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An Adaptive Framework of Learner Model Using Learner Characteristics for Intelligent Tutoring Systems

  • Amit KumarEmail author
  • Neelu Jyoti Ahuja
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 989)

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.

Keywords

Learner model Intelligent tutoring system (ITS) Adaptivity e-learning Learning style 

Notes

Acknowledgements

This work is being carried out at University of Petroleum and Energy Studies (UPES) with the reference number SR/CSI/140/2013. The authors thankfully acknowledge the funding support received from Cognitive Science Research Initiative, Department of Science and Technology for the project. The Authors thank the management of UPES, for supporting the work and granting permission to publish it.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of Petroleum and Energy StudiesDehradunIndia
  2. 2.University of Petroleum and Energy StudiesDehradunIndia

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