Scaffolding Self-directed Learning with Personalized Learning Goal Recommendations

  • Tobias Ley
  • Barbara Kump
  • Cornelia Gerdenitsch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6075)


Adaptive scaffolding has been proposed as an efficient means for supporting self-directed learning both in educational as well as in adaptive learning systems research. However, the effects of adaptation on self-directed learning and the differential contributions of different adaptation models have not been systematically examined. In this paper, we examine whether personalized scaffolding in the learning process improves learning. We conducted a controlled lab study in which 29 students had to solve several tasks and learn with the help of an adaptive learning system in a within-subjects control condition design. In the learning process, participants obtained recommendations for learning goals from the system in three conditions: fixed scaffolding where learning goals were generated from the domain model, personalized scaffolding where these recommendations were ranked according to the user model, and random suggestions of learning goals (control condition). Students in the two experimental conditions clearly outperformed students in the control condition and felt better supported by the system. Additionally, students who received personalized scaffolding selected fewer learning goals than participants from the other groups.


Adaptive scaffolding Personalization Adaptive Learning Systems Self-directed learning Layered Evaluation APOSDLE 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fischer, G., Scharff, E.: Learning technologies in support of self-directed learning. Journal of Interactive Media in Education 98(4), 1–32 (1998)Google Scholar
  2. 2.
    Lindstaedt, S., de Hoog, R., Ähnelt, M.: Supporting the Learning Dimension of Knowledge Work. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 639–644. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Simons, P.R.: Towards a constructivistic theory of self-directed learning. In: Straka, G.A. (ed.) Conceptions of self-directed learning, Waxmann, pp. 155–169 (2000)Google Scholar
  4. 4.
    Choo, C.W.: The knowing organization. How organizations use information to construct meaning, create knowledge, and make decision. Oxford University Press, New York (1998)Google Scholar
  5. 5.
    Mayer, R.E.: Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist 59(1), 14–19 (2004)CrossRefGoogle Scholar
  6. 6.
    Narciss, S., Proske, A., Koerndle, H.: Promoting self-regulated learning in web-based environments. Computers in Human Behavior 23(3), 1126–1144 (2007)CrossRefGoogle Scholar
  7. 7.
    Schnotz, W., Heiß, A.: Semantic scaffolds in hypermedia learning environments. Computers in Human Behavior 25(2), 371–380 (2009)CrossRefGoogle Scholar
  8. 8.
    Müller-Kalthoff, T., Möller, J.: The Effects of Graphical Overviews, Prior Knowledge, and Self-Concept on Hypertext Disorientation and Learning Achievement. J. of Educational Multimedia and Hypermedia 12(2), 117–134 (2003)Google Scholar
  9. 9.
    Hogan, K., Pressley, M.: Scaffolding student learning: Instructional approaches and issues. Brookline Books, Cambridge (1997)Google Scholar
  10. 10.
    Hannafin, M., Land, S., Oliver, K.: Open learning environments: Foundations, methods, and models. In: Reigeluth, C.M. (ed.) Instructional design theories and models, pp. 115–140. Erlbaum, Mahwah/N.J (1999)Google Scholar
  11. 11.
    Vye, N., Schwartz, D., Bransford, J., Barron, B., Zech, L.: SMART environments that support monitoring, reflection, and revision. In: Hacker, D., Dunlosky, J., Graesser, A. (eds.) Metacognition in educational theory and practice, pp. 305–346. Erlbaum, Mahwah/N.J (1998)Google Scholar
  12. 12.
    Azevedo, R., Cromley, J., Seibert, D.: Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemp. Educ. Psych. 29, 344–370 (2004)CrossRefGoogle Scholar
  13. 13.
    Brusilovsky, P., Peylo, C.: Adaptive and Intelligent Web-based Educational Systems. Int. J. of Artificial Intelligence in Education 13, 159–172 (2003)Google Scholar
  14. 14.
    Brusilovsky, P.: Adaptive Hypermedia. User Modeling and User-Adapted Interaction 11(1-2), 87–110 (2001)zbMATHCrossRefGoogle Scholar
  15. 15.
    Lindstaedt, S.N., Ley, T., Scheir, P., Ulbrich, A.: Applying Scruffy Methods to Enable Work-integrated Learning. Europ. J. of the Informatics Professional 9(3), 44–50 (2008)Google Scholar
  16. 16.
    Ley, T., Kump, B., Ulbrich, A., Scheir, P., Lindstaedt, S.N.: A Competence-based Approach for Formalizing Learning Goals in Work-integrated Learning. In: EdMedia 2008, pp. 2099–2108. AACE, Chesapeake/VA (2008)Google Scholar
  17. 17.
    Korossy, K.: Extending the theory of knowledge spaces: A competence-performance approach. Zeitschrift für Psychologie 205, 53–82 (1997)Google Scholar
  18. 18.
    Ley, T., Kump, B., Albert, D.: A methodology for eliciting, modelling, and evaluating expert knowledge for an adaptive work-integrated learning system. Int. J. of Human-Computer Studies 68(4), 185–208 (2010)CrossRefGoogle Scholar
  19. 19.
    Lindstaedt, S., Beham, G., Kump, B., Ley, T.: Getting to Know Your User – Unobtrusive User Model Maintenance within Work-Integrated Learning Environments. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 73–87. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Brusilovsky, P., Karagiannidis, C., Sampson, D.: The Benefits of Layered Evaluation of Adaptive Applications and Services. In: Weibelzahl, S., Chin, D., Weber, G. (eds.) Empirical evaluation of adaptive systems, Workshop at the UM 2001, pp. 1–8 (2001)Google Scholar
  21. 21.
    Weibelzahl, S., Lauer, C.U.: Framework for the evaluation of adaptive CBR-systems. In: Vollrath, I., Schmitt, S., Reimer, U. (eds.) Experience Management as Reuse of Knowledge, GWCBR 2001, Baden-Baden, Germany, pp. 254–263 (2001)Google Scholar
  22. 22.
    Ghidini, C., Rospocher, M., Serafini, L., Faatz, A., Kump, B., Ley, T., Pammer, V., Lindstaedt, S.: Collaborative enterprise integrated modelling. In: EKAW 2008, pp. 40–42, INRIA, Grenoble (2008)Google Scholar
  23. 23.
    Falmagne, J., Cosyn, E., Doble, C., Thiery, N., Uzun, H.: Assessing mathematical knowledge in a learning space: Validity and/or reliability. Paper Presented at the Annual Meeting of the Am. Educational Research Association (2007)Google Scholar
  24. 24.
    Kump, B.: A Validation Framework for Formal Models in Adaptive Work-Integrated Learning. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 416–420. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Kalyuga, S., Sweller, J.: Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educ. Technol. Research & Development 53(3), 83–93 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tobias Ley
    • 1
    • 2
  • Barbara Kump
    • 3
  • Cornelia Gerdenitsch
    • 2
  1. 1.Know-CenterGrazAustria
  2. 2.Cognitive Science SectionUniversity of GrazGrazAustria
  3. 3.Knowledge Management InstituteGraz University of TechnologyGrazAustria

Personalised recommendations