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Application of Data Analysis and Visualization Tools for U.S. Renewable Solar Energy Generation, Its Sustainability Benefits, and Teaching In Engineering Curriculum

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

Relevance Score: 0.05783289

Integrating Data Science into the Pipeline Building Toward a Diversified Workforce in Nuclear Energy and Security

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

, thenmonitored the progress during the Application Phase.Results and DiscussionThis section summarizes the data and results obtained from the summer enrichment programs inthe last three years.The number of students in each cohort is included in Table 2. The student participants andminority students rates were relatively low in 2021 due to covid-19 disruption and remotelearning/working in schools and universities, but were increased quickly in 2022-2023 with theteam’s recruiting effort and when the communities were back to normal operations, as shown inFigure 3. © American Society for Engineering Education, 2024 2024 ASEE Annual Conference and Exposition Table 1. Student

Relevance Score: 0.05036838

Work-in-Progress: Fine-Tuning Large Language Models for Automated Feedback in Complex Engineering Problem-Solving

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)

us to explore the effectiveness of utilizing well-structured, expert-createdcontent as a standalone fine-tuning source.In addition to sentence annotation for fine-tuning, 169 and 109 sentences from the ProblemDefinition sections of the 2022 and 2019 reports were manually annotated for validationpurposes. The purpose of these datasets is to evaluate the model’s classification accuracyperformance across different fine-tuning exercises. Details regarding the number of sentencesand the sources for each fine-tuning dataset used in this study are summarized in Table 1. The2022 dataset was in the context of an assistive robotic arm design, and 2019 was a scale modelhyperloop vehicle design. In each dataset, the name “2019” or “2022” corresponds to

Relevance Score: 0.04997333

Preparing Undergraduate Data Scientists for Success in the Workplace: Aligning Competencies with Job Requirements

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)

present study. Table 2: Data Science Competency (Milonas, Li, & Zhang, 2022) Category Competency Categories Data Science Competencies (Milonas, Li & Zhang, 2022) (Corresponding ACM Task Force) 1 Computing Fundamentals 4. Computing and Computer Fundamentals 9. Programming, data structures, and algorithms 10. Software development and maintenance 2 Data Management, Governance, 5. Data Acquisition, Management & Governance Privacy 7. Data Privacy, Security, Integrity, Analysis for Security

Relevance Score: 0.0436203

A Comparative Analysis of Natural Language Processing Techniques for Analyzing Student Feedback about TA Support

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)

student feedback.Specifically, it assesses student preferences for Teaching Assistant (TA) support in engineeringcourses at a large public research university. This work complements existing research with anin-depth comparative analysis of NLP approaches to examining qualitative data within the realmof engineering education, utilizing survey data (training set = 1359, test set = 341) collected from2017 to 2022. The challenges and intricacies of multiple types of classification errors emergingfrom five NLP methods are highlighted: Latent Dirichlet Allocation (LDA), Non-NegativeMatrix Factorization (NMF), BERTopic, Latent Semantic Analysis (LSA), and PrincipalComponent Analysis (PCA). These results are compared with results from traditional

Relevance Score: 0.037776284

Effectiveness of a Semi-Mastery-Based Learning Course Design

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

learn if given the time, and that both theteacher and the learner share responsibility for the desired learning (Anderson, 1975). In areview of mastery learning, Winget and Persky (2022) explain that mastery learning mayresult in better performance because it establishes an environment that supports motivation,offers regular opportunities for testing, and provides repeated feedback. The challenge is tofind practical ways of implementing mastery learning, or incorporating aspects to supportindividual needs within a course, while covering required content within a set time span(e.g. semester), and devising a grading system that matches students’ level of competencewith flexibility to acknowledge those that go beyond expectations (Guskey, 2007).In

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Bridging Theory and Practice: Building an Inclusive Undergraduate Data-Science Program

