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

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Data Science & Analytics Constituent Committee (DSA)

Paper ID #42697Envisioning and Realizing a Statewide Data Science EcosystemDr. Karl D. Schubert FIET, University of Arkansas Dr. Karl D. Schubert is a Professor of Practice and serves as the Associate Director for the Data Science Program at the University of Arkansas College of Engineering, the Sam M. Walton College of Business, and the Fulbright College of Arts & Sciences.Shantel Romer, University of ArkansasStephen R. Addison, IEEE Educational ActivitiesTina D MooreLaura J Berry, North Arkansas CollegeJennifer Marie Fowler, Arkansas State UniversityLee Shoultz, University of ArkansasChristine C Davis

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

scoring scale. Students completing Modules 1-5 couldonly earn a maximum of a ‘B’ letter grade with scores in the 90s since they learned less thanwhat is usually covered in the course. However, students completing Modules 1-7 could earnan ‘A’ even with a numerical score in the 80s because they covered more content than isnormally taught. A grade of ‘C’ or better was required to pass the course. Modules had tobe completed in chronological order, with up to three weeks allotted for each of Modules 1-4and four weeks for Module 5. The time spent on each module is shown in Table 1 with thetraditional schedule for comparison.Table 1: Number of weeks spent on each module in a traditional 16-week, face-to-face course,compared to the flipped-mastery design

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)

tool.Column A lists the names of the professors who are scheduled to teach in the term. Column B liststhe TLC capacity for each professor. For example, Prof A is teaching a full load (12 TLCs) whileProf B, also serving as the department chair, is only available for 6 TLCs. Similarly, Prof C has alower TLC capacity of 9. This is due to an endowed professorship that comes with 25% teachingload release. Finally, Prof H is an adjunct instructor with a TLC capacity of only 3. Any user caneasily add or remove courses or professors by adding or removing columns / rows in the shadedregions.3.2 Teaching preferences matrixThe remaining columns (C - R) correspond each to a course that is scheduled for offering. Figure2 does not show all the columns to fit the

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)

://onlinestatbook.com/[11] R. R. Sokal and F. J. Rohlf, Biometry: the principles and practice of statistics in biological research, 3rd ed. New York: W.H. Freeman, 1995.[12] D. Thunnissen, “Uncertainty Classification for the Design and Development of Complex Systems,” 2003.[13] B. M. Ayyub, Ed., Uncertainty modeling and analysis in civil engineering. Boca Raton: CRC Press, 1998.[14] A. A. diSessa, “Toward an Epistemology of Physics,” Cogn. Instr., vol. 10, no. 2–3, pp. 105–225, 1993.[15] A. diSessa, “A History of Conceptual Change Research: Threads and Fault Lines,” in The Cambridge handbook of: The learning sciences, Cambridge University Press, 2006, pp. 265–281.[16] A. J. Magana, “The role of frameworks in engineering education

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

MSIPP DE-NA0003980.The authors are thankful to the support of the DOE/NNSA program manager and the colleaguesat participating universities and national labs. Special thanks to Dr. Stephen Egarievwe atMorgan State University for his constant support and collaboration.References 1. J. Kennedy, P. Abichandani and A. Fontecchio, “An initial comparison of the learning propensities of 10 through 12 students for data analytics education,” IEE Frontiers in Ed- ucation Conference, Oklahoma City, OK, pp. 916-918, 2013. 2. Hirsch, D. D. (2013). The glass house effect: Big Data, the new oil, and the power of analogy. Me. L. Rev., 66, 373. 3. Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2020). Big data analytics

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)

to c to simulateguessing on an item. In the CF condition, probability was simulated using the modified 4PL IRTmodel (equation 2), where i is item number. A linear decrease of 0.005 per item was used tosimulate the results found in the studies by Reyes [9] and Balart [16]. 1 𝑃4𝑃𝐿 (𝜃) = 𝑐 + (𝑑 − 𝑐) 1+𝑒 [−1.702𝑎(𝜃−𝑏)] (1) Table 1: Simulation Parameters Student Population Ability (ϴ) Normal: mean = 0, variance = 1 Gender Binomial: p = 0.5 URM Status Binomial: p = 0.2 Test Bank a Lognormal: mean = 1, variance = 0.12 b Normal: mean = 0, variance = 1 c

