How can we integrate ethics into technical courses in a meaningful and productive way? How can we help computer science students write code that is more readable to other humans? What do these questions have to do with each other? I will present key principles of research on learning, and discuss how they relate to typical human factors research.
I will present structure choices that I found to be common in student code, both in course homework assignments and on my research assessments. These patterns affect a program’s readability and maintainability, and, in some cases (but not others), may also indicate shallow understanding of computing concepts.
I will also present a second line of work on ethics. Ethics problems with tech are easy to find on the front pages of newspapers, resulting in calls to integrate ethics across the computer science curriculum. But the topics in undergrad courses are far removed from the issues in the headlines. Even in artificial intelligence, students are learning about A* search, not about making self-driving cars safer. I will present an instructional design approach that integrates ethics with this type of low-level technical content, show how the inclusion of ethics can deepen technical learning.
This seminar is co-organized with UW LCI.
| 11:45am - 12:15pm: | Food and community socializing. |
| 12:15pm - 1:15pm: | Presentation with Q&A. Available hybrid via Zoom. |
| 1:30pm - 2:15pm: | Student meeting with speaker, held in HUB 332. Students will walk to this from the seminar. |
Eliane Wiese is an Assistant Professor in the School of Computing at the University of Utah. She was a Postdoctoral Scholar in the Graduate School of Education at UC Berkeley, advised by Dr. Marcia Linn. Dr. Wiese earned her Ph.D. from Carnegie Mellon’s Human-Computer Interaction Institute, where she was advised by Dr. Ken Koedinger and awarded an Institute of Education Sciences fellowship. As an undergrad at Columbia, she combined a major in computer science and teacher training. Dr. Wiese uses approaches from human-computer interaction and educational psychology to design new ways to support students in learning computer science.