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

.[19] S. Fan, R. Y. Lau, and J. L. Zhao, “Demystifying big data analytics for business intelligence through the lens of marketing mix,” Big Data Research, vol. 2, no. 1, pp. 28–32, 2015.[20] Sep 2023. [Online]. Available: https://www.bls.gov/ooh/math/data-scientists.htm[21] M. A. Halwani, S. Y. Amirkiaee, N. Evangelopoulos, and V. Prybutok, “Job qualifications study for data science and big data professions,” Information Technology & People, vol. 35, no. 2, pp. 510–525, 2022.[22] J. C. Adams, “Creating a balanced data science program,” in Proceedings of the 51st ACM technical symposium on computer science education, 2020, pp. 185–191.[23] K. D. Schubert and M. D. Rossetti, “Creating a multi-college interdisciplinary bs data

Relevance Score: 0.032715224

From Manual Coding to Machine Understanding: Students' Feedback Analysis

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

identified themes in this study. Future research couldexplore alternative approaches (e.g., GPT-4) to streamline the clustering and code generationprocesses, potentially leveraging advanced natural language processing techniques to automatethe identification and consolidation of overlapping themes.References[1] A. Alsharif, A. Katz, D. Knight, and S. Alatwah, “Using Sentiment Analysis to Evaluate First-year Engineering Students Teamwork Textual Feedback,” in 2022 ASEE Annual Conference & Exposition, 2022. Accessed: Nov. 28, 2023. [Online]. Available: https://peer.asee.org/41460.pdf[2] R. S. Baker and P. S. Inventado, “Educational Data Mining and Learning Analytics,” in Learning Analytics: From Research to Practice, J. A. Larusson

Relevance Score: 0.026988681

An Online Interdisciplinary Professional Master’s Program in Translational Data Analytics

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

perspective challenges in big data,” in Advances in Intelligent Data Analysis and Applications: Proceedings of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, 15-18 October 2019 Arad, Romania. Springer Singapore, 2022, pp. 309–325.[4] Bureau of Labor Statistics, “Data Scientists,” Occupational Outlook Handbook. 2023. Accessed: Aug. 15, 2023. [Online]. Available: https://www.bls.gov/ooh/math/data- scientists.htm[5] J. A. Rosendale, “Gauging the value of MOOCs,” Higher Education, Skills and Work- Based Learning, vol. 7, no. 2, pp. 141–154, Jan. 2017, doi: 10.1108/HESWBL-09-2016- 0065.[6] National Academies of Sciences, “Data science for undergraduates: Opportunities and

Relevance Score: 0.026711868

Using Machine Learning to Analyze Short-Answer Responses to Conceptually Challenging Chemical Engineering Thermodynamics Questions

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)

instructors and researchers in theirpursuit to evaluate student understanding.AcknowledgmentsWe acknowledge the support from the National Science Foundation (NSF) through grant EEC2226553. Any opinions, findings, conclusions, or recommendations expressed are those of theauthors and do not necessarily reflect the views of the NSF.References[1] H. Auby, N. Shivagunde, A. Rumshisky, and M. Koretsky, “WIP: Using machine learning to automate coding of student explanations to challenging mechanics concept questions,” in Proceedings of the 2022 American Society of Engineering Education Annual Conference & Exposition, Jun. 2022. [Online]. Available: https://peer.asee.org/40507[2] H. Auby and M. Koretsky, “Work in progress: Using

Relevance Score: 0.024887314

A Hybrid Approach to Natural Language Processing for Analyzing Student Feedback about Faculty Support

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)

process to enhance the value of NLP in educationalresearch?Methodology Research Question (RQ2):How does data analysis involving both unsupervised learning methods compared with analysisusing only unsupervised methods?ParticipantsThe study involved a total of 1,857 participants, consisting of sophomores and juniors from fourdifferent engineering majors enrolled as undergraduates. The participants were surveyed betweenthe winter of 2017 and the spring of 2022. The study population was divided into two settings:traditional (in-person) prior to the COVID-19 pandemic and emergency remote teaching (ERT),which was conducted remotely during the pandemic. The gender distribution showed that 74.1%of the participants were male, 24.4% were female, and a

Relevance Score: 0.023133155

Credit-Hour Analysis of Undergraduate Students Using Sequence Data

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)

aid variables), which are among theprevalent notions of extra credit accumulation, using the derived co-occurrence variables.4.1 DatasetWe experimented on real transcript data of Bachelor’s-degree graduated students from a largepublic (R1) university. The campus-wide dataset includes 11038 students that graduated betweenFall 2015 and Summer 2022. The campus-wide (all) student cohort included students from 114distinct degree programs. The number of campus-wide Transfer students (students thattransferred into the university) was 3015. Some noteworthy raw statistics for the undergraduatestudent group are: 9661 graduated with greater than 0 extra credits, 8841 graduated with greaterthan 0 excess (Ex) credits, 4100 graduated with greater than 0