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)

investigating the value of (a) unsupervised (Non-negative Matrix Factorization)learning techniques that use various degrees of domain expert interaction and (b) supervised (naïveBayes and support vector machine) for analyzing short answer responses from a diverse surveydataset. The dataset is a highly heterogeneous (and therefore challenging) collection of text-baseddata, generated from asking a “reach for the moon” question to students about what they wouldlike faculty to do to better support their learning.MethodsThe study was conducted at a large public research institution located in an urban setting to explorevarious forms of instructional support and course level engagement such as faculty support, student-faculty interactions, attention

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)

Learning Analytics,” Learning Analytics, pp. 61–75, 2014, doi: 10.1007/978-1-4614-3305-7_4.[7] N. Kardam, S. Misra, and D. Wilson, "Is Natural Language Processing Effective in Education Research? A case study in student perceptions of TA support," presented at the 2023 ASEE Annual Conference & Exposition, 2023. [Online]. Available: https://peer.asee.org/43887[8] Katz, M. Norris, A. M. Alsharif, M. D. Klopfer, D. B. Knight, and J. R. Grohs, “Using Natural Language Processing to Facilitate Student Feedback Analysis,” in 2021 ASEE Virtual Annual Conference. Content Access, July 26-29, 2021. [online]. Available: https://peer.asee.org/using-natural-language-processing-to-facilitate-student-feedback

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)

Paper ID #41210Data-Science Perceptions: A Textual Analysis of Reddit Posts from Non-ComputingEngineersMr. Nicolas Leger, Florida International University Nicolas L´eger is currently an engineering and computing education Ph.D. student in the School of Universal Computing, Construction, and Engineering Education (SUCCEED) at Florida International University. He earned a B.S. in Chemical and Biomolecular Engineering from the University of Maryland at College Park in May 2021 and began his Ph.D. studies the following fall semester. His research interests center on numerical and computational methods in STEM education and in

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)

Framework for Curriculum AssessmentExpanding upon our previous discussions on curricular analytics, we examine the nuanced chal-lenge of analyzing the impact of curricula on student progression. This analysis is particularlycomplex due to the multifaceted nature of curriculum-related components influencing studentprogress. Our methodology focuses on decomposing the overall complexity of a curriculum into (a) (b)Figure 1: Undergraduate Electrical Engineering program structures at two major public univer-sities with the same ABET accreditation standards.two primary elements: instructional complexity, which refers to the pedagogical methods andsupport mechanisms

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 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)

-Miguel. Implementing the sustainable development goals in university higher education: A systematic review. International Journal of Sustainable Development and Planning, 18(6):1769–1776, 2023. 4 S. Lee and D. Shapiro. Completing college: National and state report with longitudinal data dashboard on six- and eight-year completion rates. (signature report 22),. Technical report, National Student Clearinghouse Research Center, Herndon, VA, 2023. 5 A. Valero and J. Van Reenen. The economic impact of universities: Evidence from across the globe. Economics of Education Review, 68:53–67, 2019. 6 M. Hanson. College Graduation Statistics. 2023. EducationData.org. 7 B. Yoder. ASEE Retention and Time-to-Graduation

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)

to first attain l2 . (a) If l1 andl2 are placed in one course, and l3 and l4 in the other, a prerequisite constraint is created betweencourses v1 and v2 , and the structural complexity of the pattern is 5. (b) If l2 and l3 are placed in onecourse, and l1 and l4 in the other, then all prior required learning is contained within course v1 , nocourse-level prerequisites are required, and the structural complexity of the pattern is 2.complexity of a curriculum. Furthermore, if we consider the same engineering program at differ-ent institutions, we will find there is often significant variation among the structural complexitiesof the curricula offered by these programs. As mentioned previously, those with lower structuralcomplexity will have

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)

the multitude of formulas that can describe thesequantities in different situations provide students with a challenging experience of balancingconceptual and procedural knowledge [63], [64]. Thus, we chose these concept questions tounderstand narratives of understanding in short answer responses and provide a large set ofconcepts to train our machine learning algorithms.Figure 1. Student view of CT 1072 (A) and 1073 (B) on the Concept Warehouse. The imageshows the multiple-choice question and the short-answer response field analyzed in this study.The correct answers are in the green boxes.We can visualize the mixing process described in Figure 1 through the representation shown inFigure 2. The enthalpy change in concept question 1072 is zero

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

reported thatbeing a STEM major at graduation was positively correlated with extra credit accumulation [15],indicating that STEM students are more likely to graduate with extra credits. This result can beextended by a quantitative analysis of the rate, and composition, factors influencing extra creditsaccumulated by STEM students in comparison to campus-wide (all) students. As mentioned inSection 1, we pursue this direction through investigation of the accumulation hypothesis.3 MethodologyThe audit tool solves an optimization problem that matches classes to degree requirements in away that maximizes the number of applied credits, subject to the constraints that a) each class ismatched to at most one requirement and b) no excess credits are

