Intelligent Tutoring System Using Hybrid Expert System With Speech Model in Neural Networks

Many of the intelligent tutoring systems that have been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students' attention and interest for hours. This paper aims to establish some characteristics, properties and functions that an ITS should provide combined with speech, and the possible contributions that the different fields of research can make, proposing a multi-domain and multidisciplinary framework to address the research in this field. The framework incorporates a knowledge base where data and knowledge related to the problem are maintained and a model base related to student, teaching and environmental issues together with pedagogical perspectives. A theme underlying much of ITS research is domain independence, i.e. the degree to which knowledge encoded in the teaching model can be reused in different domains. Although to the external observer, domain independence seems like an essential characteristic of intelligence, many experts believe that some of the essential pedagogical knowledge in every domain is fundamentally domain-dependent. The proposed work was used for implementing ITS using supervised learning neural networks to a successful rate. Instead of being mere information-delivery systems, our systems help the students to actively construct knowledge. Index Terms—Domain Independence, Intelligent Tutoring utoring Systems(ITS), Neural Networks , Supervised Learning.