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Technology, Knowledge and Learning

, Volume 22, Issue 3, pp 405–425 | Cite as

Modeling Student Learning Behavior Patterns in an Online Science Inquiry Environment

  • Daniel G. Brenner
  • Bryan J. Matlen
  • Michael J. Timms
  • Perman Gochyyev
  • Andrew Grillo-Hill
  • Kim Luttgen
  • Marina Varfolomeeva
Original research
First Online:

Abstract

This study investigated how the frequency and level of assistance provided to students interacted with prior knowledge to affect learning in the Voyage to Galapagos (VTG) science inquiry-learning environment. VTG provides students with the opportunity to do simulated science field work in Galapagos as they investigate the key biology principles of variation, biological function, and natural selection. Thirteen teachers used the VTG module during their Natural Selection and Evolution curriculum unit. Students (N = 1728) were randomly assigned to one of four assistance conditions (Minimal-, Medium-, Medium–High, or High-Assistance). We predicted we would find an “Expertise Reversal Effect” (Kalyuga et al. in Edu Psychol Rev 194:509–539, 2007), whereby students with little prior knowledge benefit from assistance and students with higher prior knowledge benefit from minimal assistance. However, initial analyses revealed no interaction between prior knowledge and condition on student learning. To further explore results, we grouped students into 5 clusters based on student behaviors recorded during the use of VTG. The effect of assistance conditions within these clusters showed that, in two of the five clusters, results were consistent with the Expertise Reversal Effect. However, in two other clusters, the effect was reversed such that students with low prior knowledge benefited from lower amounts of assistance and vice versa. Though this study has not identified which specific characteristics determine optimal assistance levels, it suggests that prior knowledge is not sufficient for determining when students will differentially benefit from assistance. We propose that other factors such as self-regulated learning should be investigated in future research.

Keywords

Expertise reversal effect Inquiry learning Bayesian intelligent tutor Educational data mining Evolution 
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Notes

Acknowledgements

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A110021 to WestEd. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education. This work reflects the significant efforts of researchers, educators, and programmers. The authors wish to acknowledge the efforts and expertise of other members of the team in developing the intervention: Bruce McLaren, David Brown, Jerry Richardson, Nick Matzke, and Russell Almond who contributed to the theoretical and practical design work. The authors would also like to acknowledge the prior work on a non web-based version of VTG (National Science Foundation grant award number 9618014, Weihnacht and Durham).

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Daniel G. Brenner
    • 1
  • Bryan J. Matlen
    • 1
  • Michael J. Timms
    • 2
  • Perman Gochyyev
    • 3
  • Andrew Grillo-Hill
    • 1
  • Kim Luttgen
    • 1
  • Marina Varfolomeeva
    • 1
  1. 1.WestEd STEMRedwood CityUSA
  2. 2.Australian Council for Educational ResearchAdelaideAustralia
  3. 3.UC BerkeleyBerkeleyUSA

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