A university team is analyzing how data from voice and facial recognition technology could help instructors incorporate active learning.
A Carnegie Mellon University (CMU) assistant professor is using voice recognition technology that analyzes talk patterns to better inform instructors about what’s happening in their classrooms.
The voice recognition technology used in the classrooms will help teachers gain insights about what students are learning and if they are collaborating and analyzing concepts, said Amy Ogan, assistant professor of human-computer interaction in CMU’s School of Computer Science.
Right now, the technology is focused on the sounds that occur in a classroom. It detects who is talking, when, where, and for how long–all the features of talk data, Ogan said.
In giving this technology to instructors, the goal is “to give faculty an understanding of who is speaking, where, when, and how, so they can incorporate more active learning into the classroom,” Ogan said.
Teachers receive a dashboard that displays data about classroom activity, which helps determine how their actions are impacting student outcomes. The dashboard displays different lights, such as red and green lights, that correspond to how teachers might want to change or continue their teaching approach.