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

learning, and data visualization [1]. Thisintegration is crucial for handling the increasing complexity and size of data sets in chemicalengineering research and practice [2]. Data science has particularly impacted molecular sciencein chemical engineering, with applications in molecular discovery and property optimization [3].The development of a cyberinfrastructure for data-driven design and exploration of chemicalspace further underscores the potential of data science in transforming chemical research [4].The alignment of data analytics and strategy is transforming the chemical industry, with dataplaying a crucial role in production, research, marketing, and customer service strategies [5]. Theuse of big data and analytics in chemical

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)

widespread practice of publishing these curricula on public platforms. This trans-parency allows academic programs to benchmark their curricula against those offered by compa-rable institutions. For example, as depicted in Figure 1, we examine the undergraduate electricalengineering curricula of two major public U.S. institutions, both accredited by ABET 22 . Thesecurricula are structured into four-year (eight-term) plans, guiding students through their degreecompletion. We represent these curricula as graphical models, with vertices symbolizing coursesand directed edges indicating prerequisite requirements. Specifically, a directed edge from onecourse (vertex) to another mandates that the former, as a prerequisite, must be completed beforethe

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)

know, un-derstand and be able to demonstrate at the end of some learning experience. For instance, ABETstipulates a minimal set of student learning outcomes that describe what learners should knowand be able to by the time they graduate from an ABET-accredited engineering program.1 It isalso now common practice to articulate course-level learning outcomes for each of the coursesoffered by a college or university; these indicate what a learner is expected to know and be ableto do after successfully completing a course. A common approach used by curriculum design-ers, known as backwards design, involves designing a curriculum from the bottom up by startingfrom the program learning outcomes, and then creating course-level objectives that would

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

thisstudy is crucial in understanding how these advanced techniques are applied to real-world data.The dataset employed in this study comprises a rich and diverse collection of student data from 30different universities. This data set includes several covariates or variables integral to understand-ing the educational landscape and student outcomes.3.1 Data DescriptionThe dataset features a range of variables designed to capture the multifaceted nature of studentexperiences and outcomes across various universities. These variables include: 1. Program Complexity: This is a discrete variable reflecting the complexity of each program that students attend at a given university. The complexity metric could encompass factors like the

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)

broaderrange of student responses).IntroductionThe landscape of educational data collection is rapidly evolving, with significant increases instudent enrollments and class sizes leading to an unprecedented growth in textual data fromacademic sources, such as assignments, assessments, and student feedback instruments [1] - [3].This proliferation of textual data presents a critical challenge: manual analysis methods areincreasingly untenable due to their time-intensive nature, highlighting the necessity forautomation in the assessment process, whether in whole or in part [4]. In response to this need, asignificant body of recent research has focused on the use of Natural Language to assess studentwork in the form of short answers, essays, or other

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)

Literature ReviewCourse scheduling (also known as timetabling) is a multi-parameter combinatorial optimizationproblem with multiple constraints. The created schedule must take into consideration manyparameters and constraints including teacher expertise and preferences, student need for classes,best (non-conflicting) times for both teachers and students, and (for in-person courses) theavailability of a suitable classroom with adequate teaching equipment. The overall problem is anNP-hard problem [1] that has been extensively studied over the years. A wide variety of solutionshave been suggested. For example, [2] used genetic algorithms to solve the problem while [3] usedsimulated annealing. Similarly, [4] used the Particle Swarm Optimization (PSO

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)

user experience survey. The survey results gave some constructivefeedback for the developers. Overall, the project can deliver a feasible solution for courseinstructors to handle many student project teams. In the future, a generative AI feature -CHATME will also be available on the front end to help the user check the status of each studentgroup, which is built using NLTK and TensorFlow. Moreover, if a team issue arises, theplatform will alert the users, and provide constructive suggestions on how to improve the groupperformance.IntroductionIn engineering education, fostering collaborative skills [1] among students is crucial, and team-based learning has become the primary approach. It is an approach particularly prevalent infoundational

