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NEWS
Predicting Test Performance
May 1, 2014

The University of California, San Diego and Emotient, the leading provider of facial expression recognition data and analysis, announced publication of a joint study by two Emotient co-founders affiliated with UC San Diego, together with researchers from Virginia Commonwealth University and Virginia State University. The study demonstrates that a real-time engagement detection technology that processes facial expressions can perform with accuracy comparable to that of human observers. The study also revealed that engagement levels were a better predictor of students’ post-test performance than the students’ pre-test scores.

The online version of the study’s paper, “The Faces of Engagement: Automatic Recognition of Student Engagement,” appeared recently in the journal, “IEEE Transactions on Affective Computing”.

“Automatic recognition of student engagement could revolutionize education by increasing understanding of when and why students get disengaged,” said Dr. Jacob Whitehill, Machine Perception Lab researcher in UC San Diego’s Qualcomm Institute and Emotient co-founder. “Automatic engagement detection provides an opportunity for educators to adjust their curriculum for higher impact, either in real time or in subsequent lessons. 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.”

The study consisted of training an automatic detector, which measures how engaged a student appears in a webcam video while undergoing cognitive skills training on an iPad®. The researchers used automatic expression recognition technology to analyze students’ facial expressions on a frame-by-frame basis and estimate their engagement level.

This article reprinted from materials provided by University of California, San Diego.


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