Affective Tutoring System for Better Learning

Affective Tutoring System for Better Learning

Abdolhossein Sarrafzadeh (Massey University, New Zealand), Samuel T.V. Alexander (Massey University, New Zealand) and Jamshid Shanbehzadeh (Tarbiat Moalem University, Iran)
DOI: 10.4018/978-1-60960-481-3.ch009
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Intelligent tutoring systems (ITS) are still not as effective as one-on-one human tutoring. The next generation of intelligent tutors are expected to be able to take into account the emotional state of students. This paper presents research on the development of an Affective Tutoring System (ATS). The system called “Easy with Eve” adapts to students via a lifelike animated agent who is able to detect student emotion through facial expression analysis, and can display emotion herself. Eve’s adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents an analysis of facial expressions of students engaged in learning with human tutors and how a facial expression recognition system, a life like agent and a case based system based on this analysis have been integrated to develop an ATS for mathematics.
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The overall aim of this research is to develop an ATS that is capable of recognising both the cognitive and affective state of students, and of usefully adapting to this information. The aim of the ATS is summarised in Figure 1 in a model adapted from Conati (2002): the left hand side of the diagram at time ti represents the cognitive and affective state of the student immediately following a student action, where the affective state is identified by detecting the student’s facial expression. At time ti + 1 the system’s animated agent then responds to the cognitive and affective state of the student by adapting both its tutoring and its own facial expression, with the intention of mapping the student’s cognitive and affective states to a particular desired state.

Figure 1.

Model of the ATS

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