When learners highlight or tag content, they metacognitively monitor information to select and mark it. From a levels-of-processing framework, standards used in metacognitive monitoring could affect learning. We examined effects on recall and transfer of different metacognitive standards -- free highlighting expressing a generic…

In theoretical models of self-regulated learning, calibration is one important component in successful learning. Two issues of calibration are explored. First, Nelson (1987) suggested the "G" (gamma) coefficient is the most appropriate measure of calibration (judgment accuracy) and rejected signal detection theory's "d'"…

Articles in this special issue present recent advances in using state-of-the-art software systems that gather data with which to examine and measure features of learning and particularly self-regulated learning (SRL). Despite important advances, there remain challenges. I examine key features of SRL and how they are measured using common tools. I…