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Working Memory Differences in E-Learning Environments: Optimization of Learners’ Performance through Personalization

  • Nikos Tsianos
  • Panagiotis Germanakos
  • Zacharias Lekkas
  • Costas Mourlas
  • George Samaras
  • Mario Belk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)

Abstract

Working memory (WM) is a psychological construct that has a major effect on information processing, thus signifying its importance when considering individual differences and adaptive educational hypermedia. Previous work of the authors in the field has demonstrated that personalization on human factors, including the WM sub-component of visuospatial sketchpad, may assist learners in optimizing their performance. To that end, a deeper approach in WM has been carried out, both in terms of more accurate measurements and more elaborated adaptation techniques. This paper presents results from a sample of 80 university students, underpinning the importance of WM in the context of an e-learning application in a statistically robust way. In short, learners that have low WM span expectedly perform worse than learners with higher levels of WM span; however, through proper personalization techniques this difference is completely alleviated, leveling the performance of low and normal WM span learners.

Keywords

Adaptive Hypermedia e-Learning Working Memory Individual Differences User Profiling 

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References

  1. 1.
    Tomas Chamorro-Premuzic, T., Furnham, A.: Personality, intelligence and approaches to learning as predictors of academic performance. Personality and Individual Differences 44, 1596–1603 (2008)CrossRefGoogle Scholar
  2. 2.
    Chen, G., Gully, S.M., Whiteman, J., Kilcullen, R.N.: Examination of Relationships Among Trait-Like Individual Differences, State-Like Individual Differences, and Learning Performance. Journal of Applied Psychology 85(6), 835–847 (2000)CrossRefGoogle Scholar
  3. 3.
    Lau, S., Roeser, R.W.: Cognitive abilities and motivational processes in science achievement and engagement: A person-centered analysis. Learning and Individual Differences 18(4), 497–504 (2008)CrossRefGoogle Scholar
  4. 4.
    Colom, R., Escorial, S., Shih, P.C., Privado, J.: Fluid intelligence, memory span, and temperament difficulties predict academic performance of young adolescents. Personality and Individual Differences 42(8), 1503–1514 (2007)CrossRefGoogle Scholar
  5. 5.
    Cristea, A., Stewart, C., Brailsford, T., Cristea, P.: Adaptive Hypermedia System Interoperability: a ‘real world’ evaluation. Journal of Digital Information 8(3) (2007), http://journals.tdl.org/jodi/article/view/235/192
  6. 6.
    Papanikolaou, K.A., Grigoriadou, M., Kornilakis, H., Magoulas, G.D.: Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE. User-Modelling and User-Adapted Interaction 13(3), 213–267 (2003)CrossRefGoogle Scholar
  7. 7.
    Carver Jr., C.A., Howard, R.A., Lane, W.D.: Enhancing student learning through hypermedia courseware and incorporation of student learning styles. IEEE Transactions on Education 42(1), 33–38 (1999)CrossRefGoogle Scholar
  8. 8.
    Gilbert, J.E., Han, C.Y.: Arthur: A Personalized Instructional System. Journal of Computing in Higher Education 14(1), 113–129 (2002)CrossRefGoogle Scholar
  9. 9.
    Sternberg, R.J., Grigorenko, E.L.: Are Cognitive Styles Still in Style? American Psychologist 52(7), 700–712 (1997)CrossRefGoogle Scholar
  10. 10.
    Cassidy, S.: Learning Styles: An overview of theories, models, and measures. Educational Psychology 24(4), 419–444 (2004)CrossRefGoogle Scholar
  11. 11.
    Riding, R.J., Cheema, I.: Cognitive Styles – an overview and integration. Educational Psychology 11(3-4), 193–215 (1991)CrossRefGoogle Scholar
  12. 12.
    Germanakos, P., Tsianos, N., Lekkas, Z., Mourlas, C., Samaras, G.: Capturing Essential Intrinsic User Behaviour Values for the Design of Comprehensive Web-based Personalized Environments. Computers in Human Behavior 24(4), 1434–1451 (2008)CrossRefGoogle Scholar
  13. 13.
    Colom, R., Abad, F.J., Quiroga, A., Shih, P.C., Flores-Mendoza, C.: Working memory and intelligence are highly related constructs, but why? Intelligence 36(6), 584–606 (2008)CrossRefGoogle Scholar
  14. 14.
    Lynn, R., Irwing, P.: Sex differences in mental arithmetic, digit span, and g defined as working memory capacity. Intelligence 36(3), 226–235 (2008)CrossRefGoogle Scholar
  15. 15.
    Tsianos, N., Lekkas, Z., Germanakos, P., Mourlas, C., Samaras, G.: User-centered Profiling on the basis of Cognitive and Emotional Characteristics: An Empirical Study. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 214–223. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Alloway, T.P.: Working memory, but not IQ, predicts subsequent learning in children with learning difficulties. European Journal of Psychological Assessment (in press) Google Scholar
  17. 17.
    Baddeley, A.: The concept of working memory: A view of its current state and probable future development. Cognition 10(1-3), 17–23 (1981)CrossRefGoogle Scholar
  18. 18.
    Baddeley, A.: Working Memory. Science 255, 556–559 (1992)CrossRefGoogle Scholar
  19. 19.
    Baddeley, A.: The episodic buffer: a new component of working memory? Trends in Cognitive Sciences 11(4), 417–423 (2000)CrossRefGoogle Scholar
  20. 20.
    Loggie, R.H., Zucco, G.N., Baddeley, A.D.: Interference with visual short-term memory. Acta Psychologica 75(1), 55–74 (1990)CrossRefGoogle Scholar
  21. 21.
    DeStefano, D., Lefevre, J.: Cognitive load in hypertext reading: A review. Computers in Human Behavior 23(3), 1616–1641 (2007)CrossRefGoogle Scholar
  22. 22.
    Lee, M.J., Tedder, M.C.: The effects of three different computer texts on readers’ recall: based on working memory capacity. Computers in Human Behavior 19(6), 767–783 (2003)CrossRefGoogle Scholar
  23. 23.
    Kirschner, P.A.: Cognitive load theory: implications of cognitive load theory on the design of learning. Learning and Instruction 12(1), 1–10 (2002)CrossRefGoogle Scholar
  24. 24.
    Turley-Ames, K.J., Whitfield, M.M.: Strategy training and working memory task performance. Journal of Memory and Language 49, 446–468 (2003)CrossRefGoogle Scholar
  25. 25.
    Demetriou, A., Christou, C., Spanoudis, G., Platsidou, M.: The development of mental processing: Efficiency, working memory, and thinking. Monographs of the Society for Research in Child Development 67(1), 1–155 (2002)CrossRefGoogle Scholar
  26. 26.
    Pickering, S., Gathercole, S.: The Working Memory Test Battery for Children. The Psychological Corporation (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nikos Tsianos
    • 1
  • Panagiotis Germanakos
    • 2
    • 3
  • Zacharias Lekkas
    • 1
  • Costas Mourlas
    • 1
  • George Samaras
    • 2
  • Mario Belk
    • 2
  1. 1.Faculty of Communication and Media StudiesNational and Kapodistrian University of AthensAthensHellas
  2. 2.Department of Computer ScienceUniversity of CyprusNicosiaCyprus
  3. 3.Department of Management and MISUniversity of NicosiaNicosiaCyprus

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