Modeling Student Learning Behavior Patterns in an Online Science Inquiry Environment
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Modeling Student Learning Behavior Patterns in an Online Science Inquiry Environment Daniel G. Brenner1 • Bryan J. Matlen1 • Michael J. Timms2 Perman Gochyyev3 • Andrew Grillo-Hill1 • Kim Luttgen1 • Marina Varfolomeeva1
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Springer Science+Business Media B.V. 2017
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
& Daniel G. Brenner [email protected] 1
WestEd STEM, 400 Seaport Ct. Suite 222, Redwood City, CA 94063, USA
2
Australian Council for Educational Research, Adelaide, Australia
3
UC Berkeley, Berkeley, CA, USA
123
D. G. Brenner et al.
1 Introduction The goal of inquiry learning is to allow students to induce the characteristics of a domain through their own experiments and exploration (de Jong 2006). Engaging in inquiry tasks has been shown to improve students’ understanding of science, as measured by assessments such as the National Assessment of Educational Progress (NAEP), state tests, and items from the Trends in International Mathematics and Science Study (Rivet and Krajcik 2004; Marx et al. 2004). Studies point to the lack of ‘‘rigorous and excellent’’ instruction in U.S. schools on science inquiry skills—those that build student ability to form ideas or hypot
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