The Kinect is being used in an experiment by Carnegie Mellon to help improve classroom education... How cool is that?
Large lecture classes may go through the content too quickly for the typical student to understand. That's why so many schools follow the practice of breaking the class cohort into smaller sections led by teaching assistants. This more personalized attention gives students a chance to ask questions, get in-depth explanations and practice what they're learning.
While TAs are intended to help students understand the material, their teaching skills vary and they come at the job with widely different backgrounds. So what students get out of their specific sections may not be as useful as they could be.
A project at Carnegie Mellon University promises to change that with the use of sensors in the classroom hooked to software to help TAs — especially those from other countries — refine their teaching skills in STEM courses. Computer-Aided Noticing and Reflection (CANAR) is the project of Amy Ogan, an assistant professor in the Human Computer Interaction Institute, and Ph.D. student David Gerritsen. The work is being supported by a $174,000 National Science Foundation grant.
Gathering Audio Data
The setup is a fairly simple one: The two researchers place a couple of $100 Kinect sensors in the classroom, one on the left side and the other on the right side, in between the teacher and students. Each sensor includes a 1,080-pixel, high-definition camera, a microphone array for capturing sound and motion and infrared technology, and is connected to a laptop computer. Throughout the class session, that computer records activity fed by the sensors.
Right now the focus is on audio data, which is being used in two ways. First, the system signals the TA when he or she is talking too much. That shows up as a big red screen that flashes to the instructor to give a warning, or a green light to signal that all is well.
Then after class,...
Next Up: Facial Recognition
Beginning this summer, the program will experiment with analyzing video data as well. The researchers will take advantage of facial recognition technology created by others in the university's CyLab Biometrics Center, which is currently being tested by social networking sites to help people tag photos. "It's the same sort of principle that applies in the classroom," said Ogan. "It's just a little more difficult because now there are 20 or 30 faces that it's looking for as opposed to three."
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