Metacognition, Self-Regulation, and Self-Regulated Learning: Research Recommendations

Parenting Strategy for Enhancing Children’s Self-Regulated 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.

The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self‐regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self‐determination theory (SDT). In this study, we examine the mediating effects of needs satisfaction in SDT on the relationship between students' previous technical (AI) and disciplinary (English) knowledge and SRL, using an AI conversational chatbot. Data were collected from 323 9th Grade students through a questionnaire and a test. The students completed an AI basic unit and then learned English with a conversational chatbot for 5 days. Confidence intervals were calculated to investigate the mediating effects. We found that students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot, and that satisfying the need for autonomy and competence mediated the relationships between both knowledge (AI and English) and SRL, but relatedness did not. The self‐directed nature of SRL requires heavy cognitive learning and satisfying the need for autonomy and competence may more effectively engage young children in this type of learning. The findings also revealed that current chatbot technologies may not benefit students with relatively lower levels of English proficiency. We suggest that teachers can use conversational chatbots for knowledge consolidation purposes, but not in SRL explorations. Practitioner notes What is already known about this topic Artificial intelligence (AI) technologies can potentially support students' self‐regulated learning (SRL) of disciplinary knowledge through chatbots. Needs satisfaction in Self‐determination theory (SDT) can explain the directive process required for SRL. Technical and disciplinary knowledge would affect SRL with technologies. What this paper adds This study examines the mediating effects of needs satisfaction in SDT on the relationship between students' previous AI (technical) and English (disciplinary) knowledge and SRL, using an AI conversational chatbot. Students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot. Autonomy and competence were mediators, but relatedness was not. Implications for practice and/or policy Teachers should use chatbots for knowledge consolidation rather than exploration. Teachers should support students' competence and autonomy, as these were found to be the factors that directly predicted SRL. School leaders and teacher educators should include the mediating effects of needs satisfaction in professional development programmes for digital education.

Foreword ( Wilbert J. McKeachie). 1. Introduction: Self-Regulation of Learning in Postsecondary Education (Hefer Bembenutty) The chapter introduces this volume on self-regulation of learning and highlights the current trends on self-regulation presented by each of the authors. 2. Purpose of Engagement in Academic Self-Regulation (Einat Lichtinger, Avi Kaplan) This chapter argues that self-regulated learning is not a unitary construct, with students different purposes of engagement in the task meaningfully distinguishing between different types of self-regulation. 3. Self-Regulation and Achievement Goals in the College Classroom (Akane Zusho, Kelcey Edwards) This chapter links self-regulation of learning with achievement goal theory and offers practical tips to educators who struggle with disinterested learners. 4. Understanding and Facilitating Self-Regulated Help Seeking (Stuart A. Karabenick, Myron H. Dembo) The authors describe interventions to develop the competencies and resources that facilitate student help seeking as an effective selfregulated learning strategy. 5. Self-Regulation and Learning Strategies (Claire Ellen Weinstein, Taylor W. Acee, JaeHak Jung) This chapter reviews research on learning strategies, describes a model of strategic and self-regulated learning, and reports on instructional methods, interventions, and assessments that instructors can use to help students develop lifelong learning strategies they need to succeed in college. 6. Academic Delay of Gratifi cation and Academic Achievement (Hefer Bembenutty) This chapter provides a review of research on delay of gratifi cation and suggests ways in which educators can instill in students to delay gratifi cation. 7. Resistance and Disidentifi cation in Refl ective Practice with Preservice Teaching Interns (Michael Middleton, Eleanor Abrams, Jayson Seaman) This chapter examines case studies to identify contextual factors in teacher education programs that promote or inhibit teaching interns' self-refl ective practices. 8. Professional Development Needs and Practices Among Educators and School Psychologists (Timothy J. Cleary) This chapter summarizes research illustrating the importance and need for motivation and self-regulation professional development training for teachers and school psychologists across assessment, intervention, and instructional activities. 9. Transitioning from College Classroom to Teaching Career: Self-Regulation in Prospective Teachers (Judi Randi, Lyn Corno, Elisabeth Johnson) This chapter explores how preservice teachers prepare for the transition from college classroom to career through assignments and features of the learning environment designed to approximate the demands of work settings and job-related tasks. 10. The Role of Web 2.0 Technologies in Self-Regulated Learning (Anastasia Kitsantas, Nada Dabbagh) This chapter demonstrates how Web 2.0 technologies can be used to facilitate self-regulated learning in postsecondary education. The authors show how instructors can integrate social software into course design to promote students self-regulation of learning. 11. Self-Regulation of Learning with Computer-Based Learning Environments (Jeffrey A. Greene, Daniel C. Moos, Roger Azevedo) This chapter outlines how self-regulated learning skills can facilitate learning with computer-based learning environments and how educators can diagnose and build on students' self-regulated learning ability. 12. New Directions for Self-Regulation of Learning in Postsecondary Education (Hefer Bembenutty) This chapter puts into context the major contributions of this volume on self-regulation of learning and provides new directions for its promotion in postsecondary education. INDEX.

