Data used in learning analytics rarely provide strong and clear signals about how learners process content. As a result, learning as a process is not clearly described for learners or for learning scientists. Gaševic, Dawson, and Siemens (2015) urged data be sought that more straightforwardly describe processes in terms of events within learning…

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…

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…

How students develop forms for self-regulating learning is explored. It is suggested that they experiment, bootstrapping newer forms of self-regulated learning from prior forms. Obstacles are obtaining sufficient practice, remembering how learning was enacted, and reasoning about factors that affect learning. (SLD)

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…