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Enhancing Academic Pathways: A Data-Driven Approach to Reducing Curriculum Complexity and Improving Graduation Rates in Higher Education

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)

it holds a pivotal position withinthe curriculum. Figures 2b and 2d depict the blocking factors for the courses in the illustratedcurricula.Combining these analyses, we introduce a metric to define the cruciality of a course i, denotedCi , as the aggregate of its blocking and delay factors: Ci = Vi + LiThe curriculum’s overall complexity, S, is then calculated as the sum of the cruciality values forall courses: m X S= Ci (1) 1 (a

Relevance Score: 0.025047062

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)

, numerical, quantitative, and DM qualitative data. 13 Understanding the structure and characteristics of diverse datasets. DM 14 Merging or joining datasets from different sources to create a unified dataset. DM, 15 Using appropriate tools to visualize data distributions of missing values, duplicate values, inconsistency types, and outliers. DM 16 My ability to inform decisions to standardize or normalize values as needed, depending on project requirements. S, ML, B 17 In making informed decisions on handling invalid data. Based on the visualized data distributions and stakeholders

Relevance Score: 0.018575398

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

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

Relevance Score: 0.018291788

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)

in educationalresearch on a broad scale.AcknowledgementsThe authors would like to gratefully acknowledge the National Science Foundation for theirpartial support of this work (DUE grant number 1504618). Any opinions, findings, andconclusions or recommendations expressed in this material are those of the author(s) and do notnecessarily reflect the views of the National Science Foundation.References[1] National Center for Education Statistics. (2020). The SAGE Encyclopedia of Higher Education. https://doi.org/10.4135/9781529714395.n400[2] M. Parry (2012). " Supersizing" the College Classroom: How One Instructor Teaches 2,670 Students. Chronicle of Higher Education.[3] M. Soledad, J. Grohs, S. Bhaduri, J. Doggett, J. Williams, and S

Relevance Score: 0.01680208

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)

] International Engineering Alliance, "Graduate Attributes and Professional Competencies," Jun. 21, 2021. Accessed: Oct. 14, 2021. [Online]. Available: https://www.ieagreements.org/assets/Uploads/IEA-Graduate-Attributesand-Professional- Competencies-2021.1-Sept-2021.pdf[2] D. H. Jonassen, Learning to solve problems: A handbook for designing problem-solving learning environments. Routledge, 2010.[3] S. Sheppard, K. Macatangay, A. Colby, W. M. Sullivan, and L. S. Shulman, Educating engineers: Designing for the future of the field, 1st ed. San Francisco, CA, USA: Jossey-Bass, 2009.[4] H. S. Lee, A. Pallant, S. Pryputniewicz, T. Lord, M. Mulholland, and O. L. Liu, "Automated text scoring and real‐time adjustable

Relevance Score: 0.01680208

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)

further expand somefields as we know them.There is also a growing body of work looking at data science applications in engineering [6].Although we know it may be applied or beneficial for the broader field and its subfields (e.g.,mechanical, industrial, chemical), we are limited in our understanding of how non-computingengineers may apply it in their work or practice. With that said, it is necessary to understand hownon-computing engineers may apply data science in their work, as this remains a challenge in thefield. In the context of engineering education and practice, Beck et al.’s article suggests addingdata science as a “competency” in chemical engineering both in “the university curriculum or ina professional development context.” They also

Relevance Score: 0.014217869

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: 0.013861008

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

] S. Negash, “Business intelligence,” Communications of the association for information systems, vol. 13, no. 1, p. 15, 2004. [5] S. Siuly and Y. Zhang, “Medical big data: neurological diseases diagnosis through medical data analysis,” Data Science and Engineering, vol. 1, pp. 54–64, 2016. [6] D. A. Jenkins, M. Sperrin, G. P. Martin, and N. Peek, “Dynamic models to predict health outcomes: current status and methodological challenges,” Diagnostic and prognostic research, vol. 2, no. 1, pp. 1–9, 2018. [7] J. Chen, K. Li, H. Rong, K. Bilal, N. Yang, and K. Li, “A disease diagnosis and treatment recommendation system based on big data mining and cloud computing,” Information Sciences, vol. 435, pp. 124–149, 2018. [8] L. Sun, C. Liu

