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- W163239295 abstract "If we consider S is a session of a class then there are i different representation for performing of S. But unfortunately teacher can select and represent one of them according to the background knowledge of students and learning specifications. If we consider selected representation in a class with M members as Ps then we can say that Ps is only suitable for N members of students of that class. Therefore training effectiveness of Ps is equal to N/M. we know that variety of student increase in the Distance Education by E-learning and selecting one representation for all students decrease training effectiveness. Fortunately in E-learning computer equipments provide multi-representing and we can perform a suitable representation on each student's computer.In this article, my research is focused on investigating a Self-tuning Multi-Representing E-learning System (SMES). In this system we can store different representation for items of a class session. SMES has a Decision Maker Intelligent Agent (DMIA) for selecting suitable representation of a class session for each student. DMIA has a knowledge base that obtained from experienced teacher's knowledge. DMIA selects the suitable representation by use of the student's profile and its knowledge. SMES has a Knowledge discovery from Database (KDD) process. DMIA and KDD discover knowledge about the student by use of analysis the student's behavior during the representation and they can dynamically change the representation for better result also KDD is able to discover and add new regulations and knowledge to its knowledge base. Therefore SMES can tune itself for better decision making and increase training effectiveness in future selection." @default.
- W163239295 created "2016-06-24" @default.
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- W163239295 date "2005-02-13" @default.
- W163239295 modified "2023-10-17" @default.
- W163239295 title "Self-tuning multi-representing e-learning system (SMES) by decision maker intelligent agent (DMIA) for increasing training effectiveness" @default.
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