Self-Regulated Learning

Theory · All theories
Evidence · 221 claims (101 for, 112 mixed, 8 against) · 165 studies (52 associational, 43 causal, 25 review, 17 quant-synthesis, 15 theoretical, 8 qualitative, 5 design), q1–q4 · 39 of 165 report an effect size · 205 claims rest on one study

Description

Self-Regulated Learning (SRL) explains learning as a cyclical process in which learners set goals, choose strategies, monitor progress, and reflect on outcomes in order to improve later performance. Rather than treating learning as passive reception, SRL emphasizes that effective learners actively manage attention, effort, strategy use, and feedback across time.

Barry Zimmerman's work is one of the most influential formulations of SRL. In that model, learners move through forethought, performance, and self-reflection phases. Goal setting, strategy selection, self-monitoring, and self-evaluation are therefore not add-ons to learning; they are central mechanisms that shape whether learning efforts become effective.

A second, complementary formulation comes from clinical and addiction-behavior research: building on Kanfer's foundational work (Kanfer, 1970a, 1970b), Miller and Brown (1991; Brown, 1998) proposed a seven-step model in which self-regulation can falter at any of seven points: (1) receiving relevant information, (2) evaluating it against a norm, (3) triggering the decision to change, (4) searching for options, (5) formulating a plan, (6) implementing the plan, and (7) assessing the plan's effectiveness — which recycles back to steps 1-2. Unlike Zimmerman's three-phase cycle, this model is explicitly diagnostic: a scored instrument, the Self-Regulation Questionnaire (SRQ) (Brown, Miller, & Lawendowski, 1999), lets a practitioner identify which specific step in the cycle is failing for a given person rather than treating self-regulation as a single undifferentiated capacity.

Implications

Context

• Strong fit where learners must plan, monitor, and revise over extended tasks
• Especially relevant in independent study, writing, problem solving, exam preparation, and online learning
• Often requires instructional supports such as explicit goals, feedback, prompts, and reflection routines before learners can regulate effectively on their own

Target Learners

• Learners developing independence and metacognitive control
• Adults and adolescents managing long-term goals, deadlines, and self-study demands
• Novices who need support learning how to learn, not just what to learn

Target Learning Objectives

• Goal setting and strategic planning
• Monitoring understanding and performance during work
• Adjusting strategy in response to feedback or error
• Building persistence and more accurate self-evaluation

