Modeling Involuntary Dynamic Behaviors to Support Intelligent Tutoring

Abstract

Problem solving is one of the most important 21st century skills. However, effectively coaching young students for problem solving is challenging because teachers must continuously monitor their cognitive and affective states, and make real-time pedagogical interventions to maximise their learning outcomes. It is an even more challenging task in social environments with limited human coaching resources. To lessen the cognitive load on a teacher and enable affect-sensitive intelligent tutoring, many researchers have investigated automated cognitive & affective detection methods. However, most of the studies use culturally-sensitive indices of affect that are prone to social editing such as facial expressions, and only few studies have explored involuntary dynamic behavioral signals such as gross body movements. In addition, most current methods rely on expensive labelled data from trained annotators for supervised learning. In this paper, we explore a semi-supervised learning framework that can learn low-dimensional representations of involuntary dynamic behavioral signals (mainly gross-body movements) from a modest number of short time series segments. Experiments on a real-world data set reveal a significant advantage of these deep features in discriminating cognitive disequilibrium and flow, as compared with traditional complexity measures originating from dynamical systems literature and demonstrate its potential in transferring learned model to unseen subjects.