An adaptive framework of learner model using learner characteristics for intelligent tutoring systems

Abstract

Learner Model is the base for providing the adaptivity in the Intelligent Tutoring Systems (ITS) as learner performance data and information are stored in the learner model. Recently, there has been a rapid progress in education delivery through web due to the advancement in internet technology. With the limitations such as lack of adaptive support and presentations reasoned that research has been expanded in the domain of ITS. The aim of the paper is to present an adaptive framework of the learner model using learner characteristics that helps to provide the adaptive presentation and feedback to the prospective learner. Adaptive learner model has three component such as Learner Characteristics Model, Learner Classification Model, and Learner Adaptation Model. Learner Characteristics Model includes the characteristics of learner such as learning style, knowledge levels, and cognitive and meta-cognitive skills. Learner Classification Model classifies the learner into groups based on his/her learning style, levels of knowledge, and performance data and implemented through artificial intelligence technique. The Learner Adaptation Model recommends the tutoring strategy which best suits the learner to provide the adaptation.