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An Adaptive E-Learning System Using Justification Based Truth Maintenance System
TL;DR: In this article, an adaptive E learning system which is providing adaptability with support of justification based truth maintenance system is proposed, the system is accomplished of signifying students with suitable knowledge fillings and customized learning paths based on the students profile interests and previous results.
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Abstract: In most E learning systems educational activities are presented in a static way without bearing in mind the particulars or student levels and skills. Personalization and adaptation of an E learning management system are dependent on the flexibility of the system in providing different learning and content models to individual students based on their characteristics. In this paper we suggest an Adaptive E learning system which is providing adaptability with support of justification based truth maintenance system. The system is accomplished of signifying students with suitable knowledge fillings and customized learning paths based on the students profile interests and previous results. The validation of proposed framework is performed by meta model.
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References
Data mining for providing a personalized learning path in creativity: An application of decision trees
TL;DR: The experimental results show that, when the learning path suggested by a hybrid decision tree is employed, the learners have a 90% probability of obtaining an above-average creativity score, which suggests that the employed data mining technique can be a good vehicle for providing adaptive learning that is related to creativity.
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