ERIC Number: EJ1154056
Record Type: Journal
Publication Date: 2017-Oct
Pages: 21
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-2211-1662
EISSN: N/A
Available Date: N/A
Modeling Student Learning Behavior Patterns in an Online Science Inquiry Environment
Brenner, Daniel G.; Matlen, Bryan J.; Timms, Michael J.; Gochyyev, Perman; Grillo-Hill, Andrew; Luttgen, Kim; Varfolomeeva, Marina
Technology, Knowledge and Learning, v22 n3 p405-425 Oct 2017
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.
Descriptors: Learning Processes, Prior Learning, Online Courses, Science Education, Inquiry, Computer Simulation, Biology, Scientific Concepts, Units of Study, Evolution, Hypothesis Testing, Prediction, Scaffolding (Teaching Technique), Student Behavior, Expertise, Intelligent Tutoring Systems
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305A110021
Author Affiliations: N/A