Relevance Score: 0.013718842

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)

with the question, four in-context examples of answers, and the corresponding codes and instructed it to generate thecode(s) for the new answer instance. The in-context examples for GPT-4 prompt are drawn fromthe training split of the manually-coded dataset. We finetuned the Mixtral of Experts (MoE) [30]model using input and target pairs derived from the manually-coded training datasets. Thistrained model was then prompted with new test inputs, and the model-generated coded sequencewas evaluated against the manually coded target sequence. We evaluated both models on a testset of around 140 samples for each thermodynamics question. Using manual and languagemodel-based coding, we aim to answer two research questions: 1. What aspects of student

Relevance Score: 0.013607238

Continuous Speech Emotion Recognition from Audio Segments with Supervised Learning and Reinforcement Learning Approaches

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)

Discriminative Acoustic Features from Voiced Segments for Improving Speech Emotion Recognition Accuracy," International Journal of Advanced Research in Computer Science and Electronics Engineering, vol. 8, no. 9, pp. 39-44, 2019.[2] I. Trabelsi, D. B. Ayed, and N. Ellouze, "Improved frame level features and SVM supervectors approach for the recogniton of emotional states from speech: Application to categorical and dimensional states," arXiv preprint arXiv:1406.6101, 2014.[3] J. de Lope and M. Graña, "An ongoing review of speech emotion recognition," Neurocomputing, vol. 528, pp. 1-11, 2023, doi: 10.1016/j.neucom.2023.01.002.[4] S. Davis and P. Mermelstein, "Comparison of parametric representations for

Relevance Score: 0.013068285

Integrating Data-Driven and Career Development Theory-Driven Approaches to Study High School Student Persistence in STEM Career Aspirations

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)

high-dimensional data., and (2) these results can be interpreted to developstrategies to improve high school students’ STEM career aspirations and persistence. We hopethis study can inspire more educational researchers to use machine learning algorithms toanalyze big educational datasets.References[1] McCarthy, N. (2017). Recent graduates in STEMM. https://www.industryweek.com/talent/article/21998889/the-countries-with-the-most-stem- graduates Retrieved 23 April 2021.[2] Capraro, R. M., & Han, S. (2014). STEM: The education frontier to meet 21st century challenges. Middle Grades Research Journal, 9(3), XV.[3] Lent, R. W., Brown, S. D., & Hackett, G. (1994). “Toward a unifying social cognitive theory of career and academic

Relevance Score: 0.012934248

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)

technologies in the data science field. One of the limitations of this study is that it only examined job requirement data fromrepresentative metropolitan cities in each region instead of analyzing the job information for thewhole region. Additionally, further analysis of job requirements in different industries couldoffer in-depth insights into the alignment of data science in education and job requirements.Future studies may reveal whether jobs requiring specific domain expertise require students topursue advanced studies or degrees to meet these specific requirements effectively.6. Work Cited[1]. S. Gottipati, K. J. Shim, and S. Sahoo, "Glassdoor Job Description Analytics–Analyzing Data Science Professional Roles and Skills," in 2021

Relevance Score: 0.012934248

Investigating and predicting the Cognitive Fatigue Threshold as a Factor of Performance Reduction in Assessment

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)

adaptive assessment modelsto account for these effects, such endeavors will contribute to the development of more equitableand effective educational testing methodologies.References[1] D. M. Olsson and L. S. Nelson, “The nelder-mead simplex procedure for functionminimization,” Technometrics, vol. 17, no. 1, pp. 45–51, 1975, doi:10.1080/00401706.1975.10489269.[2] D. B. Wilson and A. Borgmann, “Technology and the Character of Contemporary Life: APhilosophical Inquiry,” Technol Cult, vol. 27, no. 4, p. 907, Oct. 1986, doi: 10.2307/3105376.[3] S. Stark, “Using action learning for professional development,” Educ Action Res, vol. 14,no. 1, pp. 23–43, 2006, doi: 10.1080/09650790600585244.[4] P. Gbadago, S. N. Amedome, and B. Q. Honyenuga

