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)

improvestudent achievement, engagement, and have helped students develop conceptual understandingand problem-solving skills [4] - [14]. Additionally, when students are asked to write short-answer responses to explain their reasoning to concept questions, it has been observed toimprove student performance, engagement, and prepare students for group discussion [15], [16].These responses provide instructors and researchers with a wealth of information regardingstudent thinking [17]. Still, often, it is difficult for instructors and researchers to process all ofthis written information. Machine learning researchers have applied natural language processing(NLP) and large language models (LLMs) to automate the grading and scoring of textualresponses from

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

to solve small, specific problems. For example, one student wrote “I would ask it howto write specific syntax (make arrays that are all zeros, for loops syntax, math modifiers).”While the tool code captured the way students used ChatGPT for specific tasks during the codingprocess, 25% of student responses to the survey also described ChatGPT as a learning aidbeyond syntax or debugging code (code: tutor). These responses also often included elements ofpersonalized help or access to help outside of the available hours for other support tools(professor office hours, peer tutors, etc.). “...Having a tool to be able to help me when othersaren't available to help was amazing.” As detailed above, these responses included descriptionsof how students

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

with little to noprior data science, computing, or math background. Courses use both synchronous andasynchronous delivery methods to maximize learner flexibility while providing opportunities toengage in real time with instructors and peers. All courses emphasize projects to provideopportunities for learners to apply courses concepts to real-world problems. A terminal 2-semester capstone course incorporates all three disciplines into a final culminating team project.This paper will focus on the conceptualization of the computer science (CS) portion of thecurriculum. As an applied master’s program, much of the CS curriculum takes inspiration fromindustry frameworks such as CRISP-DM and Agile project management to contextualizeconcepts. The

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

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

, with stratification by gender to ensure diversity. In total, 14 studentsparticipated in the interviews, comprising 5 seniors, 5 juniors, and 4 sophomores. The absence offirst-year students notwithstanding, the selected participants offered a broad perspective acrossthe different stages of the chemical engineering undergraduate program.Data CollectionData collection for this study was conducted through comprehensive interviews with allparticipating students. These hour-long interviews were carried out by fellow undergraduatestudents who had not only completed human subject research training but were also directlyinvolved in this research project. The choice of peer interviewers was strategic, aiming to createa relaxed and relatable atmosphere

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 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)

2020 degree share 24%), race (sample white 33% vs 2020 degreeshare 56%), and nationality (including participants residing in Canada, Turkey, and thePhilippines). Aligned with the goals of the larger study, participants were drawn from Aerospace,Civil, and Mechanical engineering disciplines. Demographics are summarized in Table 1.Our sample size of n=24 is in line with recommendations for qualitative research [22], and iscomparable with other peer-reviewed qualitative research projects [23], [24], [25].Table 1. Summary of participant demographics. Experience 2 years: 3 3 years: 2 4 years: 8 5+ years: 11 Race Asian: 10 Black: 2 White: 8 Other: 4 Subfield Aerospace

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 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)

enhance his or her performance or productivity,while perceived ease of use refers to the extent to which an individual believes that using aparticular technology will be easy and effortless [13]. It has been demonstrated that both (U) and(E) are important predictors of people's intentions to use technology, which eventually results inreal usage behavior [14], [15]. Figure 1. Adapted Technology Acceptance Model (TAM) [15]TAM also incorporates external variables that may influence individuals' attitudes and behaviortoward technology, such as social influence and facilitating conditions [13]. Social influencerefers to the extent to which an individual's behavior is influenced by the opinions of others, suchas peers and colleagues