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)

creditaccumulation, (b) is the sequence data helpful in explaining factors affecting extra creditaccumulation, and (c) is it possible to generate a feature that captures the cooperation betweenstudent feature sequence and excess sequence in such a way that it is helpful in explaining theoutcome, i.e., excess. The main contributions of this work can be succinctly given as: • A non-traditional approach to analyze the credit efficiency of undergraduate students by treating credit and student data as a term-by-term sequence – in particular the term-by-term excess credit sequence data. This approach, combined with the fact that the analysis takes into account the usability of the credits towards the student’s degree program requirements

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)

Requirement Set Course Set ρ1j ρkj Requirement 1 Requirement m (a) (b)Figure 2: The two types of structures used to construct a requirements tree. (a) A course set re-quirement consists of a collection of course/minimum grade pairs {ρ1j , . . . , ρjk }, as well as thenumber of credit hours taken from the courses in {ρ1j , . . . , ρjk } that must be successfully com-pleted (i.e., earn at least the minimum grade) in order to satisfy the requirement. (b) A requirementset consists of a set of requirements, i.e., course sets or other requirements sets, along with aspecification of how many of them must be satisfied

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)

of learning emerged with a more granular look into (a) TimeDependent domain with week-by-week evolution of learning analytics or (b) Content Dependentdomain by studying how students interacted with video lecture content type. A decision wasmade to generalize the outcomes of learning analytics rather than focus on individual students.The aim here was to capture broad narratives about the two courses that can eventually helpinstructors when designing learning experiences for a broad audience. Thus the students fromeach course were grouped into quartiles based on their final course performance. The lowerquartile consists of students in the 25% percentile, the upper quartile consists of students in the25% percentile, and the middle quartile with

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)

Establishing realistic timelines and defining achievable milestones using the data science life cycle. SM 3 Exploring a domain to acquire the necessary knowledge for a specific data science project. SM 4 Exploring trends and preparing reviewed literature and other scholarly justification from the data science project B, SM 5 My ability to formulate investigative questions that align with the nature of the problem. SM 6 My ability to consider ethical implications related to data privacy, bias, and fairness throughout the process. SM 7 Creating clear documentation for code, models, and any

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)

emotion modulation in learning systems," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 8, pp. 4624-4638, 2019.[8] K. Zhang, Y. Li, J. Wang, E. Cambria, and X. Li, "Real-time video emotion recognition based on reinforcement learning and domain knowledge," IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 3, pp. 1034-1047, 2021.[9] E. Lakomkin, M. A. Zamani, C. Weber, S. Magg, and S. Wermter, "Emorl: continuous acoustic emotion classification using deep reinforcement learning," in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018: IEEE, pp. 4445- 4450.[10] B. McFee et al., "librosa: Audio and music signal analysis in python

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)

, providing users with acomprehensive view of the sentiment analysis results. The user interface allowed for real-timeinteraction, enabling users to input different project group IDs and observe the correspondingchanges in the displayed results. (a) Chatbot design (b) User Interface Figure 4. Proposed Chatbot Interface CHATME.Framework for Virtual AssistantAs we built the chatbot, we began to question if a chatbot would provide the easiest and mostintuitive user experience. A chatbot would require users to type the project ids and/or first andlast names of students whose status they wanted to check. Users would also need to know whatfunctions our web app offers and specify the type of information that they were interested in, beit

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)

. Kasneci et al., "ChatGPT for good? On opportunities and challenges of large language models for education," Learning and Individual Differences, vol. 103, p. 102274, 2023/04/01/ 2023, doi: https://doi.org/10.1016/j.lindif.2023.102274.[11] K. A. Gamage, S. C. Dehideniya, Z. Xu, and X. Tang, "ChatGPT and higher education assessments: more opportunities than concerns?," Journal of Applied Learning and Teaching, vol. 6, no. 2, 2023.[12] J. Rudolph, S. Tan, and S. Tan, "ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?," Journal of Applied Learning and Teaching, vol. 6, no. 1, 2023.[13] J. Finnie-Ansley, P. Denny, B. A. Becker, A. Luxton-Reilly, and J. Prather, "The robots are

