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Knowing what you know: improving metacomprehension and calibration accuracy in digital text
Authors:Alan J Reid  Gary R Morrison  Linda Bol
Institution:1.English Department,Coastal Carolina University,Conway,USA;2.Old Dominion University,Norfolk,USA
Abstract:This paper presents results from an experimental study that examined embedded strategy prompts in digital text and their effects on calibration and metacomprehension accuracies. A sample population of 80 college undergraduates read a digital expository text on the basics of photography. The most robust treatment (mixed) read the text, generated a summary for each page of text, and then was prompted with a metacognitive strategy. The metacognitive treatment received metacognitive strategy prompts only, and the cognitive group implemented the cognitive strategy (summarization) only. A control group read the text with no embedded support. Groups were compared on measures of achievement, attitudes, cognitive load, and metacomprehension and calibration accuracy. Results indicated that a combination of embedded cognitive and metacognitive strategies in digital text improved learner achievement on application-level questions, yielded more accurate predictive calibration, and strengthened the relationship between metacomprehension and performance, all of which are common attributes of an academically successful learner.
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