Building ontology-driven tutoring models for Intelligent Tutoring Systems using data mining

Pedagogical (Tutor or Tutoring) Models are an important element of Intelligent Tutoring Systems (ITS) and they can be described by sets of (tutoring) rules. The implementation of a Tutoring Model includes both the formal representation of the aforementioned rules and a mechanism able to interpret su...

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Published inIEEE access Vol. 8; p. 1
Main Authors Chang, Maiga, DAniello, Giuseppe, Gaeta, Matteo, Orciuoli, Francesco, Samspon, Demetrios, Simonelli, Carmine
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Classification Rule Mining
Data mining
Intelligent Tutoring Systems
Knowledge representation
Ontologies
Ontology
Pedagogical Rules
Pedagogy
Programming languages
Semantic Web
Semantics
Tutoring
Web Ontology Language (OWL)
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Abstract Pedagogical (Tutor or Tutoring) Models are an important element of Intelligent Tutoring Systems (ITS) and they can be described by sets of (tutoring) rules. The implementation of a Tutoring Model includes both the formal representation of the aforementioned rules and a mechanism able to interpret such representation and execute the rules. One of the most suitable approaches to formally represent pedagogical rules is to construct semantic web ontologies that are highly interoperable and can be integrated with other models in an ITS like the subject domain and the student model. However, the main drawback of semantic web-based approaches is that they require a considerable human effort to prepare and build relevant ontologies. This paper proposes a novel approach to maintain the benefits of the semantic web-based approach in representing pedagogical rules for an ITS, while overcoming its main drawback by employing a data mining technique to automatically extract rules from real-world tutoring sessions and represent them by means of Web Ontology Language (OWL).
AbstractList Pedagogical (Tutor or Tutoring) Models are an important element of Intelligent Tutoring Systems (ITS) and they can be described by sets of (tutoring) rules. The implementation of a Tutoring Model includes both the formal representation of the aforementioned rules and a mechanism able to interpret such representation and execute the rules. One of the most suitable approaches to formally represent pedagogical rules is to construct semantic web ontologies that are highly interoperable and can be integrated with other models in an ITS like the subject domain and the student model. However, the main drawback of semantic web-based approaches is that they require a considerable human effort to prepare and build relevant ontologies. This paper proposes a novel approach to maintain the benefits of the semantic web-based approach in representing pedagogical rules for an ITS, while overcoming its main drawback by employing a data mining technique to automatically extract rules from real-world tutoring sessions and represent them by means of Web Ontology Language (OWL).
Author Gaeta, Matteo
Orciuoli, Francesco
Simonelli, Carmine
Samspon, Demetrios
DAniello, Giuseppe
Chang, Maiga
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SubjectTerms Classification Rule Mining
Data mining
Intelligent Tutoring Systems
Knowledge representation
Ontologies
Ontology
Pedagogical Rules
Pedagogy
Programming languages
Semantic Web
Semantics
Tutoring
Web Ontology Language (OWL)
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Title Building ontology-driven tutoring models for Intelligent Tutoring Systems using data mining
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