Metacognitive calibration—an extended conceptualization and potential applications

By Stephanie Pieschl. 2009

189 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/42b19ce14aad05669d61d0131e7c806ad3a1cdcc

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In Artificial Intelligence

In Developmental and Educational Psychology

Citations in the corpus

Assessing metacognitive knowledge: Development and evaluation of a test instrument (2013, unknown): “…a clear benchmark are, for example, measures for error detection or allocation of study time (Dubowy, 2010; Lockl & Schneider, 2003; Metcalfe, 2009; Pieschl, 2009), thinking-aloud protocols (Veenman, 2005) or log-ﬁ le-based analyses for computer-based learning (Azevedo, 2007); for an overview see,…”
The effects of feedback on achievement, interest and self-evaluation: the role of feedback’s perceived usefulness (2014, methodology): “…0 through .20, .40 and so forth to 1) and performance (coded as 0 = item solved incorrectly, 1 = item solved correctly) for each item, summed over all items in the pretest and posttest, respectively, and divided by the total number of pretest and posttest items, respectively (e.g., Pieschl, 2009).”
Miscalibration of self-efficacy and academic performance: Self-efficacy ≠ self-fulfilling prophecy (2019, methodology): “…used localised judgements, which are made in relation to individual items (e.g., mathematics problems) and then averaged, whereas judgements referring to a whole task (i.e., global judgements, such as for an essay or exam) are more likely to occur naturally in educational settings (Pieschl, 2009).”
Epistemological beliefs are standards for adaptive learning: a functional theory about epistemological beliefs and metacognition (2010, methodology): “COPES model in order to specify the functional relationship between epistemological beliefs, metacognition, and learning: Muis’ (2007; Muis and Franco 2009) consistency hypothesis and our calibration approach ( Pieschl 2009; Pieschl et al. 2008a, b; Stahl et al. 2006).”
Metacognition in Psychology (2019, background): “The discrepancy that sometimes occurs between metacognition and performance has not only been used to infer conscious availability but has also itself been a focus of cognitive research, for instance, on the Dunning–Kruger effect (Kruger & Dunning, 1999) and research on metacognitive calibration (Pi”
Enhancing students’ self-regulation and mathematics performance: the influence of feedback and self-evaluative standards (2010, background): “Calibration refers to the degree to which students’ judgements about their capability or performance actually represent their competence (Glenberg and Epstein 1985 ;H organ1990; Pieschl 2009; Schraw 2009).”