Self-Regulated Learning (SRL): A guide for the perplexed

Parenting Strategy for Enhancing Children’s Self-Regulated Learning

IntroductionIn the 21st century, self-regulated learning (SRL) plays a vital role in the cultivation of high-quality talent. Grounded in self-regulated learning theory and attachment theory, this study aims to systematically examine the relationship between adolescents’ SRL and academic achievement, considering the roles of academic emotions and teacher–student relationships (TSR). Additionally, the study investigates whether a recursive pathway exists from academic emotions back to SRL.MethodsThe study draws on nationwide survey data from China (N = 88,149 students) and employs structural equation modeling (SEM) to analyze the relationships among SRL, academic emotions, TSR, and academic achievement. Academic emotions were included as a mediating variable, and TSR as a moderating variable.ResultsThe results indicate that: (1) adolescents’ levels of SRL are significantly and positively associated with their academic achievement; (2) three types of academic emotions (e.g., positive high-arousal emotions) significantly mediate the relationship between SRL and academic achievement; and (3) TSR moderate both the first stage of the mediation pathway (“SRL → academic emotions → academic achievement”) and the direct effect of SRL on academic achievement.DiscussionOverall, the findings support the cognition–emotion cyclical model, suggesting that students can enhance SRL through positive academic emotions, while appropriate SRL strategies can in turn foster positive academic emotions, forming a virtuous cycle that strengthens students’ autonomy in the learning process. This study extends Pintrich’s SRL model by empirically validating a recursive loop between SRL and academic emotions. Furthermore, it highlights a “dual empowerment” path of emotional regulation and metacognitive monitoring through optimized TSR, offering vital policy insights for fostering high-order autonomous learning in the AI era.

Why do large-scale field experiments in education often have muted effects? Drawing on system dynamics and selfregulated learning theory, we sought answer this question by simulating the behavior of self-regulated (discrepancyreducing) learners over time affected by different types of educational interventions. We analyze three types of interventions: changing students’ learning rates (learning strategies), intercepts (prior knowledge or teaching effectiveness), and norms of study (achievement goals). We uncover situations where educational interventions can affect achievement in the short run, but typical cross-sectional analyses do not find a measurable effect in the long run. Results indicate that highly motivated, self-regulated learners may resist external interventions, particularly those targeting learning strategies or prior knowledge. In contrast, interventions show the greatest effect on achievement when students are under time constraints and struggling to achieve their desired performance. Ultimately, self-regulated learners may be the hardest to help, a phenomenon we call the “self-regulated learning paradox.”

Much research has been conducted on metacognition, self-regulation, and self-regulated learning, but the articles in this special issue make it clear that we still have many unanswered questions. Recommendations for research include providing clear definitions of processes, identifying relevant theories, ensuring that assessments clearly reflect processes, linking processes with academic outcomes, conducting more educational developmental research, and tying processes firmly with instructional methods. Collectively, these recommen-dations will enhance our understanding of metacognition, self-regulation, and self-regulated learning and will lead to solid implications for educational policy and practice.

The focus of education is increasingly on learners’ ability to regulate their own learning within technology-enhanced learning environments. Prior research has shown that self-regulated learning (SRL) leads to better learning performance. However, many learners struggle to productively self-regulate their learning, as they typically need to navigate the myriad of cognitive, metacognitive, and motivational processes that SRL demands. To address these challenges, the FLoRA engine is developed to help students, workers, and professionals improve their SRL skills and become productive lifelong learners. FLoRA incorporates several learning tools that are grounded in SRL theory and enhanced with learning analytics (LA), aimed at improving learners’ mastery of different SRL skills. The engine tracks learners’ SRL behaviours during a learning task and provides automated scaffolding to help learners effectively regulate their learning. The main contributions of FLoRA include (1) creating instrumentation tools that unobtrusively collect intensively sampled, fine-grained, and temporally ordered trace data about learners’ learning actions; (2) building a trace parser that uses LA and related analytical techniques (e.g., process mining) to model and understand learners’ SRL processes; and (3) providing a scaffolding module that presents analytics-based adaptive, personalized scaffolds based on students’ learning progress. The architecture and implementation of the FLoRA engine are also discussed in this paper.

