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A neural network-based intelligent cognitive state recognizer for confidence-based e-learning system

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Abstract

This paper presents an intelligent recognizer of the cognitive state of an e-learner as an integral part of confidence-based e-learning (CBeL) system. It addresses the problem of providing technology-driven pedagogical support to an e-Learner to achieve the desired cognitive state of mastery which is endowed by high levels of both knowledge and confidence. As per best of our knowledge concerned, no prior work has been done in the area of CBeL. The issue is crucial in the present scenario of teaching–learning in the twenty-first century where lifelong learning is gaining increasing importance vis-à-vis traditional classroom teaching–learning. However, self-learning is an indispensable mode of lifelong learning. It is felt that e-learning systems should have the capacity to simulate the behavior a human expert to identify the gap between the learners’ cognitive state and the learning objective with the intension of guiding the self-learner take initiative to bridge the gap with appropriate action and eventually achieve his learning objective. An artificial neural network-based intelligent recognizer has been designed to identify the CBeL state of the learner on the basis of his performance in a CBeL test. This recognizer is the major agent that facilitates the implementation of the proposed CBeL system. Extensive experimentation has been carried out to ensure the performance of the recognizer. Results show ample evidence that the ANN-based intelligent recognizer is able to faithfully simulate the behavior of a human evaluator.

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Acknowledgments

The authors wish to express their gratitude to the anonymous reviewers whose comments and observations helped us to improve the quality of the paper to a great extent.

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Correspondence to Suman Bhattacharya.

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Bhattacharya, S., Roy, S. & Chowdhury, S. A neural network-based intelligent cognitive state recognizer for confidence-based e-learning system. Neural Comput & Applic 29, 205–219 (2018). https://doi.org/10.1007/s00521-016-2430-5

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Keywords

  • CBeL
  • Intelligent recognizer
  • e-learning
  • Artificial neural network
  • Multilayer perceptron feed-forward network
  • ANN