Analysis of Self-Regulated Learning Processing Using Statistical Models for Count Data

Autor/inn/en	Greene, Jeffrey Alan; Costa, Lara-Jeane; Dellinger, Kristin
Titel	Analysis of Self-Regulated Learning Processing Using Statistical Models for Count Data
Quelle	In: Metacognition and Learning, 6 (2011) 3, S.275-301 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1556-1623
DOI	10.1007/s11409-011-9078-4
Schlagwörter	Metacognition; Educational Research; Prior Learning; Anatomy; Least Squares Statistics; Human Body; Profiles; Statistical Analysis; Pretests Posttests; Concept Formation; Path Analysis; College Students; Learning Processes; Computer Assisted Instruction + Suchen Sie Ihr Suchwort? Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Bildungsforschung; Pädagogische Forschung; Vorkenntnisse; Anatomie; Menschlicher Körper; Charakterisierung; Profilanalyse; Statistische Analyse; Concept learning; Begriffsbildung; Pfadanalyse; Collegestudent; Learning process; Lernprozess; Computer based training; Computerunterstützter Unterricht
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10