Self-regulated Learning Research Research Articles

Bridging Cognitive Load and Self-Regulated Learning Research: A complementary approach to contemporary issues in educational research

The aim of this Introduction to the Special Issue ‘Bridging Cognitive Load and Self-Regulated Learning Research’ is to explore how cognitive load theory, which is particularly relevant for how learners deal with complex information, and self-regulated learning theory, which is particularly relevant for how learners use information to monitor and control their learning, can be combined into one joint research paradigm that is relevant for contemporary and future developments in education. The first two sections introduce cognitive load theory and self-regulated learning theory. The third section discusses the main similarities and differences between the theories, and describes how the cue-utilization framework can be used as the basis for a joint research paradigm. The main idea postulated is that new instructional methods should help learners identify diagnostic cues in available information that provide an accurate indication of where learners stand in relation to criterion task performance. Use of these diagnostic cues when monitoring learning will lead to better regulation of learning activities and of mental resources allocated, and thus to more efficient learning and higher learning outcomes.In the fourth section, the six studies and two commentaries presented in this special issue are positioned within this paradigm. In the fifth and final section, a common research agenda based on the joint CLT-SRL paradigm is sketched and its relevance for future developments is explained. The studies presented in this special issue and the two commentaries, which complete the Special Issue, should be seen as a very first step in executing this research agenda.

Online Self-Regulated Learning Questionnaire in a Russian MOOC

The learning opportunities that Massive Open Online Courses (MOOCs) promote urge more research in self-regulated learning to enhance student engagement. Since self-regulation can be determined by the Online Self-Regulating Learning Questionnaire, this research assessed its adaptation, using the modified kappa coefficient and the Content Validity Indexing (CVI) in the Russian context. Clarity, cultural equivalence and relevance of the instrument were classified by seven experts. Forty-five students confirmed the ease of understanding in a pre-test. At scale-level index, the “Goal Setting” value was below the adequate level. Three items were considered unacceptable, and one of them was identified as a potentially problematic item. Confirmatory factor analysis showed some problems in the adjustment of the questionnaire model to the data. Self-regulated learning skills of Russian engineering students in MOOCs are moderate, with high levels in terms of “Environment Structuring” and “Goal Setting”, but low in “Help Seeking”. Self-regulated learning variables indicated no gender differences.

The Trajectory of Scholarship about Self-Regulated Learning

The trajectory of scholarship about self-regulated learning (SRL) originates in mid-19th-century writings about learners’ sense of responsibility in self education. Although Descartes's 17th-century writings implied mental activities consistent with metacognition, a central feature of SRL, these were inarticulate until Flavell and colleagues’ studies circa 1970. Since then, research on metacognition and its role in SRL has approximately doubled every decade. Foundations for modeling SRL include Skinner's behaviorism, which acknowledged learners’ choices about reinforcers for behavior, and Bandura's social learning theory, with its construct of agency. Research in the 1980s gathered data about SRL mainly using interviews, self-report questionnaires, and think-aloud protocols. These methods were quickly supplemented by observations of behavior and traces of learning activities tightly coupled to features of SRL. Today, SRL research is prominent across a broad spectrum of educational topics. Its importance will grow with trends toward lifelong learning and self-directed inquiries that survey vast information on the Internet, where students control what and how they will learn. Implications for future research include reconceptualizing “error variance” as arising partially due to SRL and capitalizing on software technologies that massively increase access to data about how and to what effects learners self-regulate learning.

The Latent Change Score Model: A More Flexible Approach to Modeling Time in Self-Regulated Learning

This research advances understanding of empirical time modeling techniques in self-regulated learning research. We intuitively explain several such methods by situating their use in the extant literature. Further, we note key statistical and inferential assumptions of each method while making clear the inferential consequences of inattention to such assumptions. Using a population model derived from a recent large-scale review of the training and work learning literature, we employ a Monte Carlo simulation fitting six variations of linear mixed models, seven variations of latent common factor models, and a single latent change score model to 1500 simulated datasets. The latent change score model outperformed all six of the linear mixed models and all seven of the latent common factor models with respect to (1) estimation precision of the average learner improvement, (2) correctly rejecting a false null hypothesis about such average improvement, and (3) correctly failing to reject true null hypothesis about between-learner differences (i.e., random slopes) in average improvement. The latent change score model is a more flexible method of modeling time in self-regulated learning research, particularly for learner processes consistent with twenty-first-century workplaces. Consequently, defaulting to linear mixed or latent common factor modeling methods may have adverse inferential consequences for better understanding self-regulated learning in twenty-first-century work. Ours is the first study to critically, rigorously, and empirically evaluate self-regulated learning modeling methods and to provide a more flexible alternative consistent with modern self-regulated learning knowledge.

