Tutoring Process in Emotionally Intelligent Tutoring Systems

Tutoring Process in Emotionally Intelligent Tutoring Systems

Sintija Petrovica (Faculty of Computer Science and Information Technology, Riga Technical University, Riga, Latvia)
Copyright: © 2014 |Pages: 14
DOI: 10.4018/ijtem.2014010106
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Abstract

Research has shown that emotions can influence learning in situations when students have to analyze, reason, make conclusions, apply acquired knowledge, answer questions, solve tasks, and provide explanations. A number of research groups inspired by the close relationship between emotions and learning have been working to develop emotionally intelligent tutoring systems. Despite the research carried out so far, a problem how to adapt tutoring not only to a student's knowledge state but also to his/her emotional state has been disregarded. The paper aims to examine to what extent the tutoring process and tutoring strategies are adapted to students' emotional and knowledge states in these systems. It also presents a study on how to influence student's emotions looking from the pedagogical point of view and provides general guidelines for selection of tutoring strategies to influence and regulate student's emotions.
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Intelligent Tutoring Systems

ITSs are adaptive computer systems, which are based on the theory of learning and cognition. ITS-based learning process is very similar to the process when a student and a tutor interact in a one-to-one situation, therefore, an effective intelligent tutoring should simulate what good human-tutors do when implementing individualized instruction. The key feature of ITSs is their ability to adapt presentation of teaching material to a particular student by using methods of artificial intelligence (AI) to make pedagogical decisions and to represent information about each student.

Such systems allow implementation of a more natural learning process by adapting a learning environment (content, feedback, navigation, etc.) to characteristics of a particular student. Adaptation is possible because of a student diagnosis module that collects and processes information about the student (his/her learning progress, problem solving behavior, psychological characteristics, learning style, etc.) and of a student model that stores this information. Additionally to the mentioned components, the student diagnosis module and the student model, the ITS architecture includes (Anohina & Intenberga, 2008):

  • A pedagogical module that is responsible for implementation of the tutoring process and a pedagogical model storing tutoring model and strategies;

  • An expert module or problem domain module that is able to generate and solve problems in the problem domain and an expert model storing knowledge what must be taught to the student;

  • A communication module managing interaction among the system and the student through different devices.

The ITS architecture and interaction between system's components is represented in Figure 1.

Figure 1.

Traditional architecture of intelligent tutoring systems

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