Over-reliance on generative artificial intelligence (AI) for writing tasks can have negative effects on engineering education. Despite growing concerns over generative AI’s impact on authentic writing skills, limited research has critically examined alternatives that integrate between self-regulated learning (SRL) theories with knowledge visualization tools to foster monitoring and evaluation processes in engineering education. This exploratory study addresses this gap by exploring how machine learning-based text analytics can scaffold SRL, extending prior frameworks on information processing. Thirty participants were recruited from two sections of an engineering technology course. As a course task, participants wrote essays using the knowledge visualization system over a semester. SRL skills were measured through a survey, and final course grades served as a measurement of learning performance (LP). First, there were no noticeable relationships between students’ SRL, LP, and writing performance (WP). Second, regression analysis showed that SRL and course grades do not significantly predict WP. Third, engineering students’ WP significantly increased over time. Lastly, there were no differences in WP changes over time between high and low SRL or LP groups. However, there is a main effect of LP on WP, and an interaction effect of LP and time was observed in the evaluating component.

Over the past 40 years, experts in the field of educational psychology have conducted extensive research on self-regulated learning (SRL) as a learning theory. The theory of SRL refers to a learner’s ability to understand and control their learning environment through goal setting, self-monitoring, self-instruction, and self-reinforcement in order to succeed in their studies (Schraw et al., 2006). Teachers who understand the application of self-regulated learning strategies may help their students gain important insight for transferring knowledge, skills, and abilities from one field to another. Furthermore, it helps the students prepare for lifelong learning and become autonomous learners. In her recent book Self-regulated Learning and Second Language Writing: Fostering Strategic Language Learners, Lin Sophie Teng explains the principles of using SRL theory in language learning, specifically through the application of SRL to students in a second language (L2) writing class. The book offers valuable knowledge for teachers to understand how to foster integrated strategies of SRL and language learning strategies in all of their students to succeed in language learning. [First paragraph]

Ability Self-regulated learning and independent learning are needed for humans throughout their life. However, the students of SMP Sunan Ampel Porong do not yet have optimal learning independence. the study aims to improve self-regulated learning and increase the learning independence of Sunan Ampel Porong Junior High School students in online learning situations during the Covid-19, through the application of Self-Regulated Learning. This research uses experimental research, the method used is quasi-experimental research design with non-equivalent control group design. The research sample consisted of sixty-nine students of SMP Sunan Ampel Porong with two classes, the experimental class, and the control class. Data on self-regulated learning and learning independence of students were collected using observation, interviews, questionnaires, and document scrutiny techniques guidance instruments self-regulated learning. Furthermore, the data were analyzed quantitatively. Based on the post-test t-test of the ability Self Regulated Learning, it is known that the average learning outcomes of the experimental class are greater than that of the control class. From the table, it is known that the value of t count > t table. achievement scores' self-regulated learning in the experimental class and the control class. While the post-test t-test of learning independence of the experimental class is known to have an average learning outcome of the experimental class is greater than that of the control class. From the table, it is known that the value of t count > t table. It can be concluded that there are significant differences in the scores of students' independent learning outcomes in the experimental class and the control class.

From the perspective of lifelong learning, promoting adult learners' self-regulated learning by using information technology effectively is crucial important to the creation of the learning society. It is necessary to conduct in-depth study on the factors affecting the self-regulated learning of adult when using web-based learning space. This study deeply integrated technology acceptance theory and self-regulated learning theory, on the basis of which constructed an influencing factors model of self-regulated learning of adult in web-based learning space. This study selected 670 adult learners who participated in public welfare courses as the research objects, the structural equation model(SEM) analysis method is used to test and modify the model. The results show that openness to experience, risk propensity, technical support availability and external equipment accessibility can affect the self-regulated learning of adult when using web-based learning space, finally, the study put forward relevant strategies to promote the self-regulated learning of adult learners when using web-based learning space.

