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Displaying all 5 results

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
Paper ID #42783Application of Data Analysis and Visualization Tools for U.S. Renewable SolarEnergy Generation, Its Sustainability Benefits, and Teaching In EngineeringCurriculumMr. Ben D Radhakrishnan, National University Ben D Radhakrishnan is a Professor of Practice, currently a full time Faculty in the Department of Engineering, School of Technology and Engineering, National University, San Diego, California, USA. He is the Academic Program Director for MS Engineering Management program. He develops and teaches Engineering courses in different programs including engineering and business management schools. His research
Relevance Score: 0.5406196

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: 0.015189884

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)
measure in this study.Table 6. The number of the participants’ responses based on the type of courses they teach % of I am not # of participants Course Type No Yes Maybe sure participants First-year engineering 30 course 8 12 20 Capstone 37 course 14 11 25 Laboratory 34
Relevance Score: 0.015082663

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)
,” Asia-Pac. Educ. Res., vol. 30, no. 5, pp. 375–394, Oct. 2021, doi: 10.1007/s40299-020-00525-x.[10] T. Gok and O. Gok, “Peer Instruction in chemistry education: Assessment of students’ learning strategies,” Learn. Strateg., vol. 17, no. 1, 2016.[11] M. F. Golde, C. L. McCreary, and R. Koeske, “Peer Instruction in the general chemistry laboratory: Assessment of student learning,” J. Chem. Educ., vol. 83, no. 5, p. 804, May 2006, doi: 10.1021/ed083p804.[12] N. Lasry, E. Mazur, and J. Watkins, “Peer Instruction: From Harvard to the two-year college,” Am. J. Phys., vol. 76, no. 11, pp. 1066–1069, Nov. 2008, doi: 10.1119/1.2978182.[13] J. Schell and E. Mazur, “Flipping the chemistry classroom with Peer
Relevance Score: 0.008005956

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)
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
Relevance Score: 0.005904086