a specific task. This approach has been proveneffective for improving performance on various NLP tasks, such as sentence classification,question answering and named

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

CurriculumBy the second year, the DS program comprises classes from both the Applied Math and the ComputerScience programs. Namely, DS students take Probability and Statistics, Linear Algebra, and AdvancedStatistics from the Applied Math program as well as Data Structures and Algorithms from the ComputerScience program. Additionally, there is a required course named Data Science Fundamentals forsecond-year DS students; this course aims to provide the fundamental knowledge and skills commonlyrequired to solve data-driven problems.Similarly in the third year, DS students are taking a mixture of Mathematics courses (MultivariableCalculus) and Computer Science Courses (Databases), as well as starting with courses that are designedfor students in the Data

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

havetaken a specific statistics course, leaving many without a solid foundation in this area.ProgrammingProgramming appears to be a more familiar area for the students, with a range of experiencesacross different platforms and languages. All have some level of programming (VBA, Matlab,Python)." This suggests that while programming is part of their academic experience, itsrelevance to data science is not always made explicit. The level of confidence varies many arecomfortable with basic programming but weak on complex programming. Students reportcomfort with fundamental programming but acknowledge their limitations with more advancedprogramming tasks, indicating an area for potential growth and development within thecurriculum.Data VisualizationData

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

sentiment analysis Its value comes fromanalyzing large amounts of text data [2]. For example, its applications have been used to analyzesocial media posts to track public opinion and identify trends (e.g., O’Connor [8]). In the field ofeducation, it has been applied to the analysis of student essays to provide feedback, teamworkreview analysis, and students’ feedback loop [1], [3], [9]. Another application is in the generationof natural language text (e.g., machine translation systems use NLP to translate text from onelanguage to another) [10]. In addition, it has been used to generate feedback on student writing [11] and to createpersonalized study materials [12]. It also can facilitate more personalized and effectiveinstruction [13]. By

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 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)

central problem of his book Full House, This book treats the even more fundamental taxonomic issue of what we designate as a thing or an object in the first place. I will argue that we are still suffering from a legacy as old as Plato, a tendency to abstract a single ideal or average as the "essence" of a system, and to devalue or ignore variation among the individuals that constitute the full population.Based on the definitions cited above, reification of the mean seems to be associated with treatingvariability as erroneous. While we have seen that “error” in statistics does not only connoteerroneous variability, it is common to interpret “error” as deviations from a true value for thepurposes of statistical

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)

on anoverview of Natural Language Processing in Section 3.3.3.1 Overview of Data ScienceData science, as defined by Provost and Fawcett, is “a set of fundamental principles that supportand guide the principled extraction of information and knowledge from data” [1, p. 2]. Datascience involves the use of statistical and computational methods to gather, analyze, and interpretlarge volumes of structured and unstructured data to inform decision-making, identify patterns,and make predictions. Data science involves several stages, including data collection, datapreprocessing, data exploration, model building, model evaluation, and deployment. Varioustools and techniques may be involved in these stages. In industries such as finance, healthcare