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Students’ Adaptation and Transfer of Strategies across Levels of Scaffolding in an Exploratory Environment

  • Ido Roll
  • Nikki Yee
  • Adriana Briseno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)

Abstract

While the effect of scaffolding on learning has received much attention, less is known about its effect on students’ strategy use, especially in transfer activities. This study focuses on students’ adaptive behaviours as a function of given scaffolding and when transitioning from a scaffolded to an unstructured activity. We study this in the context of a complex physics simulation in which students choose between 124 different actions. We evaluate (i) how the scaffolding affects students’ building and testing behaviours, (ii) whether these behaviours transfer to an unstructured activity, and (iii) the relationship between the adapted behaviours and learning. A repeated-measures MANOVA suggests that students adapt their learning behaviours according to the demands and affordances of the task and the environment, and that these strategies transfer from a scaffolded to an unstructured activity. No significant relationships were found between these patterns and learning.

Keywords

scaffolding inquiry learning microworlds interactive simulations transfer self-regulated learning 

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References

  1. 1.
    Hmelo-Silver, C.E., Golan Duncan, R., Chinn, C.A.: Scaffolding and Achievement in Problem-Based and Inquiry Learning: A Response to Kirschner, Sweller, and Clark. Educ. Psych. 42(2), 99–107 (2007)CrossRefGoogle Scholar
  2. 2.
    Mulder, Y.G., Lazonder, A.W., de Jong, T.: Finding Out How They Find It Out: An Empirical Analysis of Inquiry Learners’ Need for Support. Int’l J. of Sci. Ed., 1–21 (2009)Google Scholar
  3. 3.
    Holmes, N.G., Day, J., Park, A.H.K., Bonn, D.A., Roll, I.: Making the failure more productive: Scaffolding the invention process to improve inquiry behaviours and outcomes in productive failure activities. Instructional Science (2013), doi:10.1007/s11251-013-9300-7Google Scholar
  4. 4.
    Sao Pedro, M.A., Baker, R.S.J.d., Gobert, J.D., Montalvo, O., Nakama, A.: Leveraging Machine-learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill. User Modeling and User-Adapted Interaction 23, 1–39 (2011)Google Scholar
  5. 5.
    Gobert, J., Raziuddin, J., Koedinger, K.R.: Auto-scoring Discovery and Confirmation Bias in Interpreting Data during Science Inquiry in a Microworld. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 770–773. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Jeong, H., Biswas, G.: Mining Student Behavior Models in Learning-by-Teaching Environments. In: de Baker, R.S.J., Barnes, T., Beck, J.E. (eds.) Proceeds of the First International Conference on Educational Data Mining, Montreal, Quebec (2008)Google Scholar
  7. 7.
    Koedinger, K.R., Aleven, V., Roll, I., Baker, R.: In vivo experiments on whether supporting metacognition in intelligent tutoring systems yields robust learning. In: Handbook of Metacognition in Education, pp. 897–964 (2009)Google Scholar
  8. 8.
    Roll, I., Aleven, V., McLaren, B.M., Koedinger, K.R.: Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction 21, 267–280 (2011)CrossRefGoogle Scholar
  9. 9.
    Wieman, C.E., Adams, W.K., Perkins, K.K.: PhET: Simulations that enhance learning. Science 322(5902), 682–683 (2008)CrossRefGoogle Scholar
  10. 10.
    Podolefsky, N.S., Perkins, K.K., Adams, W.K.: Factors promoting engaged exploration with computer simulations. Phys. Rev. Special Topics - Phys. Ed. Res. 6(2) (2010)Google Scholar
  11. 11.
    Nathan, M.J.: Knowledge and situational feedback in a learning environment for algebra story problem solving. Interactive Learning Environments 5(1), 135–159 (1998)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Roll, I., Briseno, A., Yee, N.: Not a magic bullet: The effect of scaffolding on knowledge and attitudes in online simulations. In: Proceedings of ICLS (2014)Google Scholar
  13. 13.
    Kardan, S., Roll, I., Conati, C.: The usefulness of log based clustering in a complex simulation environment. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 168–177. Springer, Heidelberg (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ido Roll
    • 1
  • Nikki Yee
    • 1
  • Adriana Briseno
    • 1
  1. 1.Centre for Teaching, Learning, and Technologythe University of British ColumbiaVancouverCanada

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