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

. 2IntroductionThe demand for energy consumption in the world is growing at an annual rate of close to 2% peryear ( Welker, A., et al., 2022) ) and that translates to about 3,598 Twh in 2022. In the UnitedStates (US), the energy consumption growth rate is 2.6% which translates to about 106 Gwh in2022 (EIA, US Electricity Overview, 2023). The US energy generation sources in 2022 (USPrimary Energy Source, 2022) are shown in Figure 1. Figure 1 US Energy Source Distribution (Source EIA)Fossil fuels (Petroleum, Natural Gas, and coal) make up 78.8% of the total energy source in theUS. Petroleum is the largest source and solar is the lowest. Renewable sources add up to 13.1%(Solar at 1.9%). It has been a fact that these non-renewable sources

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), Diversity

our analysis, we present acomparison of engineering school results to that of campus-wide results to uncover similarities(or dissimilarities) in extra credit accumulation patterns. The results reveal that althoughengineering and campus-wide students accumulate a similar number of extra credits, theircomposition is different. We would like to note that the methods used in this analysis, althoughapplied to the data from a specific university, are generally useful for credit-hour analysis.1 IntroductionCredit hours are a metric of time spent by a student in the classroom [4]: one credit hour equalsone hour in class every week for one semester [21, 11]. As per the requirements of all the regionalaccrediting agencies in the US, a bachelor’s

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 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)

but also provides empirical evidence of its effectiveness, offering avaluable contribution to the field of educational research and the development of ASAC systems.IntroductionIn the dynamic landscape of education research, the evaluation of student experiences andperspectives is integral for fostering effective learning environments. Short answer questions insurveys and assessments provide valuable insights, capturing student perspectives on support,learning outcomes, and satisfaction. Traditional qualitative methods, while valuable for theirdepth and nuance, often struggle to efficiently handle the vast amount of textual data generatedby student surveys and assessments [1]. This data, typically collected through short answerquestions

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)

to erode trust in the data (11/24 participants) and can lead to a more dangerousinterpretation of variability (2/24 participants). These results have important implications forcommunication on interdisciplinary teams and teaching statistics to engineering students.IntroductionVariability is ubiquitous in engineering but its impact is often ignored, sometimes to dangerouseffect. For example, in the 1940s the U.S. Air Force had serious issues with uncontrollableaircraft: At the height of this calamity 17 pilots crashed in a single day [1]. The standard at thetime was to design aircraft for “the average man,” with non-adjustable controls assuming fixedhuman dimensions. Gilbert Daniels [2] studied the measurements of 4063 pilots, and found

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

al.(2010) that, a grade should communicate mastery of learning standards, homework is essentialfor learning but should not be included in the grade, and learning may take more thanone attempt. With these guiding principles, faculty at a mid-size, primarily undergraduateinstitution, investigated the effectiveness of a flipped mastery design with a hierarchical lettergrade scheme that rewarded those that went beyond the minimum amount of course contentassigned. Learning analytics, data visualizations, and statistical analysis were used to answerthe following questions: 1. Are students likely to stop learning once they achieve a passing grade for the course? 2. What motivates students to go beyond the minimum amount of course content? 3

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)

computer science or computer engineering-specific formal education or degrees. Toassess varying perspectives, we conducted a study utilizing Reddit posts. Reddit is a platformwhere many engineering students and practitioners may talk openly about different topics. Wecollected data using web scraping and analyzed it using a couple of Natural Language Processing(NLP) techniques, including Latent Dirichlet Allocation (LDA). Using the top keywords, wethen took a manual approach, using whole posts for context to perform thematic analysis toderive the topics. Our findings suggest that non-computing engineers are generally positive aboutdata science and its potential applications. They see it as especially important for 1) CareerProspects and

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 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)

propose a novelfeature engineering method as a way to study cooperation between a student feature sequence(e.g., financial aid, program change, etc.) and an outcome feature sequence (e.g., excess credits).As a result, each relevant student feature sequence is mapped into a feature value that attempts tocapture information that is relevant to the outcome. This enables a data-driven way to analyze theeffect of a large number of student features on excess credit accumulation.1 IntroductionThe credit hour was born out of the need to standardize learning for all students, to improveefficiency of institutions, to facilitate cross-institutional transfer, and to keep tabs on curriculumquality [20]. Recently, it has additionally grown into an instrument

