Estimating the degree of failed understanding: a possible role for online technology

Abstract

Accurate identification of what a learner does not know is essential for efficient self-directed learning. The accuracy of this awareness, often described as calibration, has been operationalized in several ways. Calibration data are often collected in applied settings by having students predict a future examination score. This method is efficient but not a direct measurement of the awareness of specific strengths and weaknesses. Online technology allows a practical way to collect more specific, local data; that is, the accuracy of confidence ratings for individual assessment items. These two methods of estimating calibration, global and local, were contrasted as predictors of performance in an introductory college course. Both measures were demonstrated to be significant and unique predictors of future examination performance. Online study environments requiring certitude judgments for study questions and offering immediate opportunities for review may offer the means for improving the efficiency of self-directed learning.

Exploring University Students’ Justifications for Making Metacognitive Judgments of Learning

“…Moreover, research on metacomprehension judgments has shown that there was little or no relation between absolute (i.e., judgments of overall performance) and relative accuracy (i.e., discrimination of performance across items) [37]. On the other hand, Grabe and Holfeld [38] found that both measures were significant and unique predictors of future performance in an introductory college course of online study environments, while Nietfeld et al [39] found that local compared to global prediction accuracy was more strongly associated with test performance. All these mixed findings highlight the need for investigating further the nature of judgments provided at the local and global levels.…”