Measuring component and sets of cognitive processes in self-regulated learning.

This study investigates secondary education students' self-regulated learning (SRL) processes with digital trace data, particularly whether SRL processes found in secondary education are comparable to those observed in higher education. We therefore adapted a digital learning environment and rule-based AI algorithm originally designed to measure SRL in higher education and collected multi-trace data from 13-year-old students ( N = 179) across three European countries during an essay-writing task. Hidden Markov modeling was employed to capture latent SRL processes. Four latent SRL processes emerged: orientation, first-reading, writing, and re-reading combined with monitoring. By clustering sequences of these latent SRL processes, we identified four sequential patterns of SRL processes at the task level: writing with metacognitive monitoring, writing intensively, reading first, writing next, and reading and writing simultaneously. Our findings highlight how AI and multi-trace data can be used to measure SRL during learning, providing a basis for enhancing personalized support. Self-regulated learning (SRL) is vital in the digital world. In this study, we investigated secondary education students' SRL processes with digital trace data. We also demonstrated that the instrumentation of a digital learning environment and rule-based AI algorithm originally designed to measure SRL processes in higher education can be leveraged to measure SRL processes in secondary education students. The real-time measurement of covert SRL processes is important as this information can 1) raise students' awareness on their learning, 2) help teachers to support students' learning, and 3) form the basis for providing personalized support for these processes with the help of AI-enhanced learning technologies. • Collected multi-trace data from 13-year-old students during an essay-writing task. • Found four SRL processes: orientation, reading, writing, re-reading with monitoring. • Identified four sequential patterns of SRL processes at the task level. • Students' task knowledge was associated with SRL at the task level.

Self-regulated learning (SRL) process is temporally bounded and unfolds over time. Achieving insights into temporal dynamics in SRL processing, however, requires not only fine-grained data collection and methods but also modeling approaches that accurately account for the sequential structure of the events during the SRL process. Addressing this, we investigate an understudied aspect of SRL theory: how temporal SRL processing, as captured by multimodal traces, differs between mastery and progressing groups in the context of technology-enhanced biology learning in a lab setting. We examine count-based, duration-based, and sequence-based models of SRL processing to unveil diverse dimensions of the process across different achievement levels. Specifically, for the sequence-based model, we employed a multi-method approach, incorporating epistemic network analysis (ENA) to analyze the temporal and sequential dynamics of multimodal events during SRL processing and then qualitative analysis to delve deeper into those dynamics. Our study offered valuable methodological contributions by empirically comparing different methods for modeling temporality and by utilizing a novel analytic approach (i.e., ENA) to model the temporal dynamics of SRL processes. Additionally, it makes theoretical contributions to advancing the understanding of the dynamic interplay between SRL processing and academic performance, thereby informing targeted interventions to support students’ learning processes, ultimately enhancing their learning outcomes in technology-enhanced STEM education. Future researchers can leverage these contributions to advance models of the dynamic and temporal nature of SRL.

Researchers, especially in the field of educational psychology, have argued that self-efficacy plays an important role in self-regulated learning. As such, teaching of self-regulated learning often focuses on enhancing self-efficacy. However, few studies have examined how the process of self-regulated learning might lead to the enhancement of self-efficacy. Therefore, the aim of this study was to investigate the effects of the self-regulated vocabulary learning process on self-efficacy. A total of 303 learners of English as a Foreign Language from two universities participated in the study. They completed a questionnaire measuring the process of self-regulated learning (i.e. forethought, performance or volitional control, and self-reflection). They also completed a vocabulary test as a measure of learning outcome. Correlation analysis was employed to evaluate the relationships between various variables investigated in the study. The results showed that self-regulated learning process can boost self-efficacy and increase vocabulary knowledge. The pedagogical implications of the current study are discussed mainly in terms of incorporating instructions aimed at enhancing self-efficacy and self-regulation in vocabulary teaching.

