Sense Making Alone Doesn’t Do It: Fluency Matters Too! ITS Support for Robust Learning with Multiple Representations

  • Martina A. Rau
  • Vincent Aleven
  • Nikol Rummel
  • Stacie Rohrbach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7315)

Abstract

Previous research demonstrates that multiple representations of learning content can enhance students’ learning, but also that students learn deeply from multiple representations only if the learning environment supports them in making connections between the representations. We hypothesized that connection-making support is most effective if it helps students makesense of the content across representations and in becoming fluent in making connections. We tested this hypothesis in a classroom experiment with 599 4th- and 5th-grade students using an ITS for fractions. The experiment further contrasted two forms of support for sense making: auto-linked representations and the use of worked examples involving one representation to guide work with another. Results confirm our main hypothesis: A combination of worked examples and fluency support lead to more robust learning than versions of the ITS without connection-making support. Therefore, combining different types of connection-making support is crucial in promoting students’ deep learning from multiple representations.

Keywords

Multiple representations fractions intelligent tutoring system connection making classroom evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Suh, J., Moyer, P.S.: Developing Students’ Representational Fluency Using Virtual and Physical Algebra Balances. Computers in Mathematics and Science Teaching 26, 155–173 (2007)Google Scholar
  2. 2.
    Ainsworth, S.: DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction 16, 183–198 (2006)CrossRefGoogle Scholar
  3. 3.
    Rau, M.A., Aleven, V., Rummel, N.: Intelligent tutoring systems with multiple representations and self-explanation prompts support learning of fractions. In: 14th International Conference on Artificial Intelligence, pp. 441–448. IOS Press, Amsterdam (2009)Google Scholar
  4. 4.
    Bodemer, D., et al.: Supporting learning with interactive multimedia through active integration of representations. Instructional Science 33, 73–95 (2005)CrossRefGoogle Scholar
  5. 5.
    van der Meij, J., de Jong, T.: Supporting Students’ Learning with Multiple Representations in a Dynamic Simulation-Based Learning Environment. Learning and Instruction 16, 199–212 (2006)CrossRefGoogle Scholar
  6. 6.
    Rau, M.A., et al.: How to schedule multiple graphical representations? A classroom experiment with an intelligent tutoring system for fractions. In: To appear in the Proceedings of ICLS 2012 (accepted, 2012)Google Scholar
  7. 7.
    Bodemer, D., et al.: The Active Integration of Information during Learning with Dynamic and Interactive Visualisations. Learning and Instruction 14, 325–341 (2004)CrossRefGoogle Scholar
  8. 8.
    National Mathematics Advisory Panel: Foundations for Success: Report of the National Mathematics Advisory Board Panel, U.S. Government Printing Office (2008)Google Scholar
  9. 9.
    Charalambous, C.Y., Pitta-Pantazi, D.: Drawing on a Theoretical Model to Study Students’ Understandings of Fractions. Educational Studies in Mathematics 64, 293–316 (2007)CrossRefGoogle Scholar
  10. 10.
    Siegler, R.S., et al.: Developing effective fractions instruction: A practice guide. In: National Center for Education Evaluation and Regional Assistance. IES, U.S. Department of Education, Washington, DC (2010)Google Scholar
  11. 11.
    Koedinger, K.R., et al.: Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Cognitive Science (in press)Google Scholar
  12. 12.
    Seufert, T., Brünken, R.: Cognitive load and the format of instructional aids for coherence formation. Applied Cognitive Psychology 20, 321–331 (2006)CrossRefGoogle Scholar
  13. 13.
    Bodemer, D., Faust, U.: External and mental referencing of multiple representations. Computers in Human Behavior 22, 27–42 (2006)CrossRefGoogle Scholar
  14. 14.
    Renkl, A.: The worked-out example principle in multimedia learning. In: Mayer, R. (ed.) Cambridge Handbook of Multimedia Learning, pp. 229–246. Cambridge University Press, Cambridge (2005)Google Scholar
  15. 15.
    Salden, R.J.C.M., Koedinger, K.R., Renkl, A., Aleven, V., McLaren, B.M.: Accounting for beneficial effects of worked examples in tutored problem solving. Educational Psychology Review 22, 379–392 (2010)CrossRefGoogle Scholar
  16. 16.
    Berthold, K., Eysink, T., Renkl, A.: Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science 37, 345–363 (2009)CrossRefGoogle Scholar
  17. 17.
    Koedinger, K.R., Corbett, A.: Cognitive Tutors: Technology Bringing Learning Sciences to the Classroom. In: Sawyer, R.K. (ed.) The Cambridge Handbook of the Learning Sciences, pp. 61–77. Cambridge University Press, New York (2006)Google Scholar
  18. 18.
    Koedinger, K.R., Corbett, A.: Cognitive tutors: Technology bringing learning sciences to the classroom. Cambridge University Press, New York (2006)Google Scholar
  19. 19.
    Rittle-Johnson, B., Koedinger, K.R.: Designing Knowledge Scaffolds to Support Mathematical Problem Solving. Cognition and Instruction 23, 313–349 (2005)CrossRefGoogle Scholar
  20. 20.
    Aleven, V., et al.: A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education 19, 105–154 (2009)Google Scholar
  21. 21.
    Raudenbush, S.W., Bryk, A.S.: Hierarchical Linear Models: Applications and Data Analysis Methods. Sage Publications, Newbury Park (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Martina A. Rau
    • 1
  • Vincent Aleven
    • 1
  • Nikol Rummel
    • 1
    • 2
  • Stacie Rohrbach
    • 3
  1. 1.Human-Computer Interaction InstituteCarnegie Mellon UniversityUSA
  2. 2.Institute of Educational ResearchRuhr-Universität BochumGermany
  3. 3.School of DesignCarnegie Mellon UniversityUSA

Personalised recommendations