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15-07-2017 | Original research

Modeling Student Learning Behavior Patterns in an Online Science Inquiry Environment

Authors: Daniel G. Brenner, Bryan J. Matlen, Michael J. Timms, Perman Gochyyev, Andrew Grillo-Hill, Kim Luttgen, Marina Varfolomeeva

Published in: Technology, Knowledge and Learning | Issue 3/2017

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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.

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Footnotes
1
One hundred and nine students were omitted from these analyses due to missing data, leaving 1619 students in this analysis.
 
2
One-hundred and twenty-three students were omitted from this analysis due to missing data, leaving 1605 students in this analysis.
 
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Metadata
Title
Modeling Student Learning Behavior Patterns in an Online Science Inquiry Environment
Authors
Daniel G. Brenner
Bryan J. Matlen
Michael J. Timms
Perman Gochyyev
Andrew Grillo-Hill
Kim Luttgen
Marina Varfolomeeva
Publication date
15-07-2017
Publisher
Springer Netherlands
Published in
Technology, Knowledge and Learning / Issue 3/2017
Print ISSN: 2211-1662
Electronic ISSN: 2211-1670
DOI
https://doi.org/10.1007/s10758-017-9325-0

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