Student responses to various instructional methods differ due to individual differences. Additionally, these responses are observed in the student’s affect. In human instructor learning environments, the human instructor is able to adapt his teaching method based on observable signals of the student’s affect (e.g., wandering gaze, slumped shoulders, facial expressions, etc.). However, in an intelligent tutoring environment, the system is not able to infer the student’s affect and consequently, the instruction is tailored solely on the student’s performance. There are methods for automatically obtaining objective measures of affect. As such, an affective component has been designed to enhance the student model in an ITS in providing a more comprehensive diagnosis of the student’s performance (e.g., discriminating between lack of knowledge and boredom or frustration). This paper describes an experiment that was conducted in support of the development of the affective component
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.