Modeling Self-Efficacy Across Age Groups with Automatically Tracked Facial Expression

Affect plays a central role in learning. Students’ facial expressions are key indicators of affective states and recent work has increasingly used automated facial expression tracking technologies as a method of affect detection. However, there has not been an investigation of facial expressions compared across age groups. The present study collected facial expressions of college and middle school students in the CRYSTAL ISLAND game-based learning environment. Facial expressions were tracked using the Computer Expression Recognition Toolbox and models of self-efficacy for each age group highlighted differences in facial expressions. Age-specific findings such as these will inform the development of enriched affect models for broadening populations of learners using affect-sensitive learning environments.

• Type: Journal
• Year: 2015
• Where: AIED
• Authors: Joseph F. Grafsgaard, Seung Y. Lee, Bradford W. Mott, Kristy Elizabeth Boyer, James C. Lester
• Keywords: AIED 2015, Artificial Intelligence