Test day approaching? Get your game face on. A study of a computer program that recognizes and interprets facial expressions has found that identifying students' level of engagement while learning may predict their performance in the class. Computer scientists at the University of California, San Diego and Emotient, a San Diego-based company that developed the facial-recognition software used in the study, teamed with psychologists at Virginia Commonwealth and Virginia State universities to look at "when and why students get disengaged," study lead author Jacob Whitehill, PhD, researcher in UC San Diego’s Qualcomm Institute and Emotient co-founder, said in a release.
The authors write in the study, which was published in an early online version in the journal IEEE Transactions on Affective Computing:
In this paper we explore approaches for automatic recognition of engagement from students’ facial expressions. We studied whether human observers can reliably judge engagement from the face; analyzed the signals observers use to make these judgments; and automated the process using machine learning.
“Automatic engagement detection provides an opportunity for educators to adjust their curriculum for higher impact, either in real time or in subsequent lessons," Whitehill said in the release. "Automatic engagement detection could be a valuable asset for developing adaptive educational games, improving intelligent tutoring systems and tailoring massive open online courses, or MOOCs.”
Previously: Looks of fear and disgust help us to see threats, study shows, Providing medical, educational and technological tools in Zimbabwe and Whiz Kids: Teaching anatomy with augmented reality
Photo by Jesús Gorriti
