The Joint PhD Program in Computer Science and Learning Sciences builds on enduring and growing connections between research on learning and computation. Rapid technological advances continue to create new and exciting ways to both understand and support learning in all settings and in all stages of life. This program is intended for students with an interest in both fields who would otherwise be forced to choose one area or the other.
Areas of Interest
The possible areas of study are broad and draw from the diverse expertise of affiliated faculty. However, all research must have clear relevance to both Computer Science and Learning Sciences. Example areas of interest include educational data mining; computational modeling as a means to understand complex scientific phenomena; adaptive technology for learning; equity issues in computing; intelligent tutoring systems; and interaction design to support learning.
- Interaction Design
- Computational modeling and simulations
- Artificial Intelligence
- Programming language design
- Machine Learning
- Crowd Sourcing
- Social Computing
- Cognitive Modeling
- Learning Analytics
- Game Design
- Educational Data Mining
- Computer Science Education
- Learning at Scale
- Tangible and Ubiquitous Computing
Learn more about our program by registering for an upcoming virtual information session.
Anne Marie Piper
Joint CS + LS & CS Colloquium
Monday, December 4th, 2:00 PM
Designing for Irrational Curiosity
The learning sciences tend to present science education as a deeply sensible and rational enterprise, in which youngsters acquire scientific knowledge for clear, socially approved, understandable reasons. In this view, the basic obstacles to scientific pursuits are either cognitive (e.g., children must overcome scientific misconceptions) or rooted in classroom socialization (e.g., children must be engaged in the right sorts of conversations in school settings). Educational design, it follows, must be focused upon strengthening scientific cognition and/or improving classroom practice.
Neither the history of science nor the state of science in current American culture is particularly supportive of this traditional view. Scientific pursuit is best understood as a deeply irrational choice––never more so than now. As a consequence, the design of educational technology must take its fundamental goal as supporting the pleasures and easing the discomfort of a type of humane but (to the rest of the world) suspicious madness. This talk will discuss research themes and potential design projects in line with this perspective on science education.
Michael Eisenberg is Professor in the Department of Computer Science and Institute of Cognitive Science at the University of Colorado, Boulder, where he has been on the faculty since 1992. He received a BA in Chemistry from Columbia University in 1978, and a Ph.D. in Electrical Engineering and Computer Science from MIT in 1991. He is the author of a programing textbook (Programming in Scheme, MIT Press) and a published play (Hackers, Samuel French Ltd.), and was co-editor (with Yasmin Kafai, Leah Buechley, and Kylie Peppler) of the book Textile Messages (Peter Lang Publishing). He is a member of the President’s Teaching Scholars at the University of Colorado and has been the recipient of several major teaching awards at the university.
Questions about the program may be directed to the program assistant: