Affect Recognition in Learning Scenarios: Matching Facial- and BCI-Based Values

Abstract

The ability of a learning system to infer a student's affects has become highly relevant to be able to adjust its pedagogical strategies. Several methods have been used to infer affects. One of the most recognized for its reliability is face-based affect recognition. Another emerging one involves the use of brain-computer interfaces. In this paper we compare those strategies and explore if, to a great extent, it is possible to infer the values of one source from the other source.