Relevance Score: 0.023133155

A Qualitative Study of Engineers’ Perception of Variability as 'Error'

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)

: 10.1111/j.1751-5823.1999.tb00442.x.[7] G. H. Golub, J. M. Ortega, and J. M. Ortega, Scientific computing and differential equations: an introduction to numerical methods. Boston: Academic Press, 1992.[8] K. Hadley and W. Oyetunji, “Extending the Theoretical Framework of Numeracy to Engineers,” J. Eng. Educ., vol. 111, no. 2, pp. 376–399, Apr. 2022, doi: 10.1002/jee.20453.[9] D. Salsburg, The lady tasting tea: how statistics revolutionized science in the twentieth century, First Holt paperbacks edition. New York: Henry Holt and Company, 2002.[10] D. Lane, M. Hebl, R. Guerra, D. Osherson, and H. Zimmer, Online Statistics Education: An Interactive Multimedia Course of Study. Accessed: Jan. 18, 2023. [Online]. Available: https

Relevance Score: 0.022259891

Unfettered ChatGPT Access in First-Year Engineering: Student Usage & Perceptions

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

BackgroundLarge language models (LLMs) are artificial intelligence (AI) tools trained to create human-likecontent such as text and images through simple prompts by processing extremely large sets ofdata [1], [2]. LLMs include generated pre-trained transformers (GPT) like ChatGPT, an Open AItool. A very popular LLM since its release in 2022, ChatGPT has garnered attention from bothindustry and academia. These tools have been applied across various fields for purposesincluding generating codes in programming [3], helping with language translation [4], writingassistance[5], replying to mental health questions [6], and scheduling construction projects [7].In the academic context, ChatGPT has been used in research, teaching and learning [8] includingwithin

Relevance Score: 0.022259891

Innovating Engineering Education Analysis through Creative Data Visualization

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

and slowly changing problem. 1.00 50 1.09 1.29 40 1.65 Percent Women 2.19 Male : Female Ratio 30 3.03 4.18 20 10 0 2012 2022 2032 2042 2052 2062 2072

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Minimizing Curricular Complexity through Backwards Design

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)

thepool of optimal solutions could be another direction for future research.References1 ABET. Criteria for Accrediting Engineering Programs: Effective for Reviews During the 2022–2023 Accreditation Cycle. 2022. www.abet.org.2 Edmund C. Short. A historical look at curriculum design. Theory Into Practice, 25(1):3–9, 1986.3 Grant Wiggins and Jay McTighe. Understanding by Design. Association for Supervision and Curriculum Develop- ment, Alexandria, VA, 2nd edition, 2005.4 Gregory L. Heileman, Chaouki T. Abdallah, Ahmad Slim, and Michael Hickman. Curricular analytics: A framework for quantifying the impact of curricular reforms and pedagogical innovations. www.arXiv.org, arXiv:1811.09676 [cs.CY], 2018. arxiv.org/abs

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Data-Science Perceptions: A Textual Analysis of Reddit Posts from Non-Computing Engineers

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)

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Extra Credit Analysis of Undergraduate Engineering Students

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

public (R1) universitywas used for experimentation. The campus-wide dataset includes 11038 students that graduatedbetween Fall 2015 and Summer 2022 from this university, 999 of which were engineeringstudents. The campus-wide (all) student cohort encompassed 114 different degree programs. Thenumber of campus-wide Transfer students (students that transferred into the university understudy) in the dataset was 3015, out of which 263 were engineering. Following are a few notableraw statistics among the engineering graduates: 971 graduated with greater than 0 extra credits,778 graduated with greater than 0 excess (Ex) credits, 852 of them graduated with greater than 0unusable-earned (UE) credits, 211 of them graduated with greater than 0 unusable

Relevance Score: 0.018888142

Developing an Instrument for Assessing Self-Efficacy Confidence in Data Science

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)