Collection 2014

WHY PROMPTING METACOGNITION IN COMPUTER-BASED LEARNING ENVIRONMENTS

Written by Loredana MIHALCA on . Posted in Volume XVIII, Nr 4

ABSTRACT

Self-regulated learning (SRL) is a central construct in education, especially in the context of the widespread use of instructional technologies. According to metacognition models (e.g., Nelson & Narens, 1994), the accuracy of monitoring plays a crucial role for effective regulation of one’s cognition, behavior, and motivation as s/he strives to achieve academic performance. In other words, being able to make accurate metacognitive judgments during learning (e.g., judge if the information was sufficiently learned) is assumed to positively affect subsequent effort and strategic behaviors (Hadwin & Webster, 2013). From this perspective, calibration between students’ judgments and their actual performance represents a metacognitive indicator for the need to regulate and adapt studying behavior (e.g., allocation of studying time; Metcalfe, 2009). Given the empirical evidence that many students have poor calibration skills (Graesser & McNamara, 2010), there is a need to prompt them to accurately monitor their metacognitive processes, which is expected to foster SRL and performance. Therefore, the purpose of this paper is to provide an overview of research on metacognitive judgments accuracy and, in particular, on how this accuracy impacts students’ self-regulation as they learn using CBLEs.

KEYWORDS: self-regulated learning, metacognitive judgments, academic performance

PAGES:299-314