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

Press, 2018. [2] P. Fox and J. Hendler, “Changing the Equation on Scientific Data Visualization,” Science, vol. 331, no. 6018. American Association for the Advancement of Science (AAAS), pp. 705–708, Feb. 11, 2011. doi: 10.1126/science.1197654. [3] Q. Li, “Data visualization as creative art practice,” Visual Communication, vol. 17, no. 3. SAGE Publications, pp. 299–312, Apr. 17, 2018. doi: 10.1177/1470357218768202. [4] W. E. B. (William E. B. Du Bois), “A series of statistical charts illustrating the condition of the descendants of former African slaves now in residence in the United States of America,” Library of Congress, https://www.loc.gov/pictures/item

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)

aspirations. Total count 8 6 13 11 81 ELS 2002 variablesNote that X* indicates that the theory-driven model selected and marked this variable assignificant, while Boruta identified this variable as important. X label meant the theory drivemodel selected, but not significant. The dependent variable of this study was to describe the 12-grade students who expect tochoose STEM occupations at age 30. The original variable included 9 STEM occupations, notSTEM occupations, and the option of I don’t know. Therefore, we combined the STEM jobscategories and recreated this variable to become (a) 1: STEM occupation (56.18%) and (b) 0:non-STEM occupation (43.82%). The non-STEM occupation includes non-STEM jobs

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

and B. White, Eds., New York, NY: Springer, 2014, pp. 61–75. doi: 10.1007/978-1-4614-3305-7_4.[3] A. Katz, M. Norris, A. M. Alsharif, M. D. Klopfer, D. B. Knight, and J. R. Grohs, “Using Natural Language Processing to Facilitate Student Feedback Analysis,” presented at the 2021 ASEE Virtual Annual Conference Content Access, Jul. 2021. Accessed: Dec. 07, 2023. [Online]. Available: https://peer.asee.org/using-natural-language-processing-to-facilitate-student-feedback-analy sis[4] D. Jurafsky and J. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, vol. 2. 2008.[5] C. Manning and H. Schutze, Foundations of Statistical Natural

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)

layers," as the number of layers in this category can besubstantial, establishing a reliable linear relationship becomes challenging. Therefore, it isrecommended to classify anything other than monolayer or bilayer as "few layers." 7 fig (a) fig (b) fig (c)Fig. 4 Linear relation between (a) Gback and Gmono, (b) Gback and Gbi (c) parallel relationship between Gmono and GbiFrom the above developments, we developed the linear regression-based approach (for settingthe threshold criteria) which produces a dynamic threshold.The initial step in this approach

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

. D. S. T. Force, Computing Competencies for Undergraduate Data Science Curricula. New York, NY, USA: Association for Computing Machinery, 2021. [28] A. f. C. M. A. Joint Task Force on Computing Curricula and I. C. Society, Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science. New York, NY, USA: Association for Computing Machinery, 2013. [29] A. S. Association et al., “Curriculum guidelines for undergraduate programs in statistical science,” Retrieved December, vol. 15, p. 2017, 2014. [30] B. Cassel and H. Topi, “Strengthening data science education through collaboration,” in Workshop on data science education workshop

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

. Huang, X. Wang, S. Shi, and Z. Tu, “Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine.” arXiv, Nov. 02, 2023. doi: 10.48550/arXiv.2301.08745.[5] T. Eloundou, S. Manning, P. Mishkin, and D. Rock, “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models.” arXiv, Aug. 21, 2023. doi: 10.48550/arXiv.2303.10130.[6] H. Kumar et al., “Exploring The Design of Prompts For Applying GPT-3 based Chatbots: A Mental Wellbeing Case Study on Mechanical Turk.” arXiv, Sep. 22, 2022. doi: 10.48550/arXiv.2209.11344.[7] S. A. Prieto, E. T. Mengiste, and B. García de Soto, “Investigating the Use of ChatGPT for the Scheduling of Construction Projects,” Buildings, vol. 13, no. 4, Art. no. 4, Apr. 2023

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

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)

IEEE Global Engineering Education Conference (EDUCON), pp. 1329-1336, IEEE, 2021.[2]. B. A. Quismorio, M. A. D. Pasquin, and C. S. Tayco, "Assessing the alignment of Philippine higher education with the emerging demands for data science and analytics workforce," PIDS Discussion Paper Series, 2019, no. 2019-34.[3]. M. Almgerbi, A. De Mauro, A. Kahlawi, and V. Poggioni, "A systematic review of data analytics job requirements and online-courses," Journal of Computer Information Systems, vol. 62, no. 2, pp. 422-434, 2022.[4]. E. Milonas, Q. Zhang, and D. Li, "Do Undergraduate Data Science Program Competencies Vary by College Rankings?" In Proceedings of the 2022 ASEE Annual Conference & Exposition, Minneapolis, MN