New software can track facial expressions to assess the emotions of students during interactive online learning activities.
Researchers from North Carolina State University say that the ability to view emotions allows educators to predict the effectiveness of online tutoring sessions.
Researchers automatically tracked facial expressions related to anxiety, confusion, engagement, and frustration.
“This work is part of a larger effort to develop artificial intelligence software to teach students computer science,” said Kristy Boyer, Ph.D., co-author of a paper on the work.
“The program, JavaTutor, will not only respond to what a student knows, but to each student’s feelings of frustration or engagement. This is important because research shows that student emotion plays an important role in the learning process.”
The researchers used the automated Computer Expression Recognition Toolbox (CERT) program to evaluate facial expressions of 65 college students engaged in one-on-one online tutoring sessions.
Investigators found that CERT was able to identify facial movements associated with learning-centered emotions, such as frustration or concentration — and that the automated program’s findings were consistent with expert human assessments more than 85 percent of the time.
The researchers also had the students report how effective they felt the tutorial was, and tested the students before and after each tutoring session to measure how much they learned.
Observational data from CERT, along with student self-assessments and test results, were used to develop models to predict how effective a tutorial session was, based on what the facial expressions of the students indicated about each student’s feelings of frustration or engagement.
“This work feeds directly into the next stage of JavaTutor system development, which will enable the program to provide cognitive and emotion-based feedback to students,” said Joseph Grafsgaard, a Ph.D. student at NC State and lead author of the paper.
Source: North Carolina State University