Claims

• Self-regulated learning models treat self-monitoring as a core process through which learners notice gaps and adjust strategy [+M]
• Process goals lead to better skill acquisition for novices than outcome goals. [+M]
• Specific, difficult goals lead to higher performance than easy or vague "do your best" goals. [+M]
• Argumentation quality is associated with denser co-occurrence of scientific reasoning and self-regulation processes [~M]
• Early ability to delay gratification predicts later academic and social outcomes, but the association is weaker and more context-dependent than originally reported. [~M] — self-control/delay of gratification is closely related to self-regulation, though the relationship to later outcomes is more context-dependent than the classic marshmallow-test framing suggests
• Students' SRL profile under automated scoring was uneven, and only Execution-and-Monitoring was significantly associated with score gains [+M] — attached 2026-10-10 from Chen C et al. (2026), which proposed "Zimmerman's three-phase cyclical SRL model as a lens for locating which phases automated feedback reaches".
• Interviews show students used scores to trigger action but reflected shallowly, derived planning from previous scores, and diverged emotionally [+W] — attached 2026-10-10 from Chen C et al. (2026), which proposed "Zimmerman's three-phase cyclical SRL model as a lens for locating which phases automated feedback reaches".
• ENA revealed high CT students had significantly more coherent SRL cognitive networks spanning planning, execution, and self-reflection than low CT students [+W] — attached 2026-10-10 from Shu Zhao et al. (2026), which proposed "SRL framework (planning, execution, self-reflection) as a lens for CT-based cognitive regulation in AI-assisted coding".
• High CT students significantly exceeded low CT students in task strategies, elaboration, and self-assessment during AICA interactions [+W] — attached 2026-10-10 from Shu Zhao et al. (2026), which proposed "SRL framework (planning, execution, self-reflection) as a lens for CT-based cognitive regulation in AI-assisted coding".
• Metacognitive self-regulation mediates the relationships between all three AI interaction mechanisms and both higher-order outcomes [+W] — attached 2026-10-10 from Davor I et al. (2026), which proposed "Process-oriented framework of AI interaction mechanisms with metacognitive self-regulation as the central explanatory pathway".
• Reflective and hybrid feedback outperform direct GenAI feedback on delayed AI-free transfer [~W] — attached 2026-10-10 from Ates (2026), which proposed "Process model of GenAI-supported feedback: designs distribute evaluative work and act directly and indirectly through uptake and self-regulated learning".
• Within-session help-seeking development predicts unaided post-test performance beyond pre-test performance and static AI-use features [+W] — attached 2026-10-10 from Abdelghani et al. (2026), which proposed "Epistemic proactivity (EP): the extent to which learners retain responsibility for regulating and advancing their own knowledge-building while using AI"; tests this page's relationship.
• Within a single LLM session, students' help-seeking shifts toward answer- and verification-seeking while explicit self-regulation modestly increases [~W] — attached 2026-10-10 from Abdelghani et al. (2026), which proposed "Epistemic proactivity (EP): the extent to which learners retain responsibility for regulating and advancing their own knowledge-building while using AI".
• GenAI-supported SRL in L2 writing has a six-factor latent structure spanning cognitive, metacognitive, motivational, affective, social-behavioral, and environmental regulation [+W] — attached 2026-10-11 from Xiaoqi Wang et al. (2026), which proposed "GenAI-SRL framework: six interacting regulatory dimensions for GenAI-mediated L2 writing".
• All six GenAI-SRL dimensions correlate positively with learners' perceived writing improvement (r = 0.275–0.338) [~M] — attached 2026-10-11 from Xiaoqi Wang et al. (2026), which proposed "GenAI-SRL framework: six interacting regulatory dimensions for GenAI-mediated L2 writing".
• Teachers reported using AI within planning-monitoring-evaluation-revision cycles interpreted as teacher-level self-regulation [+W] — attached 2026-10-11 from Hochberg N et al. (2026), which proposed "Dual-layer relationship: teacher reflective regulation and critical AI literacy mediate learner-facing agency and capability design".
• During AI-tutor interactions, students' turns were dominated by help requests with near-absent monitoring and evaluation [-W] — attached 2026-10-11 from Rania Abdelghani et al. (2026), which proposed "Turn-level coding framework combining SRL, help-seeking, and mathematical modeling codes with LLM-specific constructs of epistemic vigilance and agency over the AI".
• Socratic feedback is associated with higher task orientation and comprehension monitoring than directive feedback; on feedback regulation it has no main effect, but its effect depends on agent type [~M] — attached 2026-10-12 from Han Xue et al. (2026), which proposed "Multidimensional framework for examining AI-generated feedback in self-directed learning".
• Reaching the qualitative SOLO phase in GenAI interaction required repeated meta-level learner interventions, demanding the very self-regulatory skills the tool was expected to support [+W] — attached 2026-10-12 from Linus Brunnström et al. (2026), which proposed "AI-interaction literacy: the capacity to steer, evaluate, and learn from iterative GenAI interaction".