Relevance Score: 0.0127817765

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)

themes within the dataset, emphasizing the need fora nuanced evaluation of its effectiveness across different thematic categories. Table 3. Topics, Themes, and word clouds emerged from Method #2 (Most Frequently Occurring Words associated with Each Topic (N=1785)) Topic 1 Topic 2 Topic 3 'lecture', 'class', 'lectures', 'student 'office', 'hours', 'hold', 'available' 'practice', 'problems', 'exams', 'p s', 'notes', 'questions', 'time', 'make , 'offer', 'extra', 'help', 'provide', ' rovide', 'examples', 'tests', 'home ', 'online', 'slides' having', 'hour' work', 'extra', 'exam', 'example

Relevance Score: 0.012523532

Causal Inference Networks: Unraveling the Complex Relationships Between Curriculum Complexity, Student Characteristics, and Performance in Higher Education

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

HSGPAranges.Continuing from the insights provided by the KDE analysis, we further examine the variability inprogram complexity among universities. This part of the exploratory data analysis focuses on howthe structural aspects of university curricula influence student enrollment decisions. As highlightedin Figure 3, the distribution of program complexity varies notably between different institutions,such as University ’1’ and University ’3’. This variability is not merely incidental but indica-tive of these institutions’ diverse academic cultures and curricular frameworks. The KDE plot forUniversity ’1’, with a multi-peaked distribution, suggests a curriculum that offers a wide array ofprograms ranging from less to more complex. In contrast, University ’3’s

Relevance Score: 0.012348369

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

research paper will specifically explore the past production of solar energy in all the statesin the US, and with the use of data analysis tools will predict the production to the year 2030.The reduction of CO2 emissions with the use of renewable solar energy is in direct support of thethree elements of sustainability, namely the 3Es: Environment, Economics, and Equity (or socialjustice). This research will quantify the past benefits already realized in all these three areas forsolar energy, and project them up to 2030. Cluster analysis technique will be applied to solar generation across all US States to identifygroup(s) at distinct levels of production. This can help States to follow the leading State(s) policyand process to increase their solar

Relevance Score: 0.011899184

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)

following three co-occ features: cGA , corresponding to giftaid; cSH , corresponding to self help aid; and cW S , corresponding to federal work study. Based onthe non-zero median (10 credits) for the excess credit category seen in Fig. 2, the students weredivided into low excess (Low-Ex) and high excess (High-Ex) student cohorts, and thecorresponding co-occ features were derived. Table 1 provides the summary statistics for theseco-occ features.The results for all students show that cGA seems to have the most co-occurrences of gift aid withTable 1: Summary statistics for financial aid-based co-occ variables for all (All), low excess (Low-Ex), and high excess (High-Ex) student cohorts. Mean Std Median Q1

Relevance Score: 0.011880864

Optimizing Transfer Pathways in Higher Education

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)

requirements and required minimum num-ber of credits W . Set requirement tree TCC = ∅ and transfer equivalency map A = ∅. Next, set the Pncredit hours of each course requirement equal to cr[c1 ] = w1 , . . . , cr[cn ] = wn , set wi = S. Let i=1xi = 1 denote that course i is excluded in the produced degree plan, and xi = 0 denote that coursei is included in the plan. Then, any solution to the OTP problem using these instances exists only n Pif set I = {i

Relevance Score: 0.011880864

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

helps to communicate key findings [3]. By considering various datavisualizations methods as well as the design principles used to present them, understanding andinterpretation by the user can be improved.In the 1890’s, W.E.B. DuBois published artistic visualizations of African American civil rightsviolations that grabbed the public’s attention and clearly displays the intended conclusions [4].These pioneering data visualizations demonstrate the profound significance of creativity withinthe field of data representation and analysis. Through his use of intricate hand-drawn charts,graphs, and maps, DuBois demonstrated that the presentation of data could go beyond statisticsand become a powerful tool for conveying complex realities. For example

