Publication Date
In 2025 | 2 |
Since 2024 | 4 |
Since 2021 (last 5 years) | 6 |
Since 2016 (last 10 years) | 6 |
Since 2006 (last 20 years) | 6 |
Descriptor
Data Science | 6 |
Engineering Education | 6 |
Ethics | 2 |
Undergraduate Students | 2 |
Algorithms | 1 |
Anthropology | 1 |
Architectural Education | 1 |
Artificial Intelligence | 1 |
Bias | 1 |
Chemical Engineering | 1 |
Cognitive Psychology | 1 |
More ▼ |
Source
IEEE Transactions on Education | 2 |
Chemical Engineering Education | 1 |
Education and Information… | 1 |
Journal of Civil Engineering… | 1 |
Journal of Statistics and… | 1 |
Author
Abel, Michael | 1 |
Avital Binah-Pollak | 1 |
Beck, David C. | 1 |
Caitlin Snyder | 1 |
Castillo, Ivan | 1 |
Chiang, Leo H. | 1 |
Christopher P. Vanags | 1 |
Emily C. Kern | 1 |
Erin C. Henrick | 1 |
Erin R. Hotchkiss | 1 |
Gautam Biswas | 1 |
More ▼ |
Publication Type
Journal Articles | 6 |
Reports - Research | 5 |
Reports - Evaluative | 1 |
Education Level
Higher Education | 4 |
Postsecondary Education | 4 |
Audience
Location
Canada | 1 |
Oregon | 1 |
Philippines | 1 |
Turkey | 1 |
Washington | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Avital Binah-Pollak; Orit Hazzan; Koby Mike; Ronit Lis Hacohen – Education and Information Technologies, 2024
The significance of ethics in data science research has attracted considerable attention in recent years. While there is widespread agreement on the importance of teaching ethics within computing contexts, there is no clear method for its implementation and assessment. Studies focusing on methods for integrating ethics into data science courses…
Descriptors: Data Science, Anthropology, Ethics, Context Effect
Rebecca Napolitano; Ryan Solnosky; Wesley Reinhart – Journal of Civil Engineering Education, 2025
This study examines the impact of changes in exam modalities on the performance and experiences of architectural engineering students in a domain-specific data science class. Specifically, the number and duration of exams (and thereby the amount of content on each) and setting in which the students took the exams in changed among the three years…
Descriptors: Data Science, Engineering Education, Architectural Education, Student Evaluation
Md. Yunus Naseri; Caitlin Snyder; Katherine X. Perez-Rivera; Sambridhi Bhandari; Habtamu Alemu Workneh; Niroj Aryal; Gautam Biswas; Erin C. Henrick; Erin R. Hotchkiss; Manoj K. Jha; Steven Jiang; Emily C. Kern; Vinod K. Lohani; Landon T. Marston; Christopher P. Vanags; Kang Xia – IEEE Transactions on Education, 2025
Contribution: This article discusses a research-practice partnership (RPP) where instructors from six undergraduate courses in three universities developed data science modules tailored to the needs of their respective disciplines, academic levels, and pedagogies. Background: STEM disciplines at universities are incorporating data science topics…
Descriptors: Data Science, Courses, Research and Development, Theory Practice Relationship
Komp, Evan A.; Pelkie, Brenden; Janulaitis, Nida; Abel, Michael; Castillo, Ivan; Chiang, Leo H.; Peng, You; Beck, David C.; Valleau, Stéphanie – Chemical Engineering Education, 2023
We present a two-week active learning chemical engineering hackathon event specifically designed to teach undergraduate chemical engineering students of any skill level data science through Python and directly apply this knowledge to a real problem provided by industry. The event is free and optional to the students. We use self-evaluation surveys…
Descriptors: Data Science, Undergraduate Students, Learning Activities, Chemical Engineering
Zachary del Rosario – Journal of Statistics and Data Science Education, 2024
Variability is underemphasized in domains such as engineering. Statistics and data science education research offers a variety of frameworks for understanding variability, but new frameworks for domain applications are necessary. This study investigated the professional practices of working engineers to develop such a framework. The Neglected,…
Descriptors: Foreign Countries, Engineering Education, Engineering, Technical Occupations
Mike, Koby; Hazzan, Orit – IEEE Transactions on Education, 2023
Contribution: This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect…
Descriptors: Engineering Education, Computer Science Education, Data Science, Introductory Courses