학술논문

Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining
Document Type
Periodical
Source
IEEE Access Access, IEEE. 8:48151-48162 2020
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
OWL
Data mining
Ontologies
Task analysis
Adaptation models
Data models
Solid modeling
Classification rule mining
intelligent tutoring systems
ontologies
pedagogical rules
semantic web
web ontology language (OWL)
Language
ISSN
2169-3536
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).