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)

a shortage offreshly graduated, qualified data scientists, raising concerns for both academia and industries[1, 2]. Additionally, research on data science education assessments lacks, leaving manyuncertainties surrounding students’ pre-graduation skills. This paper addresses this limitationand develops a data science self-efficacy survey to evaluate and quantify individuals’confidence levels in applying data science skills to build data-driven solutions, with the goalto enhance the learning experience within data science education. Also, remedial activitieswere proposed to boost students’ confidence based on individual confidence levels. Surveydevelopment followed a modified Vinay approach, which guided construction of customizedassessments

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)

education [1]. Online learning is not a new concept, especially not in highereducation. Massive open online courses (MOOCs) were first introduced in 2008 [2]. Onlinecollege educational offerings date back to the late 1980s, starting with the University of Phoenix[3]. However, these examples were not set up for the traditional college students who were oncampus for a more intimate experience. Rather, the goal was to create an option for those whomight not be able to attend classes face-to-face due to location or schedule to still pursue highereducation [4]. Over the pandemic, all university students experienced online learning, not byoption, but by necessity. This resulted in the largest group of online learners the universitysystem had seen. The

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)

Thermodynamics QuestionsIntroductionThis paper describes the results of a study where generative Artificial Intelligence (AI) was usedto analyze short-answer explanations to two conceptually challenging chemical engineeringthermodynamics problems. This work comes from a collaboration between machine learning andengineering education researchers utilizing machine learning to analyze student narratives ofunderstanding in short-answer explanations to conceptually challenging questions [1], [2].Concept questions, sometimes called ConcepTests [3], are multiple-choice questions involvingminimal calculations and give students experience applying conceptual knowledge. Whenutilized within active learning pedagogies, concept questions have been shown to

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 course. We characterized ChatGPT usage as either productive or unproductivefor learning and defined four general reasons why students engaged with AI in this course:ChatGPT as 1) A learning aid 2) A coding resource specifically 3) An inevitability 4) A personal perspectiveWe discuss some of the ways that students can use AI responsibly as an asset to their learning.Their responses also show student awareness and current understanding of the positives andnegatives of AI use for acquiring and applying foundational programming skills. The results alsoshow that the majority of students who chose to use AI did so to enhance their learning ratherthan replacing their original work.Introduction and

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)

green channel correlation method for versatile identification.Miah Abdullah Sahriar1†, Mohd. Rakibul Hasan Abed1†, Ratchanok Somphonsane2, Houk Jang3,Chang-Yong Nam3, Saquib Ahmed5,6*1 Department of Materials and Metallurgical Engineering (MME), Bangladesh University ofEngineering and Technology (BUET), East Campus, Dhaka-1000, Bangladesh2 Department of Physics, School of Science, King Mongkut’s Institute of TechnologyLadkrabang, Bangkok 10520, Thailand3 Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York11973, USA5 Department of Mechanical Engineering Technology, SUNY – Buffalo State University, 1300Elmwood Avenue, Buffalo, NY 14222, USACenter for Integrated Studies in Nanoscience and Nanotechnology

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 model that best fits the data on hand is very important before starting thework. Not only does knowing what data a model was trained on give insight into its strengthsand limitations for different tasks but also understanding the training data of a model providesinformation about the contexts and patterns the model will recognize. This knowledge allows foran assessment of which types of tasks the model will execute effectively versus those at which itmay struggle. Selecting an appropriate model and having knowledge of its training data helpsensure optimal results. For example, NLP techniques like sentiment analysis on short responsesand word clustering perform relatively well [1]. But, when applied to large text formats, theaccuracy of

