Integrating state-of-the-art computer technologies with pedagogically sound practice provides interesting challenges and potentially significant opportunities to simultaneously promote and examine learning in context. This paper unfolds in 3 parts. We begin by introducing the reader to contemporary theories of self-regulation. We present a 4-phase model of self-regulating and a sparse literature on instructional design for SRL. Second, we build on this theory to describe features of CoNoteS2 (a prototype electronic notebook) that support self-regulation through tacit and explicit scaffolding. And finally, we describe the role of CoNoteS2 in researching about how students self-regulate their own learning. Our intent is to illustrate how contemporary learning theory can be used to drive instructional innovation and technological enhancement for the classroom.
Self-regulated learning (SRL) refers to learners' engagement of planning, monitoring, and adapting processes to learn effectively and reach goals (Zimmerman, 1986). Jonker and colleagues (2009) proposed athletes transfer SRL from sport to school. However, athletes and non-athletes demonstrated no significant differences in academic SRL at the post-secondary level (McCardle & Hadwin, 2016). We propose that if given instruction, athletes may develop academic SRL competencies at a greater rate than non-athletes, drawing on extensive regulatory experience in their sport. Our purpose was to examine differences in athletes' and non-athletes' development of SRL across a semester of SRL instruction. To assess academic SRL, competitive athletes (n = 81) and non-athletes (n = 187) responded to the context-specific Regulation of Learning Questionnaire (McCardle & Hadwin, 2015) at three points over the course of a semester in a undergraduate course on how to self-regulate. A 2 group (competitive athletes and non-athletes) by 3 time points repeated measures multivariate analysis of variance resulted in significant main effects for group (Pillai's trace = .068, F(5, 244) = 3.58, p = .004, partial eta2 = .068), with athletes reporting less engagement across all SRL processes. A significant main effect of time was also found (Pillai's trace = .086, F(10, 239) = 2.26, p = .015, partial eta2 = .086), with an increase over time for task understanding and evaluating. The interaction was non-significant. Our hypothesis was not supported and the slight disadvantage of athletes in terms of SRL contrasts results found using a domain-general measure (Jonker et al., 2010, 2011); discussion contributes to the debate on domain-specificity of SRL competencies.Acknowledgments: This research was supported in part by SSHRC Standard Research Grant 410-2008-0700 (PI: Hadwin).
We conducted a meta-review of the computer supported collaborative learning (CSCL) literature. This literature included a rich array of methodologies, theoretical and operational definitions, and collaborative models. However, the literature lacked an overall framework for reporting important design and research details. This paper highlights key findings from our systematic review. The paper: (a) presents the array of definitions, tools, and supports researched in the CSCL literature and (b) proposes standards for reporting collaborative models, tools, and research. These standards, which have implications for both the CSCL and computersupported collaborative work areas, have potential to build a shared language upon which cross-disciplinary communication and collaboration may be based.
Background/Context Models of self-regulated learning (SRL) have increasingly acknowledged aspects of social context influence in its process; however, great diversity exists in the theoretical positioning of “social” in these models. Purpose/Objective/Research Question/Focus of Study The purpose of this review article is to introduce and contrast social aspects across three perspectives: self-regulated learning, coregulated learning, and socially shared regulation of learning. Research Design The kind of research design taken in this review paper is an analytic essay. The article contrasts self-regulated, coregulated, and socially shared regulation of learning in terms of theory, operational definition, and research approaches. Data Collection and Analysis Chapters and articles were collected through search engines (e.g., EBSCOhost, PsycINFO, PsycARTICLES, ERIC). Findings/Results Three different perspectives are summarized: self-regulation, coregulation, and socially shared regulation of learning. Conclusions/Recommendations In this article, we contrasted three different perspectives of social in each model, as well as research based on each model. In doing so, the article introduces a language for describing various bodies of work that strive to consider roles of individual and social context in the regulation of learning. We hope to provide a frame for considering multimethodological approaches to study SRL in future research.
This study examined prior knowledge and student engagement in student performance. Log data were used to explore the distribution of final grades (i.e., weak, good, excellent final grades) occurring in an elective under-graduate course. Previous research has established behavioral and agentic engagement factors contribute to academic achievement (Reeve, 2013). Hierarchical logistic regression using both prior knowledge and log data from the course revealed: (a) the weak-grades group demonstrated less behavioral engagement than the good-grades group, (b) the good-grades group demonstrated less agentic engagement than the excellent-grades group, and (c) models composed of both prior knowledge and engagement measures were more accurate than models composed of only engagement measures. Findings demonstrate students performing at different grade-levels may experience different challenges in their course engagement. This study informs our own instructional strategies and interventions to increase student success in the course and provides recommendations for other instructors to support student success.
This study examined undergraduate students' reports of emotions and emotion regulation during studying from a self-regulated learning (SRL) perspective. Participants were 111 university students enrolled in a first-year course designed to teach skills in SRL. Students reflected on their emotional experiences during goal-directed studying episodes at three times over the semester. Measures included self-evaluations of goal attainment, emotion intensity ratings and open-ended descriptions of emotion regulation strategies. Findings generally revealed that positive emotions were positive predictors and negative emotions were negative predictors of self-evaluations of goal attainment, although positive emotions were associated with larger changes in self-evaluations. Boredom was analysed separately and was found to be a positive predictor at the between-person level but not a predictor at the within-person level. Finally, students reported (a) enacting a variety of strategies to regulate their emotions and (b) using a different strategy more often than the same strategy from one study session to the next.
This poster profiles the new $750,000 Technology Integration and Evaluation (TIE) Research Lab at the University of Victoria (Canada), which has been funded by the Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Dell, Telus, Knowledge North, the University of Victoria, and SMART Technologies. The lab houses three high definition video conferencing codecs, a recording/streaming server, various SMART products, eight research workstations, mobile laptop labs and two experimental classrooms for deployment of the aforementioned equipment. This poster will showcase the current directions for research. Conversations surrounding opportunities for collaboration will be welcome.