The use of self-regulation strategies by foreign language learners and its role in language achievement

Studies show that incorporating self-regulated learning (SRL) strategies into foreign language teaching encourages the development of autonomous learners. However, interviews with teachers ( n = 51) indicate that they mostly do not consider SRL in classroom practices. The present study attempts to highlight the significance of SRL in language teaching by exploring its impact on language achievement. It investigates learner reported use of SRL, focusing on its three main components – orientation, performance, and evaluation – and their power in predicting foreign language achievement. A total of 222 undergraduate foreign language learners at a state university participated in the study. Data was collected from two sources: a five-point Likert-type self-regulated language learning questionnaire, adapted from models and research instruments used in previous studies to investigate SRL and language learning strategies, and the university’s English achievement exam. Quantitative analyses indicated that although participants reported moderate to low levels of SRL use, it is a significant predictor of foreign language achievement and had significant correlations with language achievement. The results are meant to draw attention to the importance of SRL research within the foreign language teaching field as well as foster SRL implementation in language instruction.

How research on self-regulated learning can advance computer supported collaborative learning / El papel de la investigación sobre aprendizaje autorregulado en el desarrollo del aprendizaje colaborativo asistido por ordenador

Learning in and for the twenty-first century requires motivational competence and effective learning strategies in individual and collaborative learning settings. Being able to strategically regulate one’s own learning and that of others is a vital and increasingly important skill. Most learners are not equipped to regulate and direct their own learning or might lack the motivation to do so. Additionally, if self-regulation of learning is difficult at the individual level, it becomes even more difficult when interacting with peers and in teams, known as co-regulation and shared regulation. Fortunately, recent research has shown that regulation can be both learned and also often supported with self-regulation tools and/or environments. This paper discusses recent trends in self-regulated learning research, focusing especially on regulating learning in computer supported collaborative learning. It is concluded that today’s education should help learners become aware of their strengths and weaknesses in a learning situation, so as to help them develop skills and strategies to continue to learn throughout their lives.

A Framework for Evaluating and Enhancing Alignment in Self-Regulated Learning Research.

We discuss the articles of this special issue with reference to an important yet previously only implicit dimension of study quality: alignment across the theoretical and methodological decisions that collectively define an approach to self-regulated learning. Integrating and extending work by leaders in the field, we propose a framework for evaluating alignment in the way self-regulated learning research is both conducted and reported. Within this framework, the special issue articles provide a springboard for discussing methodological promises and pitfalls of increasingly sophisticated research on the dynamic, contingent, and contextualized features of self-regulated learning.

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

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

Using multiple, contextualized data sources to measure learners’ perceptions of their self-regulated learning

As theory and research in self-regulated learning (SRL) advance, debate continues about how to measure SRL as strategic, fine-grained, dynamic adaptations learners make during and between study sessions. Recognizing learners’ perceptions are critical to the strategic adaptations they make during studying, this research examined the unique contributions of self-report data for understanding regulation as it develops over time. Data included (a) scores on the Regulation of Learning Questionnaire (RLQ) completed in the first and last few weeks of a 13-week course and (b) diary-like Weekly Reflections completed over 11 weeks. Participants were 263 undergraduate students in a course about SRL. First, exploratory factor analysis resulted in a five-factor model of the RLQ with factors labeled Task Understanding, Goal Setting, Monitoring, Evaluating, and Adapting. Second, latent class analysis of Time 1 and 2 RLQ scores revealed four classes: emergent regulators, moderate regulators, high regulators with emergent adapting, and high regulators. Finally, in-depth qualitative analysis of Weekly Reflections resulted in group SRL profiles based on a sub-sample of participants from each RLQ class. Qualitatively, these groups were labeled: unengaged regulators, active regulators, struggling regulators, and emergent regulators. Quantitative and qualitative SRL profiles were juxtaposed and similarities and differences discussed. This paper explicates and discusses the critical importance of sampling self-reports of SRL over time and tasks particularly in contexts where regulation is developing.

Student motivations as predictors of high-level cognitions in project-based classrooms

It is well established that active learning helps students engage in high-level thinking strategies and develop improved cognitive skills. Motivation and self-regulated learning research, however, illustrates that cognitive engagement is an effortful process that is related to students’ valuing of the learning tasks, adoption of internalized goal orientations, and development of personal agency. In this study, we test these relationships by generating multiple regression models that use motivational variables to predict college students’ use of elaboration and critical thinking in project-based courses. We find that elaboration strategies are significantly and positively predicted in regression models based on task value and intrinsic goal orientation or on task value and self-efficacy. Regression analyses illustrate that models based on task value and intrinsic goal orientation, in combination with either self-efficacy or extrinsic goal orientation, described students’ use of critical thinking strategies. These findings support prior research that shows that student motivations are significantly tied to their use of cognitive strategies. The findings extend the existing research base by illustrating the extent to which student motivations may be used to predict cognitive engagement in active, project-based environments.

