Calibration concerns (a) the deviation of a person's 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 Hadwin's [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 Winne's [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.