Smart e-Learning as a Student-Centered Biotechnical System

  • Vladimir Uskov
  • Andrey Lyamin
  • Lubov Lisitsyna
  • Bhuvana Sekar
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 138)


The Smart e-Learning System (SeLS) should be designed and developed as a smart student-centered biotechnical system with certain features of smart systems (sensing, transmission, big data processing, activation of actuators) and levels of “smartness” (adaptation, sensing, inferring, learning, anticipation, self-organization). In order to provide higher efficiency of learning process in general, and, SeLS, in particular, SeLS should use multiple parameters of student psychophysiological state.


e-Learning Smart system Student psychophysiological state 


  1. 1.
    Yau, S., Guppta, S., et al.: Smart classroom: enhancing collaborative learning using pervasive computing technology. In: Proceedings of the 2003 ASEE Annual Conference and Exposition, Nashville, TN, 23–25 June 2003Google Scholar
  2. 2.
    O’Driscoll, C., Mithileash, et al.: Deploying a context aware smart classroom. In: International Technology, Education and Development Conference INTED (2008)Google Scholar
  3. 3.
    Yue Suo, Y., Miyata, N., et al.: Open smart classroom: extensible and scalable learning system in smart space using Web service technology. IEEE Trans. Knowl. Data Eng. 21(6), 814–828 (2009)CrossRefGoogle Scholar
  4. 4.
    Shi, Y., Xie, W., et al.: The smart classroom: merging technologies for seamless tele-education. Pervasive Comput. 2(2), 45–55 (2003). IEEEGoogle Scholar
  5. 5.
    Das, S.K., Cook, D.J.: Designing and modeling smart environments. In: World of Wireless. Mobile and Multimedia Networks, WoWMoM (2006)Google Scholar
  6. 6.
    Akhras, G.: Smart materials and smart systems for the future. Can. Mil. J.
  7. 7.
  8. 8.
    Lisitsyna, L., Lyamin, A., Skshidlevsky, A.: Estimation of student functional state in learning management system by heart rate variability method. In: Proceedings of the First International Conference on Smart Technology Based Education and Training, Crete, Greece, June 2014Google Scholar
  9. 9.
    Maraes, V., Carreiro, D., Barbosa, N.: Study of heart rate variability of university trained at rest and exercise. In: 2013 Pan American Health Care Exchanges (PAHCE), Medellin (2013)Google Scholar
  10. 10.
    Karthikeyan, P., Murugappan, M., Yaacob, S.: A study on mental arithmetic task based human stress level classification using discrete wavelet transform. In: Proceedings of the 2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT), Kuala Lumpur, pp. 77–81, October 2012Google Scholar
  11. 11.
    Wu, W., Lee, J.: Improvement of HRV methodology for positive/negative emotion assessment. In: Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing, Washington, DC, 11–14 Nov 2009Google Scholar
  12. 12.
    Uskov, V., Uskova, M.: Reusable learning objects approach to web-based education. In: Proceedings of the 5th International Conference on Computers and Advanced Technology in Education CATE-2002, Cancun, Mexico, 20–22 May, pp. 165–170 (2002)Google Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014

Authors and Affiliations

  • Vladimir Uskov
    • 1
  • Andrey Lyamin
    • 2
  • Lubov Lisitsyna
    • 2
  • Bhuvana Sekar
    • 1
  1. 1.Department of Computer Science and Information SystemsBradley UniversityPeoriaUSA
  2. 2.Department of Computer Educational TechnologyITMO UniversitySaint PetersburgRussia

Personalised recommendations