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.
Self-regulated learning (SRL) is an important factor in online learning and is defined as the process of actively managing one’s own learning process to achieve a desired outcome. However, many ...issues remain unsolved about how to improve cognitive strategies and self-regulation in online learning environments where teachers are not physically present. As a result, this study aims to explore the effectiveness of a web-based virtual laboratory on enhanced students’ SRL. A quasi-experimental pre-/post-test with a control group design was employed involving 40 female students aged 14-15 years old. While the students in the experimental group carried out the practical activities using a specially developed web-based virtual laboratory, the students in the control group used a physical laboratory. The results obtained indicated that the virtual laboratory significantly enhanced metacognitive self-regulation, effort regulation, peer learning, and overall SRL more than the physical laboratory. These findings could be attributed to how students learn using the virtual laboratory. For instance, students can navigate the virtual lab website at their own pace at anytime and anywhere.
Self‐regulated learning (SRL)‐promoting practices enhance students' positive academic, social, and emotional development. While effective, these practices are complex and often difficult for teacher ...candidates (TCs) to learn and implement. This theoretical review presents the benefits and challenges of SRL‐promoting practices and examines how TCs' beliefs about SRL are implicated in their development and implementation of these practices. Conditions within teacher education programs that attend to TCs' beliefs about these practices are examined and suggestions are provided for further research in the area of TCs' beliefs and their development of teaching practices that promote SRL.
Practitioner points
Self‐regulated learning (SRL)‐promoting practices foster positive social, emotional, and academic student outcomes.
Attending to teacher candidates' (TCs) beliefs about SRL within teacher education programs fosters TCs' development of these practices.
TCs' self‐efficacy to implement SRL practices is fostered through the provision of activities that promote metacognitive thought.
The ability to acquire knowledge and skills through systematic and deliberate monitoring of one's behavior is not evenly distributed among each student. Quantitative research using quasi-experimental ...methods aims to findthe effect of PBL on self-regulated learning, the effect of PBL on self-regulated learning based on gender, and to identify self-regulated learning for high school students based on indicators. The research design was a Posttest Only Control-Group Design carried out on Class XI students at a high school in Palu City with a population of 7 classes. Self-regulated learning data were obtained through questionnaires that were distributed to the experimental and control classes after learning was complete. Data were analyzed using an independent t-test and analysis of each indicator of self-regulated learning using the percentage formula. The study finds that self-evaluation is the most important indicator of self-regulated learning and that learning using the PBL model has a significant impact on student self-regulated learning based on gender and in comparison to conventional learning.It is recommended to apply the PBL model to learning in schools in Indonesia in particular to increase the ability of each student to carry out independent learning.
Recent developments in educational technologies have provided a viable solution to the challenges associated with scaling personalised feedback to students. However, there is currently little ...empirical evidence about the impact such scaled feedback has on student learning progress and study behaviour. This paper presents the findings of a study that looked at the impact of a learning analytics (LA)-based feedback system on students' self-regulated learning and academic achievement in a large, first-year undergraduate course. Using the COPES model of self-regulated learning (SRL), we analysed the learning operations of students, by way of log data from the learning management system and e-book, as well as the products of SRL, namely, performance on course assessments, from three years of course offerings. The latest course offering involved an intervention condition that made use of an educational technology to provide LA-based process feedback. Propensity score matching was employed to match a control group to the student cohort enrolled in the latest course offering, creating two equal-sized groups of students who received the feedback (the experimental group) and those who did not (the control group). Growth mixture modelling and mixed between-within ANOVA were also employed to identify differences in the patterns of online self-regulated learning operations over the course of the semester. The results showed that the experimental group showed significantly different patterns in their learning operations and performed better in terms of final grades. Moreover, there was no difference in the effect of feedback on final grades among students with different prior academic achievement scores, indicating that the LA-based feedback deployed in this course is able to support students’ learning, regardless of prior academic standing.
•A learning analytics-based system was used to deliver process feedback to students in a course.•The learning-analytics feedback employed multimodal data, such as log data from the learning management system and e-book.•The pattern of self-regulated learning differed between students who had received the feedback, and those who had not.•Final course marks were significantly higher for students who had received the feedback, compared to those who had not.•There was no difference in impact of the LA-based, process feedback among students with different program entry scores.
The goal of this study was to investigate 65 students' evidence scores of emotions while they engaged in cognitive and metacognitive self-regulated learning processes as they learned about the ...circulatory system with MetaTutor, a hypermedia-based intelligent tutoring system. We coded for the accuracy of detecting students’ cognitive and metacognitive processes, and examined how the computed scores related to mean evidence scores of emotions and overall learning. Results indicated that mean evidence score of surprise negatively predicted the accuracy of making a metacognitive judgment, and mean evidence score of frustration positively predicted the accuracy of taking notes, a cognitive learning strategy. These results have implications for understanding the beneficial role of negative emotions during learning with advanced learning technologies. Future directions include providing students with feedback about the benefits of both positive and negative emotions during learning and how to regulate specific emotions to ensure the most effective learning experience with advanced learning technologies.
•Mean evidence score of contempt was significantly lower than joy or anger.•Mean evidence score of anger was significantly higher than frustration.•Emotions correlated with accuracy scores, but not proportional learning gain.•Surprise negatively predicted accuracy score of feeling of knowing.•Frustration positively predicted accuracy score of notes.
