Knowing what you know: improving metacomprehension and calibration accuracy in digital text

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.