A Concept of a New Knowledge Based Multimodal Affective Tutoring System

In this paper, we present a new concept of a multimodal Affective Tutoring System (mATS) based on knowledge, which is aimed at covering the entire functionalities of a human teacher - namely training and testing, and personal attitude towards every student. The system incorporates models of the student and of the task, as well as evaluation module for constant determination of the current states of both. Moreover, based on multimodal input signals and system-generated ones (performance, response time, etc.), supplemented with prior knowledge about the student (self-reported parameters, such as age, gender, level of experience, etc.) and user profiling approach mATS gives a variety of options for realization of adequate adaptive strategies.