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. 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. 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. 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 active process involving multiple interacting components that evolve over time, exhibiting characteristics of complex systems such as non‐linearity, emergent behaviour, self‐organization, and hierarchy. These interactions unfold at different temporal levels, each warranting a dedicated lens to capture their distinct dynamics. In this study, we apply a complex dynamic systems lens to analyse the longitudinal dynamics of SRL. We map how different SRL processes interact with each other across time and scales: (1) the stable between‐person level, which represents the dominant approach to learning or roughly the trait of SRL, (2) the contemporaneous level, which maps how SRL processes influence each other within the same time and (3) the temporal level, which captures how processes predict or influence each other in the future. Data were collected through a weekly survey administered over 4 weeks in five courses at two institutions, complemented by LMS behavioural engagement data. A panel vector autoregression model was employed to examine the structure and dynamics of SRL and LMS behavioural engagement at the three levels. The findings suggest that central SRL processes, such as planning and adapting, take place in separate stages, in accordance with the classic SRL models, whereas other processes, like effort regulation, are more pervasive, co‐occurring with most other regulatory processes. At the aggregate level, adjusting was the most central process that drove students' SRL. As such, our results align with the main characteristics of complex systems, including non‐linearity and hierarchy. These findings have implications for the design of SRL interventions, where effort can benefit from real‐time prompts, whereas metacognitive processes might require long‐term scaffolding. Furthermore, the weak association between LMS engagement and SRL processes across all levels highlights the limitations of relying solely on behavioural trace data to infer regulation. Practitioner notes What is already known about this topic? Self‐regulated learning (SRL) is an important driver of academic success and can be influenced through targeted interventions. Most SRL research is based on group‐level data, often using static, cross‐sectional designs that overlook temporal dynamics. Recent work has highlighted that SRL can and should be modelled as a complex dynamic system. What this paper adds? There is evidence of complex systems characteristics in SRL such as hierarchy, non‐linearity and feedback loops. SRL processes follow distinct temporal phases, with some processes persisting throughout all phases. Metacognition is the most central process at the between‐person level, whereas effort is central at the within‐person level. LMS behavioural data is weakly linked to self‐reported SRL. Implications for practice and/or policy SRL interventions should consider how regulatory processes unfold over time, rather than treating SRL as a static trait. Interventions targeting effort regulation and metacognition have the potential to be the most consequential. Caution must be exerted when using average or between‐person data to inform individualized support. LMS metrics should be interpreted with care and ideally complemented by self‐report or observational data.

Learning sciences are embracing the significant role technologies can play to better detect, diagnose, and act upon self-regulated learning (SRL). The field of SRL is challenged with the measurement of SRL processes to advance our understanding of how multimodal data can unobtrusively capture learners' cognitive, metacognitive, affective, and motivational states over time, tasks, domains, and contexts. This paper introduces a self-regulated learning processes, multimodal data, and analysis (SMA) grid and maps joint and individual research of the authors (63 papers) over the last five years onto the grid. This shows how multimodal data streams were used to investigate SRL processes. The two-dimensional space on the SMA grid is helpful for visualizing the relations and possible combinations between the data streams and how the measurement of SRL processes. This overview serves as an analytical introduction to the current special issue “Advancing SRL Research with Artificial Intelligence (AI)” and we encourage to position new research and unexplored frontiers. We emphasize the need for intensive and strategic collaboration to accelerate progress using new interdisciplinary methods to develop accurate measurement of SRL in educational technologies.

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

Measuring self-regulated learning (SRL) skills of engineering college students while using enhanced guided notes (EGN) promotes a better understanding of how students deal with notetaking activities. This study focused on students’ task interpretation, cognitive strategies, and SRL processes including planning, monitoring, and regulating strategies while using EGN in an electric circuits course. The main objectives of this study were to (1) understand how students’ SRL skills changed after using EGN; and (2) evaluate how students’ conceptual understanding on electric circuits improved after using EGN. Ninety-seven students enrolled in the Fundamental Electronics for Engineers course at a land grant university participated in the study. A self-regulated learning survey instrument developed using Butler and Cartier’s SRL model was used to capture students’ task interpretation, cognitive strategies, and SRL processes. In addition, Holton’s DC/AC Circuits Concept Inventory was used to measure students’ conceptual understanding of DC/AC concepts. Participants were asked to complete both surveys twice online, at the beginning and end of the semester. Descriptive statistics, independent and paired t-tests, and a cluster analysis technique were used to analyze data. Data analysis of the SRL survey revealed that students had different SRL profiles. Students in the improved group reported a significant improvement in planning, monitoring, and regulating strategies. On the other hand, those in the declined group showed a lower awareness of all SRL skills at the end of semester. Furthermore, the findings revealed an improvement in students’ conceptual understanding. This article will also discuss the potential implications for electric circuit concepts instruction.

