Images in computer-supported learning: Increasing their benefits for metacomprehension through judgments of learning

Images are widely used in computer-based learning although they might bias learners' judgments on how well they have mastered the material, which might reduce the effectiveness of metacognitive learning control. As that bias seems to result from primarily theory-based processing, we used two-step judgments of learning to induce more experience-based processes that we hypothesized to benefit metacomprehension accuracy. In Experiment 1, 381 participants studied a ten-section text on team building that was either accompanied by conceptual images, decorative images, or no images. Half of the participants made simple judgments by rating after each section the likelihood of correctly answering a knowledge question on that section (judgment of learning; JOL) The other half made combined judgments by rating text difficulty before making a JOL. As postulated, combined JOL benefitted accuracy and knowledge test scores; both were highest in the conceptual images group. In Experiment 2, to further increase accuracy, we manipulated judgment scope. Rather than predicting answers correct for an entire chapter, another 310 participants had to predict answers correct for a specific concept from a chapter. Again, accuracy and test scores were highest in the conceptual images group. Contrary to expectations, however, JOL accuracy did not benefit from term-specific judgments. We discuss implications for research into metacomprehension processes in computer-supported learning and for adaptive learner support based on judgment prompts. Tested if judgments of difficulty prior to judgments of learning increased the benefit of images for metacomprehension.We proposed that such combined judgements would promote experience-based processing.Metacomprehension accuracy was boosted by 75% under combined judgments.Moreover, knowledge test performance was significantly higher with combined judgments.