Conference Session

DSA Technical Session 3

Collection

2024 ASEE Annual Conference & Exposition

Authors

Aidan Kenny, Northeastern University; Andrew L Gillen, Northeastern University

Tagged Topics

Data Science & Analytics Constituent Committee (DSA), Diversity

studycreates a visual map of the holistic engineering education experience. Case studies 1 and 2compare traditional and creative data visualization techniques, whereas case study 3 introducesnew visualization for understanding the engineering education field. Bar plots, heatmaps,infographics, and systemograms are explored in this paper. This work not only enhances theunderstanding of the critical issues addressed in the case studies but also highlights the potentialof creative data visualization in addressing multifaceted challenges.IntroductionAcademic research in all fields is driven and supported by data. It is fundamental to decisionmaking as it provides evidence to support hypotheses, refute past claims, and give insight topatterns and trends

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

analytics is the process of inspecting, cleaning, transforming, and visualizing data with thegoal of discovering insightful and critical information for decision making [1]. The integration ofdata analytics in STEM education has had a profound impact on the advancement in every sectorof industries, government, and academia today. A workforce equipped with essential dataanalytics skills is crucial to maintaining the growth and security in nuclear energy area. Researchshows that data analytics skills are in high demand in order to generate data-driven, robustsolutions to solving the challenges that our society is facing today [2][3]. As major nuclearscience and engineering problems rely on predictive computational modeling and simulation

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)

., compared to 2.6 million in India and 4.7 million in China [1].STEM literacy is critical to human capital competency for the economy [2]. Therefore,encouraging more high school students to aspire to STEM careers can increase the likelihood ofapplying for jobs in STEM fields. Because many internal and external factors may influence highschool students’ aspirations for STEM careers, previous research on this topic often employs atheory-driven approach to identify predictors from large scale survey (LSS) data and formulatehypotheses for statistical tests. Existing LSS datasets, such as the Education Longitudinal Studyof 2002 (ELS:2002), promise a comprehensive investigation of the factors that contribute to highschool students' persistence in STEM

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)

forest models; entropy;computer adaptive testing; artificial intelligenceIntroduction Effective and impactful education is reliant on accurate and equitable assessment oflearning and proficiency. Large-scale and local assessments are used for determining admissioninto programs, for course placement, for determining which students have mastered courselearning outcomes, for reinforcing learning and providing feedback, for informing pedagogy andinterventions, and for developing self-regulated learning skills [1], [2], [3], [4]. Cognitive fatigue (CF) is a well-documented phenomenon characterized by diminishedperformance throughout the day, over the course of prolonged cognitive tasks, and even within thefirst few questions on single

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

, Geometric Abstraction, and Mathematics as they relate toengineering and art. Woven into the theoretical content are hands-on projects where studentslearn basic sketching skills, hand build a ceramic still-life piece, visit local galleries andmuseums, and, using elements or art and principles of design, turn data into data visualizationsand data physicalizations: data-driven physical artefacts whose geometry or material propertiesencode data. Students use an adapted Jansen and Dragicevic [1] information visualizationpipeline to move from raw data to data wrangling to visual and physical presentation. This paperpresents examples of the process and concludes with observations and lessons learned.Figure 1. Informa0on visualiza0on pipeline. Jansen 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)

complexity of the OTP problem is analyzed, which is shown to be N P-complete.Following this, an integer quadratic programming approximation algorithm is proposed for theOTP problem. Experiments involving transfers between two Arizona institutions are conducted todemonstrate the efficacy of the proposed algorithm.The Optimal Transfer Pathway ProblemIn the Optimal Transfer Pathway (OTP) problem, the input consists of: 1. A Boolean formula (requirements tree) representing the complete set of degree requirements from community college CC associated with a particular associate degree, denoted AD, 2. A map of transfer equivalences that details how all courses offered at CC transfer (or not) to courses at university U , and 3. A Boolean

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)

program curriculum and data science competencies used in this study wereidentified in an earlier study [4], which examined 136 colleges and their undergraduate DataScience degree program curriculum. The competencies detailed in Table 1 are drawn from theData Science Task Force of the Association of Computing Machinery (ACM) report[4], whichidentified 11 core data science competencies shown in Table 1. Table 1: Data Science Competencies and Sub-topics by 2021 ACM Data Science Task Force ACM Data Science Task Force Report Competencies1. Analysis and Presentation 7. DataPrivacy, Security, Integrity, and Analysis for ● Foundational considerations

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)

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