With the advancement of generative artificial intelligence (AI), foreign language (FL) education has stepped into a transformative new era. Despite the widespread usage of generative AI, understanding the role of self-regulation in micro-level language skills within generative AI-enhanced learning environments is inadequate. To address this gap, drawn on self-regulated learning (SRL) theory, this study explores how mobile self-regulated learning (MSRL) affects FL learners’ generative AI awareness (optimism and dependence) and morphological skills (discrimination, interpretation, and recognition) among 614 English FL learners. The results of path analysis based on partial least squares reveal that MSRL is positively associated with both dimensions of generative AI awareness and negatively associated with the three morphological skills. The mediation analysis results reveal that the dependence dimension of generative AI awareness significantly mediates the relationship between MSRL and morphological skills (interpretation and discrimination). Contrarily, optimism does not play a mediating role. This study extends SRL theory into micro-level language skills development in AI contexts and provides pedagogical implications for designing language instruction and learning platforms in the digital era.

Web 2.0 self-learning has received a lot of attention in recent years due to the advance of Internet technologies. However, students may be disoriented in Web 2.0 self-learning contexts, especially when they are incapable of regulating their own learning. In this study, we apply self-regulated learning (SRL) theory to propose a Web 2.0 self-regulated learning (Web2SRL) system. The Web2SRL system supports student performance of SRL in Web 2.0 self-learning contexts. Specifically, students use the Web2SRL system to read RSS articles acquiring knowledge from blogs of interest, in which students can use the Web2SRL system to regulate their learning, including planning, practicing, and reflecting. The results of Experiment 1 show that (1) the Web2SRL system can successfully support student acquisition of knowledge in Web 2.0 self-learning contexts, in particular in the case of low-achieving learners; (2) students perceive the Web2SRL system useful in supporting Web 2.0 self-learning. The results of Experiment 2 indicate that (1) the features of the Web2SRL system positively influence students’ use of the Web2SRL system, particularly in the case of low-achieving students; (2) the use of the Web2SRL system has a positive influence on all students’ perceptions of learning effectiveness and learning satisfaction. We thus conclude that the proposed system can play an important role in supporting learners with Web 2.0 self-learning.

Empowering learners in the second/foreign language classroom: Can self-regulated learning strategies-based writing instruction make a difference?

Self-regulation of learning has been suggested to refer to self-directed processes that help individuals learn more effectively. No instrument is available to date examining self-regulation of learning as a relatively stable individual attribute. Therefore, based on Zimmerman's self-regulated learning theory, we composed the Self-Regulation of Learning Self-Report Scale (SRL-SRS), which comprises six subscales: planning, self-monitoring, evaluation, reflection, effort and self-efficacy. This study examined the reliability and validity of the SRL-SRS. Two confirmatory factor analyses were conducted involving 601 and 600 adolescents aged 11 to 17 years (M age = 13.9, SD = 1.3). The first confirmatory factor analysis revealed that an adjusted six-factor model described the observed data and content of factors best, which was cross-validated in the second sample of adolescents. The relative and absolute test-retest reliability was satisfactory. In conclusion, this study showed that the SRL-SRS is a reliable instrument, and supported its content and construct validity.

Online academic reading assessments can test graduate students' reading ability, while the results of tests as a feedback can help students reflect on their own ability, realize their weaknesses, and then take actions to improve their ability. These behaviors form the basis of self-regulated learning ability. In this paper, a negotiated online reading assessment system is developed to provide students with the function of self-assessment and reflection. For designing the system, the authors use the concept of a negotiated learner model, so that students may negotiate with the system and try to reach an agreement. In order to explore the differences in students' learning behaviors in such a system, the authors used the hidden Markov Model to construct the behavioral model of students' self-regulated learning ability. Furthermore, the authors differentiated the negotiation behaviors of students with high and low self-regulated learning ability in the reading assessment system. The results showed that the students in the high and low self-regulated learning ability groups shared the behaviors of peer comparison and retesting. The high self-regulated learning group tended to transit among retesting, peer comparison, test reflection, and self-assessment. The students in the low self-regulated learning group tended to transit among retesting, peer comparison, viewing grades, and self-reflection. The results indicated that the students tended to re-test to negotiate with the system, and that the students in the high self-regulated learning group may reflect on learning through negotiation and further plan their learning strategies.

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.