Adaptive intelligent tutoring systems for e-learning systems

Abstract An e-learning system is increasingly gaining popularity in the academic community because of several benefits of learning anywhere anyplace and anytime. Most frequently it seems to be used for web-based instruction so that learners can access online courses via internet. One likely reason for the lack of success is that just placing lecture notes on the internet does not train. This situation can be improved through the use of training software such as Intelligent Tutoring Systems (ITS). ITS incorporate built-in expert systems in order to monitor the performance of a learner and to personalize instruction on the basis of adaptation to learners’ learning style, current knowledge level, and appropriate teaching strategies in e-Learning systems. While Adaptive Hypermedia systems (AH) do provide instruction in skills, it is generally less advanced than comparable ITS instruction. E-learning systems are web-based learning so that learners can access online courses via Internet without adaptation based on Learners’ behavior. Therefore, it is a challenge to make e-Learning systems to be more “adaptive”. Both ITS and AH are normally used for computer-based instruction. However, adaptive hypermedia is better suited for the instruction of concepts whereas intelligent tutoring system generally assists in the use of these concepts to solve problems. Therefore, a general instruction system requires both of these instructional approaches in order to provide a full learning environment. In this paper, describes a conceptual for combining ITS and AH into Adaptive Intelligent Tutoring System (AITS) for e-learning systems that allows knowledge to be stored in such a way that is not only independent of the knowledge domain, but also supports the storage of transfer knowledge relationships and prerequisite knowledge relationships. The conceptual results show that this innovative approach is helpful to the learners in improving their learning achievements.