Analysis of self-regulated learning processing using statistical models for count data |
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| Authors: | Jeffrey Alan Greene Lara-Jeane Costa Kristin Dellinger |
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| Institution: | (1) School of Education, University of North Carolina at Chapel Hill, 113 Peabody Hall, CB#3500, Chapel Hill, NC 27599–3500, USA |
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| Abstract: | Researchers often use measures of the frequency of self-regulated learning (SRL; Zimmerman, American Educational Research Journal, 45(1), 166–183, 2000) processing as a predictor of learning gains. These frequency data, which are really counts of SRL processing events, are
often non-normally distributed, and the accurate analysis of these data requires the use of specialized statistical models.
In this study, we demonstrate how to implement and interpret count statistical models in path and latent profile analyses
to investigate the role of SRL as a mediator of the relation between pretest and posttest conceptual understanding. Our findings
from a sample of 170 college students using a computer to learn about the circulatory system show that SRL does mediate the
aforementioned relation, and that count models are a more accurate representation of SRL processing data than standard analysis
models based on ordinary least squares estimation. The results of our path analyses revealed which specific SRL processes
were related to learning, above and beyond the effect of prior knowledge. Our latent profile analysis revealed two groups
of participants, indicative of Brophy’s (2004) model of schematic and aschematic learners. We conclude with implications and future directions for basic and applied SRL
research. |
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