Self-regulation in science learning is a critical factor influencing academic success among students. This study investigates the mediating effect of self-confidence on the relationship between metacognitive awareness and self-regulation in science learning among 235 junior high school students. A descriptive-correlational research design was employed, utilizing stratified random sampling to select participants. Data were collected through validated questionnaires measuring metacognitive awareness, self-regulation, and self-confidence, and were analyzed using descriptive statistics, Pearson’s correlation, and mediation analysis. The findings indicated that students exhibited very high levels of metacognitive awareness (M = 4.47, SD = 0.184), self-regulation (M = 4.46, SD = 0.231), and self-confidence (M = 4.53, SD = 0.202). Pearson’s correlation analysis revealed significant positive relationships between metacognitive awareness and self-regulation (r = 0.660, p < 0.001), metacognitive awareness and self-confidence (r = 0.577, p < 0.001), and self-confidence and self-regulation (r = 0.511, p < 0.001). Mediation analysis showed that self-confidence partially mediated the relationship between metacognitive awareness and self-regulation, with an indirect effect estimate of 0.141 (SE = 0.045, p = 0.002) and a mediation percentage of 17.0%. These results suggest that while metacognitive awareness has a direct impact on self-regulation, self-confidence enhances this relationship by serving as a partial mediator. The study highlights the need for educational interventions that integrate metacognitive training with confidence-building strategies to promote self-regulated learning in science. Future research may explore the effectiveness of specific instructional methods in strengthening these constructs.

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

Purpose: In the evolving landscape of the 21st century, characterized by dynamic global challenges such as health crises, climate change, and rapid technological advancements, the imperative of lifelong learning has become more pronounced than ever. Self-Regulated Learning (SRL) plays a pivotal role in lifelong learning, involving independent, self-directed behaviors to enhance knowledge and skills. While the significance of SRL is widely acknowledged in the broader context of lifelong learning, its specific role in vocational schools is a critical aspect that requires careful investigation. In these specialized educational settings, effective SRL practices are not only integral to individual academic success but are also crucial for fostering the competencies essential for a thriving professional career. However, there is a need for a systematic analysis of how SRL is fostered within vocational schools. Despite its importance, the current best practices for promoting successful SRL in vocational schools remain underexplored. Therefore, this study aimed to systematically review intervention studies focused on changing educational practices related to SRL in vocational schools. Specifically, we addressed two key research questions: (1) What are the characteristics of intervention studies aimed at fostering SRL in vocational schools? and (2) What is the quality of these interventions in terms of their impact on promoting SRL? Methods: To address these questions, we conducted a systematic review of intervention studies focused on fostering SRL in the context of vocational education. Our review includes 11 studies, with 10 reporting positive effects of interventions promoting SRL. Findings: The findings highlight that collaborative and problem-solving approaches, along with learning environments emphasizing student autonomy, are effective methods for enhancing SRL. Additionally, the review underscores the importance of distinguishing between direct and indirect interventions to promote SRL in the classroom. The effectiveness of each approach is contingent on the specific context and student population. Conclusion: However, comparing and drawing conclusions from these studies presented challenges due to the limited similarity in outcome measures and variations in the quality of evidence. Future research should consider methodological adjustments to address the heterogeneity of intervention studies on SRL at the vocational school level, including the development of standardized assessment tools and more rigorous experimental designs to better evaluate the impact of SRL interventions. These adjustments are crucial for ensuring that vocational schools can effectively integrate SRL practices that not only align with academic goals but also prepare students for the demands of their professional careers.