Response to Vassallo's claims from a historically situated view of self-regulated learning as adaptation in the face of challenge

This article is a response to Vassallo's claim that potentially problematic assumptions about social class are implicit in contemporary self-regulated learning research and practice. Rather than refuting the claim directly, this paper offers an elaborated view of the historically situated nature of self-regulated learning as adaptation in the face of challenge. This perspective acknowledges the critical importance of varied life experiences as opportunities for developing self-regulated learning processes both inside and outside of school.

Investigating how college students’ task definitions and plans relate to self-regulated learning processing and understanding of a complex science topic

Winne and Hadwin (2008) identified four phases of self-regulated learning (SRL) including defining the task, setting goals and making plans, studying (i.e., learning), and adaptation. The vast majority of SRL research has focused on processing during the third phase, studying. In this study, we developed coding rubrics that allowed us to examine how the results of college students’ processing during phases one and two of Winne and Hadwin’s model, task definitions and plans, affected their SRL processing while learning with a hypermedia learning environment (HLE), and their subsequent academic performance. Our findings showed that, on average, participants gained both factual knowledge and integrated conceptual understanding over the course of learning with the HLE. The quality of participants’ task definitions and plans at pretest, as well as their SRL processing during learning, were related to learning outcomes. On average, participants’ task definitions improved from pretest to posttest. The findings from this study and the associated coding methodologies have implications for educational research and practice.

Revisiting the Motivated Strategies for Learning Questionnaire

The Motivated Strategies for Learning Questionnaire (MSLQ) has dominated self-regulated learning research since the early 1990s. In this study, the two MSLQ subscales specifically designed to assess self-regulation—Metacognitive Self-Regulation subscale and Effort Regulation subscale—were examined. Results indicated that the structure of the two scales is not supported by the original data reported by Pintrich, Smith, Garcia, and McKeachie in 1991 or new data. Statistical and theoretical analyses supported two modified scales, the General Strategies for Learning scale and the Clarification Strategies for Learning scale, that assess academic self-regulation from the original MSLQ items. The statistical and theoretical analyses, results, and modified scales are discussed.

MOTIVATIONAL BELIEFS AND LEARNING STRATEGIES IN ORGANIC CHEMISTRY

Students enter college chemistry courses with different sources of motivation, appropriate or inappropriate assumptions about their probability of success and how to study. This study is theoretically aligned with self-regulated learning research. Clearly, academic performance is closely related to student motivational beliefs and learning strategies. This study investigated the motivational beliefs and learning strategies of 2 years of college students in the second semester of organic chemistry. Responses to the Motivational Beliefs and Learning Strategies Questionnaire indicated that student self-efficacy was highly correlated with academic performance (semester grades). Gender differences were quite pronounced. Male academic performance was associated with intrinsic motivation as well as the importance placed on the learning task. Test anxiety was negatively associated with male grades. Extrinsic motivation was negatively correlated with female grades. Responses to students’ sense of control over learning, the value of the learning task, and self-efficacy were significantly higher for males compared to females. Faculty who attend to these different patterns may influence beliefs as well as learning strategies. Correcting erroneous assumptions about how to learn chemistry may help students shift both their attitudes and their learning practices. The notable gender difference suggests that female chemistry students may especially profit from focused faculty intervention.

How does help seeking help? – New prospects in a variety of contexts

The editors of this special section have focused on a theme that is both old and new. Research on help seeking has a long history and a strong theory pointing out that help seeking is an important academic strategy of self-regulation that facilitates learning. It is a new topic to consider in the context of broadened learning environments, increased opportunities provided by technology and multiple forms of social interactions available in new learning environments. I’ll consider the special section from a self-regulated learning research perspective and focus my remarks on three themes: (a) “How does help seeking help?”, (b) “adaptiveness of strategic help seeking”, and (c) “help seeking in pairs and teams”.

Turning the Kaleidoscope: What We See When Self-Regulated Learning is Viewed With a Qualitative Lens

This article argues for using multiple methods to investigate self-regulated learning because only by using a range of methodologies will we be able to appreciate fully its complexity. The article focuses on 2 aspects that are afforded by the use of observations and interviews in self-regulated learning research: (a) providing rich, contextualized description that can answer "what, how, why, and when" questions, and (b) enabling methodological triangulation. This article discusses these with reference to the ongoing mixed-method research of middle-grade urban students' engagement in project-based science. The article also reflects on issues and questions that have evolved throughout this research.

Modeling the Mediating Role of Volition in the Learning Process

According to action-control theory, volition plays a mediating role between the intention to learn (motivation) and goal-directed behavior (the use of learning strategies). Although extensive theoretical work has been done to document this flow of events, more empirical studies have been needed to identify the specific means by which volitional control protects the intention to learn and maintains the attempts to learn; our intention here was to address this gap in the literature. Using data from a sample of 487 college students in two different domains, we found that the positive effects of intrinsic goal orientation and self-efficacy on cognitive engagement were augmented by volitional control. We also found that the effects of volition differed by domain as well as by the type of learning strategy being considered. These results suggest that volitional control merits greater attention from those doing research in self-regulated learning.