• AI self-efficacy and self-regulated learning mediate the support–competence associations in opposite patterns for the two supports [~W] — attached 2026-10-12 from Liu Xu et al. (2026), which proposed "Dual-drive dual-mediation model of AI support and pre-service teachers' innovative competence".
• In ASR-assisted college English speaking instruction, corrective feedback and reflection tasks each significantly predict reflective behavior [~M]
• BEAGLE's HIGH and LOW performer profiles differentiate by SRL strategy: HIGH performers allocate 72% of steps to PLANNING and MONITORING while LOW performers spend 50.8% trapped in ENACTING [+W]
• Epistemic network analysis shows metacognitive behaviors co-occur with invitation actions in DR and DE but with human-initiated reasoning and elaboration in CI [~W]
• Effort regulation negatively predicts, and general academic help seeking positively predicts, reported AI dependency; time management is non-significant [~W]
• GenAI use weakens effort adjustment to incentives on subsequent unaided reasoning [-W]
• Self-regulated learning ability significantly moderates Gen-AI's effect on HOT: high-SRL students show ES = 0.863 versus ES = 0.284 for low-SRL students [+W]
• Within the bounded condition, stronger coded reflective engagement predicted higher Week 8 independent higher-order thinking [+W]
• Within-session SRL change shows no detectable association with unaided post-test performance [~W]
• The four profiles differ in academic self-efficacy, time/study management, and effort regulation, with HL highest, but not in help seeking [~W]
• Scenarios depict AI systems monitoring and scaffolding students' socially shared regulation of learning during collaborative lessons [+W]
• Reflective behavior and learning motivation each significantly predict oral proficiency improvement in ASR-assisted instruction [~M]
• Adult learners expect AI to enhance relevance through real-time goal monitoring, goal breakdown, career companionship, and networking recommendations [~W]
• Academic emotions—enjoyment, anxiety, and boredom—mediate the association between perceived AI-adaptive environments and SRL, accounting for 54.8% of the total effect [~W]
• ASR usage frequency moderately predicts reflective behavior but does not significantly predict learning motivation [+W]
• Keystroke patterns during web-based DP exams suggested planning, revision, and sustained cognitive engagement [+W]
• Metacognitive strategies correlate strongly with problem-solving skills, and behavioural metrics correlate with task performance [+W]
• Language proficiency significantly moderates the effect of reflective behavior on oral proficiency improvement, with the effect non-significant at low proficiency [~M]
• The pooled effect of self-regulated learning does not differ significantly by school level [~W]
• Reflective thinking dimensions correlate positively with learning process/expert knowledge, and most correlate positively with learning effort and negatively with innate/fixed ability and certainty of knowledge [+W]
• Experimental-group students detected AI errors and enacted verification and ethical self-regulation as routine practices [+W]
• Repeated rapid guessing across several tests is strongly associated with students' academic self-efficacy and self-management scores [~M]
• Both the GenAI-permitted and control groups improved significantly in instructional design performance and AI readiness over the course, but not in SRL or critical thinking [-W]
• Strategy use showed no significant relationship with problems solved in B2, except writing code as prompt (r = .20), based on only twelve students [~W]
• Evidence for agentic AI in education is strongest for formative tutoring and SRL coaching, while orchestration and assessment applications remain at mid technology-readiness stages [+W]
• Forming implementation intentions produces a medium-to-large positive effect (d = .65) on goal attainment across 94 independent tests [+M] — attached 2026-10-15 from Gollwitzer et al. (2006), which proposed "Implementation intentions as if-then plans that specify the when, where, and how of goal striving in advance".
• Empirical studies support Zimmerman's cyclical and multi-level SRL models, including a linear trend favoring training more SRL phases [+W] — attached 2026-10-15 from Panadero E (2017), which proposed "Zimmerman's Cyclical Phases model of SRL: forethought, performance, self-reflection".
• The combined training-plus-diary condition improved specific SRL components: SRL knowledge, metacognitive strategies, cognitive strategies (elaboration, organisation, critical thinking), and resource management strategies (time-management, effort regulation) [+W] — attached 2026-10-15 from Broadbent et al. (2020), which proposed "Self-regulated learning as a multi-component construct with metacognitive, cognitive, and resource management strategies".
• Self-monitoring intervention effects rest on four key components: selecting a target behavior, defining it, self-assessment, and self-recording [~W] — attached 2026-10-15 from Davis et al. (2016), which proposed "Self-monitoring intervention framework for students with ASD organized around student, intervention, and setting variables".
• SRL activity only partially mediates the effect of SRL interventions on achievement in higher education [~W] — attached 2026-10-15 from Jansen et al. (2019), which proposed "Three-relations mediation framework linking SRL interventions, SRL activity, and achievement".
• ESL students taught concept mapping show significantly greater gains in self-regulation strategies (self-monitoring and knowledge acquisition strategies) than the alternative strategy group [+W] — attached 2026-10-16 from Pasana Chularut (2001), which proposed "Social cognitive theory of self-regulation linking concept mapping to self-observation, self-judgment, self-reaction, and self-efficacy".