Relevance Score: 0.009240673

Paradigm Shift? Preliminary Findings of Engineering Faculty Members’ Mental Models of Assessment in the Era of Generative AI

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)

. 0, no. 0, pp. 1–17, 2023, doi: 10.1080/10494820.2023.2253861.[2] T. K. F. Chiu, Q. Xia, X. Zhou, C. S. Chai, and M. Cheng, “Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education,” Comput. Educ. Artif. Intell., vol. 4, p. 100118, 2023, doi: 10.1016/j.caeai.2022.100118.[3] T. K. F. Chiu, “Future research recommendations for transforming higher education with generative AI,” Comput. Educ. Artif. Intell., vol. 6, p. 100197, Jun. 2024, doi: 10.1016/j.caeai.2023.100197.[4] A. Smolansky, A. Cram, C. Raduescu, S. Zeivots, E. Huber, and R. F. Kizilcec, “Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher

Relevance Score: 0.009240673

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

copy and paste the answer without understanding the learning concept, it could be affect[ing] the way they learn. If they only want to complete an assignment to get a good grade or pass the class, it could be a bad idea. So I think it depends on the way the student use[s] it.Another student connected this reasoning to the question around continuing to allow access to AIin class: “I feel that if it was not allowed, people would use it without trying to learn from it, itbeing acknowledged encourages people to use it in a learning way.” Engineering educators mayfind this perspective comforting as they make choices about acknowledging or encouraging theuse of AI in their own classrooms.Other students talked about how one’s own

Relevance Score: 0.008731615

The Value and Instructor Perceptions of Learning Analytics for Small Classes

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)

analysis, collecting survey data, and preparing thismanuscript. The author would also like to thank the faculty of the Mechanical EngineeringDepartment at Rowan University for their participation in the survey. The insights gained wouldnot have been possible without their cooperation.References 1. “Startling digital divides in distance learning emerge,” UNESCO, 04-April-2020. [Online]. Available: https://www.unesco.org/en/articles/startling-digital-divides-distance-learning-emerge [Accessed: 03-Feb-2024]. 2. S. Porter, To MOOC or not to MOOC : how can online learning help to build the future of higher education?, 1st edition. Waltham, MA: Chandos Publishing, 2015. 3. H. E. Kentnor, “Distance Education and the Evolution of

Relevance Score: 0.008002657

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

. statistical analysis report. nces 2014-001. National Center for Education Statistics, 2013. [4] S. Chockkalingam, R. Yu, and Z. A. Pardos. Which one’s more work? predicting effective credit hours between courses. In LAK21: 11th International Learning Analytics and Knowledge Conference, LAK21, page 599–605, New York, NY, USA, 2021. Association for Computing Machinery. Retreived from https://doi.org/10.1145/3448139.3448204. [5] CollegeTransfer.Net. What are degree requirements? Retrieved from https://www.collegetransfer.net/AskCT/What-are-Degree-Requirements. [6] Complete College America. Time Is the Enemy: The Surprising Truth about Why Today’s College Students Aren’t Graduating... and What Needs to Change, 2011. Retrieved

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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

options,” Washington, DC, 2018.[7] L. Modenos, “No, nontraditional Is not the new traditional,” Adult Learning, vol. 31, no. 3, pp. 134–136, Jul. 2020, doi: 10.1177/1045159520941082.[8] Fortune, “Best online master’s in data science programs in 2023,” Fortune, 2023.[9] R. S. Malik, “90 MS data science programs in the USA - Complete List,” Medium. Accessed: Jul. 28, 2023. [Online]. Available: https://medium.com/@rijulsinghmalik/90- ms-data-science-programs-in-the-usa-complete-list-4a8ff82443b7[10] T. Heaney, “Adult education for social change: From center stage to the wings and back again,” Information Series No.365, pp.1-29, 1996[11] C. de Brey et. al., “Status and trends in the education of racial and ethnic groups 2018