2015_11_Modelling a learner's affective state in real time to improve intelligent tutoring effectiveness
This paper introduces, describes, and evaluates real-time models of affective states of individual learners interacting with Intelligent Tutoring Systems. Computer-based instructors, like human instructors, should use affective information for adapting instruction. This requires an accurate representation of individual learner state during tutoring; however, models described in the literature are generalised and constructed offline. Such total population models have faced validation difficulty with individuals, while individualised models have had difficulties with offline creation and online use. The simultaneous creation and utilisation of an individualised model from sensor-based physiological measurements presents an attractive alternative. We present and evaluate approaches for building affective models during the tutoring session which address the difficulties present in real-time data streams. Additionally, this work examines the impact of occasional direct user query on model quality. The results indicate that individualised real-time model construction is comparable to offline equivalents, yet can be successfully applied in tutoring settings.