Individualised modelling of affective data for intelligent tutoring systems: lessons learned

Abstract

One on one tutoring from human expert tutors to human students is the most effective form of instruction found to date. There are many actions that human tutors perform which make them remarkably effective, including the attention that they pay to the cognitive and affective states of the human students that they tutor, and the use of this knowledge to modify the way that they instruct the material. According to theoretical models, learner state data is used to inform instructional data and decisions, which then influences the learning of the student. Naturally, the data about student state must be available in order to be used to adjust the instruction. Success amongst operational systems, however, has not been observed with generalised modelling techniques. Individualised and adaptive modelling techniques from other domains in the literature present an alternative to the approach which is not observing significant operational success. This work investigates individualised adaptive models, validates the approach, and shows that it can produce models of acceptable quality, but that doing so does not obviate the experimenter from creating quality generalised models prior to individualising.