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This special issue presents a sample of modern work on self-regulated learning (SRL) among high ability and gifted students. It includes diverse views about the construct per se, and gifted students' and their teachers' accounts about SRL and factors they believe moderate it. Zeidner and Stroeger (this issue) set the stage with a sketch of an extensive literature about SRL that has deep roots in North American educational philosophy and practice. The menu of work here is fundamentally well done and, in varying ways and degrees, slightly provocative. A trite observation would be these articles don't fully represent the multiple facets and complex articulation among them comprising SRL, especially given relatively less research with participants identified as academically talented or gifted. In this situation, I would be pedantic to point out such-and-such is omitted or this-or-that is underrepresented. Rather, using admittedly using idiosyncratic standards, I select a few matters for discussion and, hopefully, constructive critique. Other commentators would likely apply different filters. Abbreviation: SRL = Self-regulated learning [ABSTRACT FROM AUTHOR]

Background: Deep versus surface knowledge is widely discussed by educational practitioners. A corresponding construct, levels of processing, has received extensive theoretical and empirical attention in learning science and psychology. In both arenas, lower levels of information and shallower levels of processing are predicted and generally empirically demonstrated to limit knowledge learners gain, curtail what they can do with newly acquired knowledge, and shorten the life span of recently acquired knowledge. Purpose: I recapitulate major accounts of levels or depth of information and information processing to set a stage for conceptualizing, first, self‐regulated learning (SRL) from this perspective and, second, how a “levels‐sensitive” approach might be implemented in research about SRL. Method: I merge the levels construct into a model of SRL (Winne, 2011, Handbook of self‐regulation of learning and performance (pp. 15–32), New York: Routledge; Winne, 2017b, Handbook of self‐regulation of learning and performance (2nd ed.), New York: Routledge; Winne & Hadwin, 1998, Metacognition in educational theory and practice (pp. 277–304). Mahwah, NJ: Lawrence Erlbaum) conceptually and with respect to operationally defining the levels construct in the context of SRL in relation to each of the model's four phases – surveying task conditions, setting goals and planning, engaging the task, and composing major adaptations for future tasks. Select illustrations are provided for each phase of SRL. Regarding phase 3, a software system called nStudy is introduced as state‐of‐the‐art instrumentation for gathering fine‐grained, time‐stamped trace data about information learners select for processing and operations they use to process that information. Conclusions: Self‐regulated learning can be viewed through a lens of the levels construct, and operational definitions can be designed to research SRL with respect to levels. While information can be organized arbitrarily deeply, the levels construct may not be particularly useful for distinguishing among processes except in a sense that, because processes in SRL operate on information with depth, they epiphenomenally acquire characteristics of levels. Thus, SRL per se is not a deeper kind of processing. Instead, it is processing more complex – deeper – information about a different topic, namely processes for learning. [ABSTRACT FROM AUTHOR]

Abstract: Computer-supported collaborative learning (CSCL) is a dynamic and varied area of research. Ideally, tools for CSCL support and encourage solo and group learning processes and products. However, most CSCL research does not focus on supporting and sustaining the co-construction of knowledge. We identify four reasons for this situation and identify three critical resources every collaborator brings to collaborations that are underutilized in CSCL research: (a) prior knowledge, (b) information not yet transformed into knowledge that is judged relevant to the task(s) addressed in collaboration, and (c) cognitive processes used to construct these informational resources. Finally, we introduce gStudy, a software tool designed to advance research in the learning sciences. gStudy helps learners manage cognitive load so they can re-assign cognitive resources to self-, co-, and shared regulation; and it automatically and unobtrusively traces each user′s engagement with content and the means chosen for cognitively processing content, thus generating real-time performance data about processes of collaborative learning. [Copyright &y& Elsevier]

Educational psychology has generated a prolific array of findings about factors that influence and correlate with academic achievement. We review select findings from this voluminous literature and identify two domains of psychology: heuristics that describe generic relations between instructional designs and learning, which we call the psychology of "the way things are," and findings about metacognition and self-regulated learning that demonstrate learners selectively apply and change their use of those heuristics, which we call the psychology of "the way learners make things." Distinguishing these domains highlights a need to marry two approaches to research methodology: the classical approach, which we describe as snapshot, bookend, between-group experimentation; and a microgenetic approach that traces proximal cause-effect bonds over time to validate theoretical accounts of how learning generates achievements. We argue for fusing these methods to advance a validated psychology of academic achievement. [ABSTRACT FROM AUTHOR]

This article analyzes the concept of validity to set out key factors bearing on claims about validity in general and particularly regarding learning analytics. Because uses of trace data in learning analytics are increasing rapidly, specific consideration is given to reliability of trace data and their role in claiming validity for interpretations grounded on trace data. This analysis reveals the essential and inescapable role of theory in deciding what trace data should be gathered and how trace data can contribute to recommendations for improving learning, one main goal for generating and using learning analytics. • Trace data are increasingly useful in developing learning analytics. • "Raw" data are biased by the theory that recommends observing those data. • Self-regulating learners acting as agents complicate reliability of trace data. • Reliability of trace data concerns dynamic events, not static aspects of a measure. • Generalizability over facets of data sets limits on reliability and validity. [ABSTRACT FROM AUTHOR]

Abstract: This paper explores the ways three different theoretical perspectives of the social aspects of self-regulated learning [Hadwin, A. F. (2000). Building a case for self-regulating as a socially constructed phenomenon. Unpublished doctoral dissertation, Simon Fraser University, Burnaby, BC, Canada; Hadwin, A. F., & Oshige, M. (2006). Self-regulation, co-regulation, and socially-shared regulation: Examining many faces of social in models of SRL. In A. F. Hadwin, & S. Jarvela (Chairs), Socially constructed self-regulated learning: Where social and self meet in strategic regulation of learning. Symposium conducted at the Annual Meeting of the American Educational Research Association, San Francisco, CA] have been operationalized in a computer supported learning environment called gStudy. In addition to contrasting social aspects of SRL and drawing connections with specific collaborative tools and structures, this paper explores the potential of gStudy to advance theory, research, and practice. Specifically it discusses how the utilization of differing collaborative models provides new avenues for systematically researching social aspects of SRL and their roles in collaboration. [Copyright &y& Elsevier]