The regulation of motivation is considered a key aspect of self-regulated learning (SRL) as it is presumed that maintaining an adequate level of motivation is essential for engagement, effort and ...persistence in academic tasks. In this review, we aimed to improve our understanding of motivational regulation strategies, their supposed antecedents and the educational implications. A search was conducted in Web of Science, Scopus, PsycInfo, and ERIC databases. Of 4027 records identified, 64 (75 studies) were deemed eligible after inclusion and exclusion criteria were applied and studies with low methodological quality were discarded. Data on 18 different motivational regulation strategies were available. Extrinsic/controlling types of strategies were reported to be used more frequently than intrinsic/autonomous strategies. Motivational regulation strategies were significantly associated with metamotivational beliefs, academic skills and adjustment. Available evidence supports assumptions of theoretical models on antecedents and academic implications of motivational self-regulation.
The present study provides a compendium of the different motivational self-regulation strategies studied to date, describes the nature of these and unifies the different denominations used. The available evidence on the frequency of use of the different strategies that has been collected may be useful for educators, enabling them to anticipate and adapt to the status of the different motivational facets in students. Drawing on theoretical models of motivational self-regulation, the interconnections between the use of the strategies and their supposed antecedents and the expected educational implications were explored. This will provide researchers and educators with an interpretive framework to draw upon when adapting to interindividual diversity in strategy use and when assessing the compatibility between educational practices and the efficient use and training of motivational strategies.
•Regulation of academic motivation is a crucial component of self-regulated learning.•The present study provides a compendium of the different motivational self-regulation strategies studied to date.•It is necessary to design educational proposals focused on the use of specific motivational self-regulation strategies.
This study aimed to validate a newly-developed instrument, The Writing Strategies for Self-Regulated Learning (SRL) Questionnaire, with respect to its multifaceted structure of SRL strategies in ...English as a foreign language (EFL) writing. A total of 790 undergraduate students from 6 universities in Northeast China volunteered to be participants. Confirmatory factor analyses (CFA) through structural equation modeling (SEM) were applied to evaluate 3 hypothesized models. The results of the CFA validated a 9-factor correlated model of second language (L2) writing strategies for SRL with satisfactory psychometric characteristics. Model comparisons confirmed a hierarchical, multidimensional structure of SRL as the best model, in which self-regulation, as a higher order construct, accounted for the correlations of the 9 lower-order writing strategies, pertaining to cognitive, metacognitive, social-behavioral, and motivational regulation aspects. Multiple regression analysis revealed that 6 out of 9 SRL strategies had significant predictive effects on EFL writing proficiency. The empirical evidence lends preliminary support to a transfer of SRL theory from educational psychology to the field of L2/EFL education, particularly L2/EFL writing. Implications of these findings are discussed.
Self‐regulated learning (SRL) is an essential skill to achieve one's learning goals. This is particularly true for online learning environments (OLEs) where the support system is often limited ...compared to a traditional classroom setting. Likewise, existing research has found that learners often struggle to adapt their behaviour to the self‐regulatory demands of OLEs. Even so, existing SRL analysis tools have limited utility for real‐time or individualised support of a learner's SRL strategy during a study session. Accordingly, we explore a reinforcement learning based approach to learning optimal SRL strategies for a specific learning task. Specifically, we utilise the sequences of SRL processes acted by 44 participants, and their assessment scores for a prescribed learning task, in a purpose‐built OLE to develop a long short‐term memory (LSTM) network based reward function. This is used to train a reinforcement learning agent to find the optimal sequence of SRL processes for the learning task. Our findings indicate that the developed agents were able to effectively select SRL processes so as to maximise a prescribed learning goal as measured by predicted assessment score and predicted knowledge gains. The contributions of this work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualised SRL focused scaffolding.
Practitioner notes
What is already known about this topic
Learners often fail to adequately adapt their behavior to the self‐regulatory demands of e‐Learning environments.
In order to promote effective Self‐regulated learning (SRL) capabilities, researchers and educators need tools that are able to analyze and diagnose a learner's SRL strategy use.
Current methods for SRL analysis are more often descriptive as opposed to prescriptive and have limited utility for real‐time analysis or support of a learner's SRL behavior.
What this paper adds
This paper proposes the use of Reinforcement Learning for prescriptive analytics of SRL. We train a Reinforcement Learning agent on sequences of SRL processes acted by learners in order to learn the optimal SRL strategy for a given learning task.
Implications for practice and/or policy
Our work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualized SRL focused scaffolding.
The implications of our work can aid in course design by predicting the self‐regulatory load imposed by a given task.
The ability to model SRL strategies using Reinforcement Learning can be extended to simulate or test SRL theories.
A core focus of self‐regulated learning (SRL) research lies in uncovering methods to empower learners within digital learning environments. As digital technologies continue to evolve during the ...current hype of artificial intelligence (AI) in education, the theoretical, empirical and methodological nuances to support SRL are emerging and offering new ways for adaptive support and guidance for learners. Such affordances offer a unique opportunity for personalised learning experiences, including adaptive interventions. Exploring the application of adaptivity to enhance SRL is an important and emerging area of research that requires further attention. This editorial introduces the contributions of seven papers for the special section on adaptive support for SRL within digital learning environments. These papers explore various themes related to enhancing SRL strategies through technological interventions, offering valuable insights and paving the way for future advancements in this dynamic area.
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