Learner Style Modeling and Its Application to A Case-Based Tutoring System

The Intelligent Tutoring Systems have been proven to be crucial due to their fast and cost-effective performance. One of the main theories in pedagogical environment is that, different learners have different learning styles. Within this respect, regarding the individualized instruction, it is important for the tutoring system to adapt its courseware arrangement format based on individual learning style. Here, the tutor can use its past experiences to realize which approach will be effective for the learner. However, no past situation is ever exactly the same as a new one, and domain knowledge for tutoring strategies is oftentimes incomplete. In this paper, we propose an intelligent tutoring system, which uses the parameters of learner model for personalizing the essential courseware in a certain field. In our work, pattern analysis & understanding has been selected as a platform for both implementing our approach, since it can be applied equally to a wide range of engineering branches, and can, at the same time, be used as a systemic discipline for non-engineering areas as well. The learner style model that has been used in our system is based on Dunn and Dunn, Kolb, and Myers-Briggs theories. Having the learner models of different users, together with the suitable arrangements of the essential courseware, we will show how a case-based reasoning approach based on a process of case adaptation can yield producing a novel courseware arrangement. A multi-agent environment has been proposed for our system, within which a number of asynchronous and non-homogeneous agents are collaborated to achieve tutoring tasks in an effective manner.

کلیدواژه ها:

Intelligent tutoring system ، user model ، learner style ، case-based reasoning ، domain knowledge ontology