Using student interviews, teacher ratings, and achievement test outcomes, we validated a strategy model of student self-regulated learning as a theoretical construct. Forty-four male and 36 female high school students were asked to describe their use of 14 self-regulated learning strategies in six contexts, and their teachers rated these students for their self-regulated learning during class. Factor analyses of the teachers' ratings along with students' scores on a standardized test of mathematics and English revealed a single self-regulated learning factor that accounted for nearly 80% of the explained variance and two smaller factors that were labeled Student Verbal Expressiveness and Achievement. Students' reports of using self-regulated learning strategies during a structured interview correlated .70 with the obtained teachers' rating factor and were negatively related to the Student Verbal Expressiveness and Achievement Factors. Our results indicate both convergent and discriminative validity for a self-regulated learning construct. An important new topic of research in student academic functioning has been termed self-regulated learning (Corno, 1986; Henderson, 1986; McCombs, 1986; Schunk, 1986; Wang & Peverly, 1986). Although interest in this topic has diverse theoretical origins, a common conceptualization of students has emerged as metacognitively, motivationally, and behaviorally active participants in their own learning process (Zimmerman, 1986). In terms of metacognitive processes, self-regulated learners plan, organize, self-instruct, and selfevaluate at various stages during the acquisition process. From a motivational vantage, self-regulated learners perceive themselves as self-efficacious, autonomous, and intrinsically motivated. In terms of behavior, self-regulated learners select, structure, and even create social and physical environments that optimize acquisition. According to this view, effective learners become aware of functional relations between their patterns of thought and action (often termed strategies) and social and environmental outcomes. There is a growing research literature indicating the importance of students' use of self-regulate d learning strategies. Recently Zimmerman and Martinez-Pons (1986) proposed a model that comprised 14 categories of self-regulated learning strategies that high school students use during class and study. These strategies included self-evaluation (Bandura & Cervone,

The purpose of the present study was to confirm previous research findings that different categories of Web-based pedagogical tools (WBPT) (e.g., collaborative and communication tools, content creation and delivery tools) supported different self-regulated learning (SRL) processes (e.g., goal setting, self-monitoring), and to further examine which WBPT were most effective in supporting student SRL while completing course assignments. The sample surveyed consisted of 65 students enrolled in three distributed courses. A mixed methods methodological approach was implemented. As expected, quantitative analyses confirmed that different WBPT supported different SRL processes. In addition, analyses of qualitative data collected revealed that WBPT were highly effective in activating the use of SRL processes necessary to support specific types of learning tasks required for completion of course assignments. Educational implications for using WBPT as scaffolds for SRL are discussed.

Despite the global adoption of Content and Language Integrated Learning (CLIL) and extensive research on the effectiveness (product-oriented) and classroom interactions (process-oriented) of CLIL, existing studies have overlooked the motivational beliefs and metacognitive strategies of CLIL students. Hence, there is a need for a participant-oriented approach to better understand students’ perspectives and enhance CLIL’s success. Drawing on the social cognitive model of self-regulated learning (SRL), this study aimed to explore how students plan, monitor, and evaluate their learning in CLIL contexts by employing a novel vignette-based microanalytic assessment approach to empirically capture students’ SRL processes. Participants were 108 junior secondary school students from three schools in Hong Kong. Data were analysed using structuring qualitative content analysis. Findings revealed that students employed diverse strategies to manage the demands of language and content learning, with variations in self-efficacy beliefs influencing their approaches. Furthermore, some learners faced challenges in maintaining effective self-regulation. This study argues that improving self-regulatory processes and enhancing CLIL’s success requires prioritising assessment, considering the state of ‘others’, and fostering deeper learning beyond CLIL. This study contributes to the empirical, methodological, and pedagogical understanding of students’ SRL in CLIL settings and informs future research on student-focused aspects in CLIL.

Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance. In this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface-level strategies, and the connections between these SRL processes and learning outcomes across two academic domains, science and history. This was a secondary data analysis of two studies. The first study sample was 170 undergraduate students from a University in the south-eastern United States. The second study sample consisted of 40 US high school students in the same area. We collected think-aloud protocol SRL and knowledge measure data and conducted both structural equationmodelling and path analysis to investigate our research questions. Findings showed across both studies and two distinct academic domains, students who enacted more frequent monitoring also enacted more frequent deep strategies resulting in better performance on academic evaluations. These findings suggest the importance of measuring not only what depth of strategies learners use, but also the degree to which they monitor their learning. Attention to both is needed in research and practice.

Self-regulated learning (SRL) is the degree to which students are metacognitively, motivationally, and behaviourally active participants in their own learning process. It involves the self-regulation of cognitive, behavioural, and affective processes. SRL holds significant potential for enhancing practise and achievement. Although affect is acknowledged as one of the three fundamental processes in SRL, there is limited research investigating it. However, emotions have been found to influence SRL efficiency while emotion regulation (ER) can impact learning outcomes. Thus, this study sought to investigate how ER processes relate to SRL among professional musicians who perform Western classical music. Four forms of regulation (reappraisal, suppression, rumination, repression) were examined in relation to the SRL three-phase model. Professional musicians (N = 334) of 39 nationalities (age: 18–66; [M = 28]; female = 215; male = 119) completed a survey comprising the Self-Regulated Learning in Music Questionnaire, the Emotion Regulation Questionnaire, and demographic items. A significant positive correlation emerged between SRL and reappraisal, and significant negative correlations emerged between SRL and the other three processes. Further multiple linear regression analysis revealed that reappraisal, practise hours, and expertise accounted for 26% of the variance in SRL. Finally, a factorial (2 × 2 × 2) ANOVA yielded significant group differences on ER as a function of gender, expertise, and occupation. Results suggest that reappraisal can enhance the use of SRL in musicians, thus highlighting the potential utility in considering ER as part of SRL. These results suggest that by including training on emotion regulation strategies within musicians’ educational institutions and workplaces, efficiency and engagement in SRL can be enhanced. This could produce more effective learning strategies and outcomes, together with higher musical achievements.

Strategic Self‐Regulation for Speaking English as a Foreign Language: Scale Development and Validation

The competence approach in pre-school education, which recommends the promotion of self-regulated learning, raises questions about its impact on the development of children’s self-regulation. As a cross-cutting skill, self-regulated learning is essential for today’s society. It provides for a person’s ability to self-educate and develop effectively and successfully. The environment of the pre-school institution and the teacher, who equips and improves this environment, play an important role in promoting the child’s self-regulated learning. In the third stage of pre-school education children have reached the age of 5 to 6 years old and are being prepared to start school. The aim of this study is to assess and analyse children’s self-regulation skills in a pre-primary education environment in the third stage of self-regulated learning. Self-regulated learning is when a student is able to function and use cognitive, emotional processes and behavioural regulation tools to achieve learning goals. The following research tasks were included: 1) analyse the essence and development of self-regulation, and guidelines for organising a self-regulated learning process; and 2) carry out pedagogical observations of children’s self-regulatory abilities within the framework of the self-regulated learning process. The research methods included analysis of pedagogical and psychological literature and sources, pedagogical observation, and statistical analysis of data. The study involved 41 children who were 5 to 6 years old. The results of the study show that self-directed learning can significantly promote the development of self-regulation skills in 5 to 6-year-old children. To better develop the process of self-regulation for 5 to 6-year-old children, the self-regulated learning process must be easier to understand, with an emphasis on updating, understanding, and reflecting on the learned content, while also clearly articulating the expected outcomes and providing feedback.