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Measuring Self-Regulated Learning

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Philip H Winne at Simon Fraser University
Philip H Winne
Nancy E. Perry at University of British Columbia
Nancy E. Perry
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Abstract

The topic of this chapter is the measurement of students' self-regulated learning (SRL) and its components. The first section develops key points about measurement in general. In the second section, the authors describe a model of SRL and its components that researchers seek to measure—metacognition, intrinsic motivation, strategic action. Currently used protocols are surveyed, including questionnaires, structured interviews, teacher ratings, think aloud methods, error detection tasks, trace methodologies, and observations. Finally, the authors critique these current protocols and forecast what measurements of SRL might be like in the future. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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... Metacognition, particularly metacognitive monitoring and control, is central to students' self-regulated learning (SRL; Efklides, 2011;Panadero, 2017;Winne & Perry, 2000). According to the metacognitive and affective model of SRL (MASRL; Efklides, 2011), subjective experiences, also called metacognitive feelings (such as confidence judgements), as well as emotions during learning processes, provide critical information for the regulation of learning. ...
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... Therefore, the measurement and support of students' SRL skills have become key areas of research in learning analytics. Traditionally, SRL skills can be measured by surveys or ongoing observations of students learning behaviours, which can be expensive and time-consuming [7,43]. The increasing availability of trace data -records of student interactions with online learning environments, provides significant potential to automatically assess SRL at scale. ...
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Self-regulated learning (SRL) skills are critical for effective learning and academic success. With the growing availability of trace data from students' online learning activities, researchers are increasingly leveraging this data to infer SRL processes. However, challenges remain regarding the validity of these inferences and their generalisability across diverse learning contexts. This study presents a structured approach to investigate these challenges by examining SRL behaviours in a multidisciplinary university co-hort. The dataset includes 76 baseline survey responses, over 300 daily SRL survey submissions, and more than 6,000 sequences of recorded learning actions as trace data. Using mixed linear models and sequence mining, the analysis is grounded in SRL theory and evaluated through machine learning performance metrics. Our findings indicate consistent within-person patterns of SRL and online learning behaviours, supporting the concept of transferable, holistic skill development. Additionally, the results validate the trace-based detection of SRL engagement but highlight limitations in accurately detecting planning and reflection phases. These findings underscore the potential of automating SRL engagement detection while emphasising the need for multi-modal approaches to capture the full spectrum of SRL processes comprehensively.
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... Such a lens has been applied -and extended-over the years through the work of several researchers who applied a temporal lens to capture the sequences, the transitions, and the temporal unfolding of learning events and their typical patterns [18,21,22,27]. In particular, TNA draws on Winne and Perry's framework that the learning process unfolds as transitions (i.e., contingencies) between occurrences (the actions learners take), such as from reading a passage to taking a note [43,46]. Repeated transitions form -patterns-that can be inferred and "computed using matrices that tally transitions from one SRL event to another" [43, p. 275]. ...
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... In such cases, their behaviors resemble avoidant help-seeking behaviors, but their reasons for not seeking help differ. Help-seeking is a form of self-regulated learning, and Winne and Perry (2000) suggested two ways to measure it: self-report questionnaires and behavioral observations and records. Self-report questionnaires focus on static and broad measurements of students' attitudes, whereas behavioral observations and records focus on more granular, dynamic processes. ...
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The topic of how students become self-regulated as learners has attracted researchers for decades. Initial attempts to measure self-regulated learning (SRL) using questionnaires and interviews were successful in demonstrating significant predictions of students’ academic outcomes. The present article describes the second wave of research, which has involved the development of online measures of self-regulatory processes and motivational feelings or beliefs regarding learning in authentic contexts. These innovative methods include computer traces, think-aloud protocols, diaries of studying, direct observation, and microanalyses. Although still in the formative stage of development, these online measures are providing valuable new information regarding the causal impact of SRL processes as well as raising new questions for future study.
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Full-text available
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Six features are enumerated that distinguish studying from learning in general and describe circumstances that essentially compel students to engage in complex bundles of goal-directed cognitive and motivational processes that 'get studying done.' We view these bundles as instances of metacognitively powered self-regulated learning. As a first step toward examining studying through metacognitive lenses, we present a general typology that delineates facets of academic tasks in general, including studying tasks. Then, we use this typology to characterize four distinguishable but recursively linked stages of studying: task definition, goal setting and planning, enacting study tactics and strategies, and metacognitively adapting studying. Next, we develop connections between our typology for studying and models of metacognitive monitoring, metacognitive control, and self-regulated learning. With this backdrop, we then survey select research that highlights metacognitive activities in each of the four stages of studying. Finally, we summarize our model of studying and offer suggestions for next steps in research on studying as a complex, self-regulated learning event.
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Self-regulated learning (SRL) with hypermedia environments involves a complex cycle of temporally unfolding cognitive and metacognitive processes that impact students’ learning. We present several methodological issues related to treating SRL as an event and strengths and challenges of using online trace methodologies to detect, trace, model, and foster students’ SRL processes. We first describe a scenario illustrating the complex nature of SRL processes during learning with hypermedia. We provide our theoretically driven assumptions regarding the use of several cognitive methodologies, including concurrent think aloud protocols, and provide several examples of empirical evidence regarding the advantages of treating SRL as an event. Last, we discuss challenges for measuring cognitive and metacognitive processes in the context of MetaTutor, an intelligent adaptive hypermedia learning environment. This discussion includes the roles of pedagogical agents in goal-generation, multiple representations, agent-learner dialogue, and a system's ability to detect, track, and model SRL processes during learning.
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