Abstract
We discuss the articles of this special issue with reference to an important yet previously only implicit dimension of study quality: alignment across the theoretical and methodological decisions that collectively define an approach to self-regulated learning. Integrating and extending work by leaders in the field, we propose a framework for evaluating alignment in the way self-regulated learning research is both conducted and reported. Within this framework, the special issue articles provide a springboard for discussing methodological considerations of increasingly sophisticated research on the dynamic, contingent, and contextualized features of self-regulated learning.
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Notes
An alternative and perhaps more appropriate statistical approach to capture variation in patterns of improvement within this context is factor mixture modeling (Lubke and Muthen 2005). In particular, factor mixture modeling permits the possibility that the marginal fit of the measurement model could be attributed to nonequivalence of the latent structure over time or across subgroups within the sample.
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During preparation of this manuscript, the second author was supported by National Institute on Drug Abuse (NIDA) Grant P30 DA023026. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of NIDA.
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Dent, A.L., Hoyle, R.H. A framework for evaluating and enhancing alignment in self-regulated learning research. Metacognition Learning 10, 165–179 (2015). https://doi.org/10.1007/s11409-015-9136-4
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DOI: https://doi.org/10.1007/s11409-015-9136-4