IntroductionSelf-regulated learning (SRL) is a multidimensional process that involves personal (cognitive and emotional), behavioral, and contextual components. Teachers, as mediators in socialization, can influence SRL in various direct and indirect ways: by instructing students on effective learning strategies and structuring the learning environment. Most teachers agree that students should be helped to become self-regulated in their learning, however, they are unsure of how to do this, which is why they encourage it to a limited degree. Therefore, the objectives of the research were: (1) to examine how much teachers know about SRL, to what degree and how they encourage it in students; (2) to examine the differences in encouraging SRL with regard to gender, seniority, type of school and status of school subjects they teach.MethodThe research was conducted using an online questionnaire at two measurement points. A total of 251/179 primary and secondary school teachers in Croatia participated in the study. To assess teaching knowledge and methods of encouraging SRL, the questionnaire employed both open-ended questions (“Provide your own subjective definition of self-regulated learning.”; “How you promote self-regulated learning in your practice? Which approaches are most effective?”) and the Self-Regulated Learning (SRL) Encouragement Scale (encouragement of learning planning and learning organization strategies, metacognitive monitoring of learning, elaboration and evaluation, understanding and effort investment).ResultsTeachers only partially know what self-regulated learning constitutes. About two-thirds of teachers have heard of the SRL, but only 14% accurately defines the construct. When defining SRL, they most often emphasize independently regulated/directed learning, and the definitions are mostly partially correct. On the other hand, teachers estimate that they encourage SRL to a significant extent. There are certain discrepancies in the data regarding the methods of encouraging SRL depending on the methodology (qualitative/quantitative). Teachers encourage SRL to a limited extent and provide implicit and indirect SRL encouragement. The results indicate that female teachers and teachers in humanities encourage SRL to a greater extent when compared to natural science and technical subject teachers, and the same goes for elective subject teachers when compared to compulsory subject teachers.DiscussionThe results have theoretical, but also great practical implications when it comes to the implementation of this multidimensional and complex construct in the educational system. They corroborate the theoretical foundations of SRL and SRL encouragement, whilst indicating the need for improving teachers’ knowledge and competencies in regard to SRL encouragement in students.

Notwithstanding the value of self-regulation, different studies evidence that self-regulated learning (SRL) is still insufficiently ingrained in schools and that teachers only rarely promote it among their students. Therefore, the current study reports on a one-year school-wide professional development programme (PDP). A quasi-experimental pre-test-post-test design was used with 10 schools randomly assigned to the experimental group (EG) and 10 schools to the control group (CG). To analyse how far the effects of the PDP reach, the impact on teachers’ SRL beliefs, self-efficacy, their SRL promotion behaviour and students’ SRL competences and achievement scores was investigated. Data were gathered from 40 fifth and sixth grade teachers (nEG = 22, nCG = 18) and their 747 upper primary school students (nEG = 448, nCG = 299). Analyses of covariance were used to study teacher-level effects, while multilevel analyses were adopted to investigate student-level effects. Results indicate that the PDP generated an increase in teachers’ SRL promotion, while no significant effect on their SRL beliefs and SRL self-efficacy could be found. Similarly, results overall indicate the absence of a desired intervention effect at the student level. Different explanations for the present results and recommendations for future research are discussed.

IntroductionIn knowledge-based work environments, workplace learning is essential for successful employee integration and long-term performance. Onboarding represents a crucial phase in which newcomers begin to acquire organizational knowledge, take on new tasks, and establish social connections. While existing research has highlighted the role of formal and informal learning formats, less is known about how different learning forms interact and how newcomers actively contribute to their onboarding by engaging in self-regulated learning behaviors.MethodsThis qualitative study investigates onboarding as a dynamic learning process, focusing on how newcomers engage in formal, informal, and self-regulated workplace learning behaviors across four content dimensions: compliance, clarification, connection, and culture. The study is based on 40 semi-structured interviews with newcomers and analyzed using qualitative content analysis.ResultsThe findings show that newcomers engage in diverse learning activities that vary in structure and learner involvement. These differences illustrate distinct patterns in observed workplace learning behaviors across the four content dimensions.DiscussionThe study contributes to onboarding and workplace learning theory by linking content dimensions to learning forms and highlighting how newcomers actively shape their onboarding experience. It challenges static models of onboarding and conceptualizes it instead as an individualized and interactive learning path shaped by both organizational structures and learner behavior. Practical implications include designing onboarding processes that combine structure with learner autonomy and recognize newcomers not only as recipients of information, but as active participants who can co-construct organizational learning through their engagement.