• SRL training effects vary by outcome category, largest for metacognitive strategies (g = .40) and smallest for cognitive strategies (g = .32) [+W] — attached 2026-10-16 from Theobald (2021), which proposed "SRL strategy taxonomy: cognitive, metacognitive, and resource management strategies within a three-phase cyclical process".
• Goal setting and strategic planning positively predict attainment of personal course goals in MOOCs [+W] — attached 2026-10-16 from Kizilcec et al. (2017), which proposed "Pintrich's model of self-regulated learning as three categories of trainable strategies".
• University students who choose e-textbooks report greater self-regulation of learning than students who choose print textbooks [+W] — attached 2026-10-16 from Amanda J. Rockinson-Szapkiw et al. (2013), which proposed "Self-regulated learning theory as a framework for textbook reading at the university level".
• For first graders with weak initial self-regulation, ISI is associated with greater self-regulation gains, increasing with teachers' A2i use [~W] — attached 2026-10-17 from Connor Carol McDonald et al. (2010), which proposed "Working model of self-regulation supporting academic growth through teacher practices".
• Fall self-regulation (HTKS) scores predict spring literacy gains in first grade [~W] — attached 2026-10-17 from Connor Carol McDonald et al. (2010), which proposed "Working model of self-regulation supporting academic growth through teacher practices".
• Success in PBL requires students to set goals, monitor, reflect, and sustain motivation across the whole project, yet many students do not do these processes naturally [+W] — attached 2026-10-17 from Mary C. English (2013), which proposed "A theoretical model of the relationship between PBL and self-regulated learning".
• Open-ended planning prompts at the start of a MOOC increase course completion by 29% [+M] — attached 2026-10-17 from Planning prompts increase and forecast course completion in massive open online courses, which proposed "The intention-action gap in MOOC learning and plan-making as a cheap, effective nudge"; tests this page's relationship.
• Regulating GA proceeds via metacognitive monitoring of adequacy and accuracy plus control through outward- and inward-oriented strategies [+W] — attached 2026-10-18 from Lenka Schnaubert (2022), which proposed "GA participates in three regulatory cycles: regulating GA, regulating social interaction, and regulating partner schema building".
• Shared and co-constructed intra-group interaction were significantly correlated with deep-level metacognitive activities during pre-task regulation [+W] — attached 2026-10-18 from Kielstra et al. (2022), which proposed "Four types of intra-group social metacognitive interaction (ignored, accepted, shared, co-constructed) as a measure of sharedness in socially shared regulated learning".
• Pre-course SRL level statistically significantly predicted participation in collaborative chat activities [+W] — attached 2026-10-18 from Psathas et al. (2023), which proposed "SRL profiling as a basis for grouping, intervention targeting, and participation prediction in MOOCs".
• Adaptive AI support directs regulatory activity toward monitoring while static AI support produces strategy-focused, procedural regulation [+W] — attached 2026-10-18 from Ridwan Whitehead · Andy Nguyen · Sanna Järvelä (2026), which proposed "Trigger regulation framework for identifying multimodal SSRL processes".
• SRL phase transitions during clinical reasoning with a multimedia system are frequent, cyclical, and dominated by Forethought→Performance and Performance→Forethought transitions [+W] — attached 2026-10-18 from Cloude et al. (2025), which proposed "Extending Zimmerman and Moylan's socio-cognitive model of SRL to include metacognitive feelings (uncertainty) as a phase in the clinical-reasoning cycle"; tests this page's relationship.
• Experts verbalized fewer SRL phases overall than novices during clinical reasoning, with feelings of uncertainty almost absent among experts [~W] — attached 2026-10-18 from Cloude et al. (2025), which proposed "Extending Zimmerman and Moylan's socio-cognitive model of SRL to include metacognitive feelings (uncertainty) as a phase in the clinical-reasoning cycle".
• Continuing prompts long-term in learning journals confers no additional benefit: self-reported strategy use and self-efficacy decline across the second unit in both the short-term and long-term conditions, while the quantity of cognitive strategy use in the journals stays stable [~M] — attached 2026-10-18 from Nadja M. Gentner et al. (2024), which proposed "Zimmerman's cyclical three-phase model of self-regulated learning (forethinking, performance, self-reflection)".
• Whether an SRL component correlates with GPA depends on how it is assessed: cognition via a strategy knowledge test, motivation via questionnaire, metacognition via an open-ended microanalytic question [~W] — attached 2026-10-18 from Laura Dörrenbächer-Ulrich et al. (2021), which proposed "Three-layer model of self-regulated learning (Boekaerts): cognitive, metacognitive, and motivational regulatory layers".
• Interviewed students reported that the SRL intervention elicited self-regulated learning behaviors across goal setting, strategic planning, self-observation, and self-reflection [+W] — attached 2026-10-18 from Hengtao Tang (2025), which proposed "Zimmerman's cyclical self-regulated learning model (forethought, performance/volition, self-reflection)".
