Boosting Learning: Non-intrusive Monitoring of Student’s Efficiency

  • Sérgio Gonçalves
  • Manuel Rodrigues
  • Davide Carneiro
  • Florentino Fdez-Riverola
  • Paulo Novais
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 374)

Abstract

Keeping students interested and motivated is perhaps one of the most difficult and traditional tasks assigned to teachers. With technology being engaged increasingly into learning activities, with its advantages and disadvantages, some new aspects need to be considered. Undoubtedly, technology acts as an enhancer for learning, opening new paths for teaching. However there are some drawbacks too. Keeping students in the right track, doing what they are expected to do, with commitment and motivation, becomes an enormous challenge when an amazing digital world full of all kind of temptations is at the distance of their personal smartphones or even in the computer they use to study. This excess of stimuli and the process of switching and choosing between them has as potential effects on attention, stress and mental fatigue. Stressed or fatigued students fail to deliver the required performance for the task they are engaged in. This paper presents a non-intrusive approach for monitoring student’s performance in real time and measure the effect of these external variables on students. The long-term goal is to empower teachers with valuable information about the students’ state, allowing them to better manage their students and teaching methodologies.

Keywords

e-learning Fatigue Stress Recommendation System Monitoring 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Palmer, S., Cooper, C., Thomas, K.: Creating a Balance: Managing Stress. British Library, London (2003)Google Scholar
  2. 2.
    Rodrigues, M., Fdez-Riverola, F., Novais, P.: Moodle and Affective Computing -Knowing Who’s on the Other Side. In: ECEL-2011 - 10th European Conference on Elearning, pp. 678–685 (2011)Google Scholar
  3. 3.
    Alzaghoul, A.F.: The implication of the learning theories on implementing e-learning courses. The Research Bulletin of Jordan 2(2), 27–30 (2012)Google Scholar
  4. 4.
    Hwang, K., Yang, C.: Automated Inattention and Fatigue Detection System in Distance Education for Elementary School Students. Journal of Educational Technology & Society 12, 22–35 (2009)Google Scholar
  5. 5.
    Pimenta, A., Carneiro, D., Novais, P., Neves, J.: Monitoring Mental Fatigue through the Analysis of Keyboard and Mouse Interaction Patterns. In: Pan, J.-S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) HAIS 2013. LNCS, vol. 8073, pp. 222–231. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Jones, F., Kinman, G.: Approaches to Studying Stress. In: Jones, F., Bright, J. (eds.) Stress: Myth, Theory and Research, Pearson Education, Harlow (2001)Google Scholar
  7. 7.
    Pimenta, A., Carneiro, D., Novais, P., Neves, J.: Analysis of Human Performance as a Measure of Mental Fatigue. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS, vol. 8480, pp. 389–401. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  8. 8.
    Monrose, F., Rubin, A.: Authentication via keystroke dynamics. In: Proceedings of the 4th ACM Conference on Computer and Communications Security, pp. 48–56. ACM, New York (1997)CrossRefGoogle Scholar
  9. 9.
    Alves, F., Pagano, A., Da Silva, I.: A new window on translators’ cognitive activity: methodological issues in the combined use of eye tracking, key logging and retrospective protocols. Copenhagen Studies in Language (38), 267–291 (2010)Google Scholar
  10. 10.
    Rodrigues, M., Gonçalves, S., Carneiro, D., Novais, P., Fdez-Riverola, F.: Keystrokes and Clicks: Measuring Stress on E-learning Students. In: Casillas, J., Martínez-López, F.J., Vicari, R., De la Prieta, F. (eds.) Management Intelligent Systems. AISC, vol. 220, pp. 119–126. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Turner, R.: The handbook of operator fatigue. Ergonomics 56(9), 1486 (2013)CrossRefGoogle Scholar
  12. 12.
    Dholi, P.R., Chaudhari, K.P.: Typing Pattern Recognition Using Keystroke Dynamics. In: Das, V.V., Chaba, Y. (eds.) AIM/CCPE 2012. CCIS, vol. 296, pp. 275–280. Springer, Heidelberg (2013)Google Scholar
  13. 13.
    Kraan, K.O., Dhondt, S., Houtman, I.L.D., Batenburg, R.S., Kompier, M.A.J., Taris, T.W.: Computers and types of control in relation to work stress and learning. Behaviour & Information Technology 33(10), 1013–1026 (2014)CrossRefGoogle Scholar
  14. 14.
    Baños, R.M., Etchemendy, E., Castilla, D., García-Palacios, A., Quero, S., Botella, C.: Positive mood induction procedures for virtual environments designed for elderly people. Interacting with Computers 24(3), 131–138 (2012)CrossRefGoogle Scholar
  15. 15.
    Seyle, H.: The stress of life. McGraw-Hill, New York (1956)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sérgio Gonçalves
    • 1
  • Manuel Rodrigues
    • 2
  • Davide Carneiro
    • 3
  • Florentino Fdez-Riverola
    • 1
  • Paulo Novais
    • 1
    • 2
  1. 1.Informatics DepartmentUniversity of VigoOurenseSpain
  2. 2.Escola Superior de Tecnologia e GestãoFelgueirasPortugal
  3. 3.Algoritmi Centre/Department of InformaticsUniversity of MinhoBragaPortugal

Personalised recommendations