Teachable Agent Improves Affect Regulation: Evidence from Betty’s Brain

Abstract

Intelligent learning technologies are often applied within the educational industries. While these technologies can be used to create learning experiences tailored to an individual student, they cannot address students’ affect accurately and quickly during the learning process. This paper focuses on two core research questions. How do students regulate affect and what are the processes that affect regulation? First, this paper reviews the affect regulation methods and processes in an intelligent learning environment based on affective transition and affect compensation. This process, along with affect analysis, affect regulation, intelligent agents, and an intervention strategy can be used to analyze specific affect regulation methods and improve the affective regulation system. Seventy-two 7th grade students were randomly placed into an experimental condition that used Betty’s Brain, an intelligent tutoring system (ITS), or a classroom control. A lag sequence analysis and a multinomial processing tree analysis of video data captured at 25-minute intervals revealed significant differences in affect transitions frequencies between the two groups. Based on the results of the above analyses and after-class interviews, we found that Betty’s Brain was able to promote effective affect-regulation strategies to students in the domain of forest ecosystems.