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).
Highlights
As defined in [1], an Intelligent Tutoring System (ITS) [2], [3] is a software system providing adaptive educational experiences
An ITS can be divided into five core conceptual components [14]: the Expert Model representing the domain knowledge; the Domain Model containing the knowledge about the actual teaching material; the Student Model storing learners’ profile like, for instance, details about the learner’s current problem-solving state and long-term knowledge progress, which are essential for adapting the experience; the Pedagogical (Tutor or Tutoring) Model providing the knowledge to tailor the selection and the provisioning of the teaching elements according to the student model; the Communication (User Interface) Model enabling the interactions between learner and system
This paper presents a novel approach to build the pedagogical model of an ITS represented with the W3C Web Ontology Language (OWL) and a classification rule mining algorithm over a dataset containing the data observed during real-world tutoring sessions
Summary
As defined in [1], an Intelligent Tutoring System (ITS) [2], [3] is a software system providing adaptive educational experiences. The main advantage of the techniques of the second class is that it is possible to automatically build ITS models, to a large extent, based on processing existing data from real life scenarios using machine learning algorithms. The drawback of the second class is that the resulting models are typically represented as a black box, so they cannot be directly processed by humans [27]–[29] In this context, an approach that combines the benefits of both techniques while overcoming their drawbacks is highly desirable. This paper presents a novel approach to build the pedagogical model of an ITS represented with the W3C Web Ontology Language (OWL) and a classification rule mining algorithm over a dataset containing the data observed during real-world tutoring sessions. The proposed approach provides an approach to automatically build an ontology-driven ITS from real data, maintaining the benefits of both the first and the second classes of techniques.
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