This study aimed to investigate the impact of self-regulated learning (SRL) skills on the academic success of non-commissioned officer (NCO) students within a Special Vocational College (SVC) belonging to the government context. As blended courses become increasingly prevalent, there is a growing interest in understanding the role of SRL, a critical determinant of academic performance. While SRL has garnered substantial attention in higher education, limited research has focused on its relevance within SVCs. Furthermore, little is known about the factors influencing students' SRL levels in online learning settings and their subsequent impact on course performance. Therefore, this study endeavors to identify the factors that influence SVC students' SRL and assess how it impacts success in blended courses. The study was conducted during the Fall semester of 2022-2023, encompassing five distinct compulsory blended courses and involving a total of 203 SVC students. We utilized a path model, expanding upon the framework proposed by Liaw and Huang in 2013, to assess the influence of SRL on academic achievement. The findings demonstrated that students' perceptions of the interactivity in the learning environment (ILE) and their perceived self-efficacy (PSE) had a positive and significant impact on their perceived satisfaction (PS). Additionally, ILE, PSE, and PS significantly influenced the perceived usefulness (PU) of the learning management system (LMS). Furthermore, ILE, PU, and PS emerged as significant predictors of students' SRL skills. However, the observed results did not entirely align with the expected impact on academic success. The study's findings shed light on the complex relationship between course achievement and SRL in blended learning settings, as well as the interrelated factors at play. The practical implications of these findings extend to how LMS administrators can support students in blended courses in becoming more self-regulated learners.

Literature on teacher learning has shown links between being a self-regulated learner, reflecting effectively on one’s own practice, and being described as an “adaptive expert”. For instance, the metacognitive skills needed for effective reflection on teaching practice are seen as critically important to developing adaptive expertise in the context of the highly complex classroom environment. Similarly, self-regulated learning is often defined, at least in part, in terms of using metacognitive skill to adapt one’s approach to complex learning situations or problems. Although there is rich literature on reflective practice in teacher education, less is known about measuring teachers’ self-regulated learning or the relationship between self-regulated learning and teacher reflections. This research examines reflective practice and self-regulated learning through pre-service teachers’ written reflections. The study makes a novel adaptation of a rubric designed to evaluate teacher education candidates’ reflections to measure self-regulated learning. Findings suggest that the rubric could also be useful in understanding the self-regulated practices of teacher education candidates.

Examining teacher candidates’ self-determined motivation to develop self-regulated learning promoting practices

Self-regulated learning (SRL) is essential to professional learning and practice across disciplines. However, the literature provides limited insights into how medical educators could leverage the SRL framework to support trainees' strategic processes in clinical reasoning activities. In this study, we investigated the relationship between SRL competency and clinical reasoning tendency as 64 medical students diagnosed a virtual patient in a computer-simulated environment. We further examined whether students with different profiles of SRL competency and clinical reasoning tendency differed in their behavioral patterns and performance. The results suggested that SRL competency positively predicted clinical reasoning tendency. Enhancing medical students' SRL competency, especially their self-reflection skills, could increase the tendency toward relying on an analytic approach to clinical reasoning. Moreover, we identified two groups of students (i.e., analytic SRL learners, and non-analytic, low SRL learners) using K-means clustering analysis. The two groups of students differed in their behavioral patterns in clinical reasoning, as revealed by lag sequential analysis. Furthermore, analytic SRL learners ordered more relevant lab tests than non-analytic low SRL learners in clinical reasoning. This study has methodological and practical implications.

This study examines the impact of AI-based learning applications on students' self-regulated learning using the Personalized Learning method. The research employs an experimental design with two groups: the experimental group using AI-based applications and the control group following traditional learning methods. Data were collected through questionnaires, interviews, and learning outcome tests. The results show that AI-based applications significantly enhance students' self-regulated learning and academic performance compared to traditional learning. The use of this application has proven effective in supporting self-regulated learning and improving students' academic outcomes.