Addressing complexities in self-regulated learning: a focus on contextual factors, contingencies, and dynamic relations

Abstract

The conceptualization of self-regulated learning (SRL) has been profoundly articulated in recent special issues focused on the similarities and distinctions between SRL and related phenomena like self-regulation, metacognition, and motivation as well as the development of new environments and methods that improve SRL research (Alexander 2008; Azevedo 2005a, 2005b, 2007; Azevedo and Hadwin 2005; Greene and Azevedo 2010; Gress and Hadwin 2010; Jarvela and Hadwin 2013; Molenaar and Jarvela 2014; Perry 2002; Winne and Baker 2013). These special issues have examined how the affordances of (usually computer-based) learning environments can provide the means to observe phenomena described in SRL models and to empirically test theoretical assumptions (Azevedo 2005b; Azevedo and Hadwin 2005; Efklides 2011; Winne and Hadwin 2008; Winne 2010, 2011; Zimmerman and Schunk 2008, 2011). In this special issue, we tighten the focus of this ongoing discussion by focusing on three key features of SRL frameworks: contextual factors, contingencies, and dynamic relations. Contextual factors encompass features of the learning environment, the learner, and their interaction (Efklides 2011). Contingencies refer to instances where one event occurs in the context of another (Winne and Hadwin 2008; Winne 2010, 2011) – a critical assumption of process models of SRL and the basis for research on sequential and temporal characteristics of SRL (Molenaar and Jarvela 2014). Dynamic relations occur when two or more SRL processes influence one another (Winne and Hadwin 2008; Zimmerman and Schunk 2011). The contributors of this special issue are leading international researchers who examine how contextual factors influence SRL (Ben-Eliyahu and Linnenbrink-Garcia 2015; McCardle and Hadwin 2015), how contingent metacognitive processes influence achievement (Binbarasan-Tuysuzoglu and Greene 2015), and how SRL processes dynamically influence one another as learning unfolds (Bernacki et al. 2015; Lichtinger and Kaplan 2015). Two commentaries identify the cross-cutting themes of the special issue (Karabenick and Zusho 2015) and appraise the alignment between the theoretical assumptions, research designs, and Metacognition Learning (2015) 10:1–13 DOI 10.1007/s11409-015-9134-6