Architecture of an intelligent tutor system for personalized recommendation of learning objects considering the theory of structured knowledge maps
Keywords:Intelligent Tutoring Systems; Affective Computing; Adaptive Course Planning; Informatics in education.
This work proposes an architecture of an Intelligent Tutor System based on the Theory of Structured Knowledge to customize the learning objects offered to the student. For this, it intends that the teacher's interface be inserted in the system's architecture. The adaptive sequencing of the course is carried out through the theory of Structured Knowledge Maps, in which the teacher will be responsible for specifying the concepts and minimum knowledge needed to understand each item in the curriculum. In the student interface, in order to avoid cognitive overload, the system will map the prerequisite doubts of concepts and knowledge and then present the learning objects, in different formats, according to the needs of student learning. Thus, through the information contained in the Student Model and the Domain Model, upon detecting that the student is in an unproductive learning cycle, as well as a need for learning, the Pedagogical Module will execute personalized instructions based on prior knowledge and affective profile of the student.
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Copyright (c) 2021 Sara Luzia de Melo; Régis Michel dos Santos Sousa; Luciano Vieira Lima
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