Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining

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). INDEX TERMS Classification rule mining, intelligent tutoring systems, ontologies, pedagogical rules, semantic web, web ontology language (OWL).

Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view

“…Therefore, the related computer science studies were focused on specialized computer science skills and the incorporation of educational or psychological frameworks. For example, Chang et al ( 2020 ) proposed methods to maintain the benefits of semantic web-based approaches when representing ITS pedagogical rules, and Liu et al ( 2018 ) incorporated Cognitive Diagnosis models, which were an evaluation system based on cognitive psychology, statistics, and computer science, into an ITS to model to analyze student answer data, which introduced a correlation between test questions and knowledge structures to diagnose student cognitive states and quantitatively investigate student differences and cognitive levels.…”

“…ITSs have also been found to be advantageous in tracking psychological attributes, such as emotional/cognitive recognition, and in developing diagnostic information models (Chang et al, 2020 ; Conati & Maclaren, 2009 ; Craig et al, 2004 ) and computer perception devices that automatically monitor student emotions while they are learning (Aleven et al, 2006 ; Graesser & d'mello, 2012 ).…”

Improving the portability of predicting students’ performance models by using ontologies

“…In Castro and Alonso ( 2011 ) they propose a general architecture for EDM in which there is an educational ontology, but they do not define or develop the ontology, only providing a general statement of it as a part of a higher-level architecture. There are even some works such as the one presented in Chang et al ( 2020 ) where they utilize data mining techniques (association in this case) to build ontology-driven tutoring models for intelligent tutoring systems (this is precisely the opposite process to ours since we use the ontology for a further data mining analysis).…”

Teachers’ Ontological Perspectives of Computational Thinking and Assessment: A Text Mining Approach

“…This allows openness in investigating teachers' insights without potential influence from researchers' preconceptions. Lastly, data/text-mining is an appropriate method for addressing ontological questions (Chang et al, 2020). Figure 1 displays a flowchart that explains the text mining procedures of this study.…”