Individual Learning Pattern Related to Intention

with a Biological Model of Knowledge Construction
  • Toshie Ninomiya
  • Wataru Tsukahara
  • Toshiaki Honda
  • Toshio Okamoto
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 230)

Abstract

In an e-learning system, it is difficult to grasp the learner’s condition for learning. In the face-to-face learning environment, we tend to notice if there is something unusual about learners and help them out of difficulties. In addition, we often cannot keep learners from dropping out of the e-learning course. This suggests a requirement for a research study specially focused on how to predict the learner’s condition with the learning log data in the e-learning system. Therefore, we drew up a learning model with biological knowledge from the latest molecular biology and brain science. In this paper, results from our learning model were verified by comparison with real learning log data in an e-learning system. Our model suggests that there is a learning type with high intention who prefers to learn in a short term in order to construct his/her knowledge. Then we examined the data related to intention, and it correlated closely with the learning period of one exercise.

Keywords

Knowledge construction learning pattern biological model dopamine 

5. References

  1. Bliss, T.V. and Collingridge, G.L., 1993. A synaptic model of memory: long-term potentiation in the hippocampus. Nature, Vol.361, pp.31–39.CrossRefGoogle Scholar
  2. Botto, L.D. and Khoury, M.J., 2001. Facing the challenge of the gene-environment interaction: The two by for table and beyond. American Journal of Epidemiology, Vol.157, pp.1011–1020.Google Scholar
  3. Bush, G. et al., 2000. Cognitive and emotional influences in the anterior cingulated cortex. Trends in Cognitive Sciences, Vol.4, pp.215–222.CrossRefGoogle Scholar
  4. Cooper, R.M. and Zubek, J.P., 1958. Effects of enriched and restricted early environments on the learning ability of bright and dull rats. Canadian Journal of Psychology. Vol.12, pp. 159–164.Google Scholar
  5. Damasio, A.R., 1994. Descartes’ Error: Emotion, Reason and the Human Brain. Putnam Pub Group Published, NY, USA.Google Scholar
  6. Damasio, A.R., 1999. The Feeling of What Happens: Body and Emotion in the Making of Consciousness. Harcourt Published, CA, USA.Google Scholar
  7. Dubnau, J., et al., 2003. The staufen/pumilio pathway is involved in Drosophila long-term memory. Current Biology, Vol. 13, pp.286–296.CrossRefGoogle Scholar
  8. Eagan, M.F., et al., 2003. The BDKF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell, Vol. 112, pp.257–269.CrossRefGoogle Scholar
  9. ELeGI org., 2005/7. GRID Technology for Learning: the “European Learning GRID Infrastructure” project. http://www.elegi.org/.Google Scholar
  10. Gainetdinov, R.R., et al. 1999. Role of serotonin in the paradoxical calming effect of psychostimulants on hyperactivity. Science, Vol.283. pp.397–402.CrossRefGoogle Scholar
  11. Grabinger, S. and Dunlop, J. 1995. Rich environments for active learning. Association for Learning Technology Journal, 3(2), pp5–34.Google Scholar
  12. Gross, C., et al., 2002. Serotonin 1A receptor acts during development to establish normal anxiety-like behavior in the adult. Nature, Vol.416, pp.396–400.CrossRefGoogle Scholar
  13. Kandel, E.R., 2001. The molecular biology or memory storage: a dialogue between genes and synapses. Science, Vol.294, pp.1030–1038.CrossRefGoogle Scholar
  14. Hariri, A.R., et al., 2002. Serotonin transporter genetic variation and the response of the human amygdala. Science, Vol.297, pp.400–403.CrossRefGoogle Scholar
  15. LaHoste, G.J. et al., 1996. Dopamine D4 receptor gene polymorphism is associated with attention deficit hyperactivity disorder. Molecular Psychiatry, Vol. 1, pp.121–124.Google Scholar
  16. Milner, B. et al., 1998. Cognitive neuroscience and the study of memory. Neuron, Vol.20, pp.445–468.CrossRefGoogle Scholar
  17. Murphy, D.L., et al., 2001. Genetic perspectives on the serotonin transporter. Brain Research Bulletin, Vol.56, pp.487–494.CrossRefGoogle Scholar
  18. Ninomiya, T. et al., 2005. Social and biological model of cognitive development and learning pattern. CELDA 2005, pp.18–25.Google Scholar
  19. Nunes, J.M. and McPherson, M.A. 2002. Pedagogical and implementation models for e-learning continuing professional distance education (CPDE) emerging from action research. International Journal of Management Education, 2(3), pp16–25.Google Scholar
  20. Poo, M.M., 2001. Neurotrophins as synaptic modulators. Nature Reviews Neuroscience, Vol.2, pp.24–32.CrossRefGoogle Scholar
  21. Swanson, J. et al., 1998, Cognitive neuroscience of attention deficit hyperactivity disorder and hyperkinetic disorder. Current Opinion in Neurobiology, Vol.8, pp.263–271.CrossRefMathSciNetGoogle Scholar
  22. Tang, Y.P.,. et al., 1999. Genetic enhancement of learning and memory in mice. Nature, Vol.401, pp.63–69.CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Toshie Ninomiya
    • 1
    • 2
  • Wataru Tsukahara
    • 1
    • 2
  • Toshiaki Honda
    • 1
    • 2
  • Toshio Okamoto
    • 1
    • 2
  1. 1.Graduate School of Information SystemsUniversity of Electro-CommunicationsJapan
  2. 2.College of EducationIbaraki UniversityJapan

Personalised recommendations