Big Data Transforms Education Research
October 3, 2017
Martin Nystrand, Professor of Education Emeritus, and collaborators were recently featured in Education Next.
“Machine Learning” to Track Student Learning
Enter the machines. What if we didn’t need to have graduate students crouching in the back of classrooms in order to catalog the play-by-play of classroom instruction? What if, instead, we could capture the action with a video camera or, better yet from a privacy perspective, a microphone? And what if we could gather that information not just for an hour or two, but all day, 180 days a year, in a big national sample of schools? And what if we could then use the magic of machine learning to have a computer figure out what the reams of data all mean?
This possibility is much closer than you might imagine, thanks to a group of professors who are teaching computers to capture and code classroom activities. Sidney D’Mello is an associate professor in the departments of psychology and computer science at the University of Notre Dame. He and collaborators Sean Kelly (University of Pittsburgh), Andrew Olney (University of Memphis), and Martin Nystrand (University of Wisconsin-Madison) are interested in helping teachers learn how to ask better questions, as research has long demonstrated that high-quality questioning can lead to better engagement and higher student achievement. They also want to show teachers examples of good and bad questions. But putting live humans in hundreds of classrooms, watching lessons unfold while coding teachers’ questions and students’ responses, would be prohibitively costly in both time and money.
So D’Mello and his team decided to teach a computer how to do the coding itself. They start by capturing high-quality audio with a noise-canceling wireless headset microphone worn by the teacher. Another mike is propped on the teacher’s desk or blackboard, where it records students’ speech, plus ambient noise of the classroom. They take the audio files and run them through several speech-recognition programs, producing a transcript. Then their algorithm goes to work, looking at both the transcript and the audio files (which have markers for intonation, tempo, and more) to match codes provided by human observers.
