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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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References

  1. Abedi J, Bruno J (1993) Concurrent validity of the information referenced testing format using MCW-APM scoring methods. J Comput Based Instruction 20(1):21–25

    Google Scholar 

  2. Booch G (2005) The unified modeling language user guide. Pearson Education India

  3. Bruno JE (1993) Using testing to provide feedback to support instruction: a reexamination of the role of assessment in educational organizations. item banking: interactive testing and self-assessment. Springer, Berlin, pp 190–209

    Google Scholar 

  4. Brusilovsky P, Peylo C (2003) Adaptive and web-based education systems. Int J Artif Intell AI 13:159–172

    Google Scholar 

  5. Buthpitiya S, Dey AK, Griss M (2014) CobLE: confidence-based learning ensembles. In: IEEE computational science and computational intelligence (CSCI), international conference, pp 386–391

  6. Chatterjee R, Mukherjee S, Dasgupta R (2011) Design of an LMS for confidence based learning. In: proceedings of INTED2011 conference, Valencia, Spain, ISBN: 978-84-614-7423-3, pp 619–626

  7. Chen T (2015) A fuzzy back-propagation network approach for planning actions to shorten the cycle time of a job in dynamic random access memory manufacturing. Neural Comput Appl 26(8):1813–1825

    Article  Google Scholar 

  8. Chernova S, Veloso M (2007) Confidence-based policy learning from demonstration using gaussian mixture models. In Proceedings of the 6th international joint conference on autonomous agents and multiagent systems

  9. Chernova S, Veloso M (2009) Interactive policy learning through confidence-based autonomy. J Artif Intell Res 34:1–25

    MathSciNet  MATH  Google Scholar 

  10. Erdt M, Fernández A, Rensing C (2015) Evaluating recommender systems for technology enhanced learning: a quantitative survey. IEEE Trans Learn Technol 8:326–344

    Article  Google Scholar 

  11. Gill TG, Holton CF (2006) A self-paced introductory programming course. J Inform Technol Educ 5:95–105

    Article  Google Scholar 

  12. Grozavu N, Ghassany M, Bennani Y (2011). Learning confidence exchange in collaborative clustering. In IEEE, proceedings of international joint conference on neural networks, San Jose, California, USA, 978-1-4244-9637-2/11, pp 872–879

  13. Hunt DP (2003) The concept of knowledge and how to measure it. J Intellect Cap 4(1):100–113

    Article  Google Scholar 

  14. Kennewell S (2001) Using affordances and constraints to evaluate the use of information technology in teaching and learning. J Inform Technol Teach Educ 10:101–116

    Article  Google Scholar 

  15. Koohang A, Durante A (2003) Learners’ perceptions toward the web-based distance learning activities/assignments portion of an undergraduate hybrid instructional model. J Inform Technol Educ 2:105–113

    Article  Google Scholar 

  16. Mancev D, Todorovic B (2012). Confidence based learning of a two-model committee for sequence labeling. IEEE, neural network applications in electrical engineering (NEUREL), 978-1-4673-1569-2, pp 167–170

  17. Moran L, Myringer B (1999) Flexible learning and university change. Higher education through open and distance learning, pp 57–71

  18. Park MG, Yoon KJ (2015) Leveraging stereo matching with learning-based confidence measures. IEEE, computer vision and pattern recognition (CVPR), pp 101–109

  19. Rothwell J, Bandar Z, O’Shea J, McLean D (2007) Charting the behavioural state of a person using a backpropagation neural network. Neural Comput Appl 16(4–5):327–339

    Article  Google Scholar 

  20. Saba L, Dey N, Ashour AS, Samanta S, Nath SS, Chakraborty S, Suri JS (2016) Automated stratification of liver disease in ultrasound: an online accurate feature classification paradigm. Comput Methods Programs Biomed 130:118–134

    Article  Google Scholar 

  21. Tinio VL (2003) ICT in Education. E-Primers for the information economy, society and polity. Manila: E-ASEAN Task Force/UNDP-APDIP

  22. Tondeur J, Braak JV, Valcke M (2007) Curricula and the use of ICT in education: two worlds apart? Brit J Educ Technol 38(6):962–976

    Article  Google Scholar 

  23. Tojo T, Ono O, Noh NM, Yusof R (2015) Intelligent decision supporting tutor in virtual group operation control and management based on e-learning system. IEEE, control conference (ASCC), 10th Asian, pp 1–6

  24. Wang T, Guo L, Li Y (2010) An algorithm of Bayesian Networks parameters learning based on confidence interval. IEEE computer, mechatronics, control and electronic engineering (CMCE), International Conference, 978-1-4244-7957-3, pp 233–235

  25. Wang J, Zhao P, Hoi SC (2012) Exact soft confidence-weighted learning. arXiv preprint; arXiv:1206.4612

  26. Willis JW, Johnson DL, Dixon PN (1983) Computers, teaching and learning: an introduction to computers in education. Dilithium Press, Veaverton

    Google Scholar 

  27. Zhang K, Maeda Y, Takahashi Y (2012) Interactive learning of social agents based on confidence degree. In Fuzzy Systems (FUZZ-IEEE). IEEe international conference, pp 1–4

Download references

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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Authors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Suman Bhattacharya

  2. Department of Computer Science and Engineering, NITTTR, Kolkata, West Bengal, India

    Samir Roy

  3. Department of Computer Science and Engineering, University of Calcutta, Kolkata, West Bengal, India

    Sankhayan Chowdhury

Authors
  1. Suman Bhattacharya
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  2. Samir Roy
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  3. Sankhayan Chowdhury
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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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  • DOI: https://doi.org/10.1007/s00521-016-2430-5

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