Learning Preferences and Self-Regulation – Design of a Learner-Directed E-Learning Model

  • Stella Lee
  • Trevor Barker
  • Vive Kumar
Part of the Communications in Computer and Information Science book series (CCIS, volume 257)

Abstract

In e-learning, questions concerned how one can create course material that motivate and support students in guiding their own learning have attracted an increasing number of research interests ranging from adaptive learning systems design to personal learning environments and learning styles/preferences theories. The main challenge of learning online remains how learners can accurately direct and regulate their own learning without the presence of tutors to provide instant feedback. Furthermore, learning a complex topic structured in various media and modes of delivery require learners to make certain instructional decisions concerning what to learn and how to go about their learning. In other words, learning requires learners to self-regulate their own learning[1]. Very often, learners have difficulty self-directing when topics are complex and unfamiliar. It is not always clear to the learners if their instructional decisions are optimal.[2] Research into adaptive e-learning systems has attempted to facilitate this process by providing recommendations, classifying learners into different preferred learning styles, or highlighting suggested learning paths[3]. However, system-initiated learning aid is just one way of supporting learners; a more holistic approach, we would argue, is to provide a simple, all-in-one interface that has a mix of delivery modes and self-regulation learning activities embedded in order to help individuals learn how to improve their learning process. The aim of this research is to explore how learners can self-direct and self-regulate their online learning both in terms of domain knowledge and meta knowledge in the subject of computer science. Two educational theories: experiential learning theory (ELT) and self-regulated learning (SRL) theory are used as the underpinning instructional design principle. To assess the usefulness of this approach, we plan to measure: changes in domain-knowledge; changes in meta-knowledge; learner satisfaction; perceived controllability; and system usability. In sum, this paper describes the research work being done on the initial development of the e-learning model, instructional design framework, research design as well as issues relating to the implementation of such approach.

Keywords

learning theory learning preferences self-regulated learning E-Learning instructional design learning design 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hadwin, F.H., Winne, P.H.: CoNoteS2: A Software Tool for Promoting Self-Regulation. Educational Research and Evaluation 7, 313–334 (2001)CrossRefGoogle Scholar
  2. 2.
    Azevedo, R., Cromley, J.G., Seibert, D., Tron, M.: The role of co-regulated learning during students’ understanding of complex systems with hypermedia. In: Annual Meeting of the American Educational Research Association, Chicago, IL (2003)Google Scholar
  3. 3.
    Brusilovsky, P.: Adaptive educational systems on the World-Wide-Web. In: Ayala, G. (ed.) Proc. of Workshop Current Trends and Applications of Artificial Intelligence in Education at 4th World Congress on Expert Systems, Mexico City, Mexico. ITESM, pp. 9–16 (1998)Google Scholar
  4. 4.
    Weber, G.: Adaptive learning systems in the World Wide Web. In: Kay, J. (ed.) 7th International Conference on User Modeling, UM 1999, Banff, Canada (1999)Google Scholar
  5. 5.
    Adisen, A., Barker, T.: Supporting the diversity of the E-learning 2.0 learners: The development of a psychological student model. In: E-Learn 2007 Conference, Quebec City, Canada (2007)Google Scholar
  6. 6.
    Barker, T., Adisen, A.: Experiments on visual and verbal skills: The development and testing of a skills profile. In: Proceedings of the European Learning Styles Information Network Conference, Univerity of Surrey, Surrey, England (2005)Google Scholar
  7. 7.
    Fischer, G.: User modeling in human-computer interaction. User Modeling and User Adapted Interaction 11, 65–86 (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Pashler, H., McDaniel, M., Rohrer, D., Bjork, R.: Learning Styles: Concepts and Evidence. Psychological Science in the Public Interest 9, 105–119 (2008)Google Scholar
  9. 9.
    Kolb, D.: Experiential learning: Experience as the source of learning and development. Prentice-Hall, Englewood Cliffs (1984)Google Scholar
  10. 10.
    Coffield, F., Moseley, D., Hall, E., Ecclestone, K.: Learning styles and pedagogy in post-16 learning: A systematic and critical review. Learning and Skills Research Centre, London (2004)Google Scholar
  11. 11.
    Kolb, A., Kolb, D.: Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of Management Learning & Education 4 (2005)Google Scholar
  12. 12.
    Boekaerts, M., Corno, L.: Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology: An International Review 54, 199–231 (2005)CrossRefGoogle Scholar
  13. 13.
    Perry, N.E., Phillips, L., Hutchinson, L.R.: Preparing student teachers to support for self-regulated learning. Elementary School Journal 106, 237–254 (2006)CrossRefGoogle Scholar
  14. 14.
    Winne, P.H., Perry, N.E.: Measuring self-regulated learning. In: Pintrich, P., Boekaerts, M., Seidner, M. (eds.) Handbook of Self-Regulation, pp. 531–566. Academic Press, Orlando (2000)CrossRefGoogle Scholar
  15. 15.
    Butler, D.L., Winne, P.H.: Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research 65, 245–281 (1995)CrossRefGoogle Scholar
  16. 16.
    Zimmerman, B.J.: Self-regulated learning and academic achievement: An overview. Educational Psychologist 25, 3–17 (1990)CrossRefGoogle Scholar
  17. 17.
    Winne, P.H.: Experimenting to bootstrap self-regulated learning. Journal of Educational Psychology 89, 397–410 (1997)CrossRefGoogle Scholar
  18. 18.
    Azevedo, R.: Using Hypermedia as a Metacognitive Tool for Enhancing Student Learning? The Role of Self-Regulated Learning. Educational Psychologist 40, 199–209 (2005)CrossRefGoogle Scholar
  19. 19.
    Pintrich, P.R.: The role of goal orientation in self-regulated learning. In: Boekaerts, M., Pintrich, P.R., Ziedner, M. (eds.) Handbook of Self-Regulation, pp. 451–502. Academic Press, San Diego (2000)CrossRefGoogle Scholar
  20. 20.
    Hadwin, A.F., Wozney, L., Pontin, O.: Scaffolding the appropriation of self-regulatory activity: A socio-cultural analysis of changes in teacher-student discourse about a graduate research portfolio. Instructional Science 33, 413–450 (2005)CrossRefGoogle Scholar
  21. 21.
    Marzano, R.J.: A different kind of classroom: Teaching with dimensions of learning. Association for Supervision and Curriculum Development, Alexandria (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Stella Lee
    • 1
    • 2
  • Trevor Barker
    • 1
  • Vive Kumar
    • 3
  1. 1.Computer Science DepartmentUniversity of HertfordshireHatfieldUK
  2. 2.Golder Associates Inc.CalgaryCanada
  3. 3.School of Computing & Information SystemsAthabasca UniversityEdmontonCanada

Personalised recommendations