The self-regulated learning paradox: Or, one reason why educational interventions might fail

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.

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.

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.

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.

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]

Achievement goal orientation significantly impacts both the motivation and self-regulated learning of students. Learners who adopt mastery goals concentrate on personal development, thereby improving their self-regulation (Çetinkalp & Türksoy, 2011; Wangid, 2022). Conversely, performance goals may induce anxiety and impede motivation and self-regulation [Hamim & Rahmawati, 2022). This study investigates the moderating role of achievement goal orientation in the relationship between self-determined motivation and self-regulated learning among Kuwaiti students learning English as a Foreign Language (EFL). Using a quantitative research design, survey data were collected from (431) EFL students enrolled in Kuwait Authority for Applied Education and Training (PAAET). Structural equation modeling (SEM) was employed to test the hypothesized relationships and the moderation effect. The results show that achievement goal orientation significantly predicts self-regulated learning (? = 0.370, p < 0.01) and self-determined motivation (? = 0.500, p < 0.01). Among goal orientations, mastery-approach goals (? = 0.261, p < 0.001) and performance-avoidance goals (? = 0.123, p < 0.05) positively predict self-regulated learning, while mastery-avoidance and performance-approach goals do not (p > 0.05). Strong correlations are found between self-determined motivation and its components, including competence (r = 0.870), autonomy (r = 0.863), and relatedness (r = 0.777). Additionally, self-determined motivation and self-regulated learning are moderately correlated (r = 0.370, p < 0.01), indicating that motivated learners are more likely to adopt better regularity practices. Interaction analysis shows that high achievement goal orientation enhances the relationship between self-regulated motivation and self-regulated learning. These results underscore the importance of fostering mastery-oriented goals in EFL settings to maximize students' self-regulated learning. Received: 4 March 2025 / Accepted: 28 April 2025 / Published: 06 May 2025

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.

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.

This study examines the relationship between self-regulated learning (SRL) strategies and English language mastery among senior high school students. Specifically, it investigates the overall effect of SRL on English achievement and identifies which specific SRL strategies serve as significant predictors of language proficiency. Using an adapted questionnaire from Magno (2010), SRL levels were measured among 400 students from grades XI and XII across Science and Social streams at X Senior High School, with a final sample size of 196 participants. Findings indicate that students demonstrated a high level of self-regulation in their learning, reflected in a mean SRL score of 2.83, and attained strong English achievement outcomes, with a mean score of 84.08. Statistical analysis confirms a significant effect of SRL on English achievement. However, the presence of heteroscedasticity prevented the application of multiple regression analysis, thereby limiting the identification of the strongest SRL predictors of English success. These results underscore the role of self-regulated learning in shaping academic achievement and highlight the need for further research into refining predictive models to better understand the impact of specific SRL strategies on language proficiency. Keywords: self-regulated, learning strategies, English mastery

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.

Adaptability regulates individuals' cognitive, behavioural and emotional responses to new, unexpected and uncertain situations, but to date no study has analysed whether adaptability contemporarily favours cognitive, behavioural and emotional aspects of learning. This study aims to address this gap by examining (i) the direct relations between adaptability and achievement emotions, self-regulated learning strategies and academic self-efficacy and (ii) the direct and indirect relations between adaptability and academic achievement and life satisfaction through and over the other study-related factors. A total of 1083 students (415 males, Mage = 13.37, SDage = 1.97, age range=10-18) in grades 6-12 participated to the study. Questionnaires were used to measure students' adaptability, positive and negative achievement emotions, self-regulated learning strategies, academic self-efficacy and life satisfaction. Schools provided grades obtained by each student at the end of the academic year. A path analysis based on 1083 students (10-18 years old) confirmed that adaptability directly relates to the three study-related factors considered and to life satisfaction and indirectly relates to academic achievement and life satisfaction-through the mediation of the other variables. The results, discussed in accordance with the self-regulated learning theory, enlarge the nomological framework of adaptability and highlight its importance for emotional, behavioural and cognitive aspects of self-regulated learning.

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.

BackgroundLifelong learning is integral to providing safe and effective healthcare. This requires effective strategies for self-regulated learning (SRL). Problem-based learning (PBL) is one recognized method for fostering SRL skills. While self-assessment is known to promote SRL, its effectiveness within PBL tutorials, especially for new medical students, remains underexplored. Given the established evidence of self-assessment’s impact on SRL, this study examines its role specifically within PBL to explore whether it enhances self-regulatory processes in novice learners.MethodsUsing a post-positivist approach, a multi-method study was conducted, with Zimmerman’s SRL framework as a theoretical lens. A correlation analysis assessed the relationship between self-assessment and SRL. A pre-test post-test interventional study without a control group measured changes in SRL following implementation of self-assessment. Hierarchical multiple regression analysis was conducted to assess whether self-assessment predict students’ self-regulated learning improvement. The intervention used a previously validated tool the “Self-assessment Scale for Active learning and Critical Thinking (SSACT).” Additionally, qualitative methods explored the impact of self-assessment on SRL skills in the PBL tutorial context.ResultsOur findings indicate a positive correlation between self-assessment and SRL, suggesting a potential dynamic relationship between the two constructs. The students’ motivation after implementation of self-assessment is significantly increased. Self-assessment played a crucial role on the development of students’ self-regulated learning. Qualitative analysis reveals that self-assessment contributes to heightened self-awareness, goal orientation, and effective task planning among medical students in PBL tutorials. Furthermore, the provision of external feedback and revision opportunities enhanced students’ self-regulation in the learning process.ConclusionThis study sheds light on the promising role of self-assessment in augmenting SRL skills among novice medical students within PBL settings. The findings underscore the potential of self-assessment as a valuable tool to cultivate self-regulation. By bridging the gap in understanding the impact of self-assessment on SRL within PBL, this research not only contributes valuable insights but also informs the design of effective educational interventions. The use of a validated self-assessment tool can empower medical learners to develop the crucial skill of self-regulation, thereby enhancing their overall educational experience.

This introductory paper to the special issue of High Ability Studies aims to provide a “guide for the perplexed” relating to self-regulated learning (SRL) theory, research, and applications. We begin by defining SRL and its key cyclical stages and criterial attributes. We move on to discuss a number of motivational and meta-motivational constructs supporting SRL. We then briefly present a number of issues related to teaching and promoting SRL. Finally, we review research shedding light on SRL in gifted, high ability, and high achieving students.

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.