Students’ calibration of knowledge and learning processes: Implications for designing powerful software learning environments

Calibration concerns (a) the deviation of a personʹs judgment from fact, introducing notions of bias and accuracy; and metric issues regarding (b) the validity of cues’ contributions to judgments and (c) the grain size of cues. Miscalibration hinders self-regulated learning (SRL). Considering calibration in the context of Winne and Hadwinʹs [Winne, P.H., & Hadwin, A.F. (1998). Studying as self-regulated learning. In D.J. Hacker, J. Dunlosky, & A.C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah, NJ: Erlbaum.] SRL model and Winneʹs [Winne, P.H. (2001). Self-regulated learning viewed from models of information processing. In B.J. Zimmerman & D.H. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed., pp. 153–189). Mahwah, NJ: Erlbaum] learning tasks model, I describe software-supported research that mines naturalistic data to explore calibration of study tactics and that develops measures sensitive to individual differences in calibration. I suggest four research-based principles for enhancing SRL: delay metacognitive monitoring, summarize content, select seminal information for review, and provide more effective practice tests.