• Pre-service teachers showed stronger sequential transitions linking controlling with monitoring and planning, while in-service teachers more often followed sociocognitive challenges with monitoring [+W] — attached 2026-10-18 from Faisal Channa et al. (2026), which proposed "Socially shared regulation of learning (SSRL) as an emergent, interactionally constructed group process shaped by professional trajectories".
• Metacognitive confidence judgments and control predict English vocabulary performance from the beginning to the end of the school year in primary school children [+W] — attached 2026-10-19 from Palladino et al. (2025), which proposed "MASRL and control-value theory as a joint framework for metacognition and achievement emotions in early L2 learning".
• Teachers' and students' SRL profiles differ more than they are alike, sharing only insufficient reflection practice [+W] — attached 2026-10-19 from Avivit Arvatz et al. (2025), which proposed "The SRL&T approach: an integrated framework of self-regulated learning and teaching".
• Teachers' overall SRL level is significantly associated with their externally assessed reflection level [+W] — attached 2026-10-19 from Avivit Arvatz et al. (2025), which proposed "The SRL&T approach: an integrated framework of self-regulated learning and teaching".
• Reported planner scores interact with episode: high planners plan more at the start and less at the end of task execution [+W] — attached 2026-10-19 from Tillema et al. (2011), which proposed "Planner and reviser writing styles as temporally distinct configurations of (meta)cognitive activities".
• For effective self-regulators, mastery goals have a large positive and performance-avoidance goals a small negative effect on metacognition [+W] — attached 2026-10-19 from Anneke Vrugt et al. (2008), which proposed "Two-pathway model of effective self-regulated learning: a metacognitive pathway and a strategy pathway"; tests this page's relationship.
• Metacognition positively affects the use of all four study strategies, with medium effects on metacognitive, deep cognitive and resource management strategies and a small effect on surface strategies [+W] — attached 2026-10-19 from Anneke Vrugt et al. (2008), which proposed "Two-pathway model of effective self-regulated learning: a metacognitive pathway and a strategy pathway"; tests this page's relationship.
• When SRL is measured at a coarse grain size (global self-regulation), students are relatively well able to report on their strategy use [~M] — attached 2026-10-19 from Rovers et al. (2019), which proposed "Granularity as the organizing construct for comparing SRL measurement methods".
• The presence of motivational problems reduces reported strategies at the co- and shared-level but not at the self-level [~W] — attached 2026-10-19 from Nadine Melzner · Markus Dresel · Ingo Kollar (2022), which proposed "Three social levels of regulation in collaborative learning (self, co, shared)".
• Effort regulation and student engagement were the most consistent predictors of learning strategy use and self-reported achievement [+W] — attached 2026-10-19 from Rebecca Hey · Mark McDaniel · Flaviu A. Hodis (2024), which proposed "Self-regulation factor framework predicting learning strategy use: eagerness, vigilance, MCSR, self-control, attention control, effort regulation, and engagement".
• In low challenge dyads the metacognitive activities are closely interconnected, whereas in high challenge dyads monitoring is disconnected from other metacognitive activities [~W] — attached 2026-10-19 from Cindy Paans et al. (2019), which proposed "Socially shared regulation of learning (SSRL) and three types of regulation in collaborative settings".
• During a collaborative exam, every group member contributed to joint monitoring, and students monitored cognition far more often than behaviour or motivation and emotions. [~W]
• Students' own cognitive and metacognitive activities predict learning, off-task activity lowers metacognitive knowledge, and group mates' relational activities raise domain knowledge [+W]
• Groups perceiving a learning situation as challenging switched frequently between planning and performance phases of regulation [~W]
• Teachers' initial SRL competence profiles moderate training effectiveness: initially high-competence teachers benefit more from training [~W]
• In the case study, the student with the most coded activities scored highest on the questionnaire and the student with the fewest scored lowest, but all four over-rated Orienting & planning relative to their protocols [~W]
• Academic engagement and regulation of behavior and context show incremental validity over cognitive predictors of university GPA [+W]
• In regressions predicting self-reported achievement, elaboration positively (Beta = 0.24) and cramming negatively (Beta = -0.30) predicted achievement, but only effort regulation and engagement remained significant in the combined model [~W]
• Secondary students in CSCL devote much of their communication to regulating task performance, especially planning the task and monitoring task progress [~W]
• Effective self-regulators score significantly higher than less effective self-regulators on achievement goals, metacognition, all four study strategies and exam scores, with equal intellectual ability [+W]
• When SRL is measured at a fine grain size (specific strategies), self-reports show low calibration with behavioral measures [~W]
• Students use self-initiated strategies of verification, skill improvement, post-editing, pre-editing and reliance management to cope with GenAI challenges [+W]
• In Experiment 1, general self-assessment feedback during a learning phase led to less accurate self-assessment in a subsequent no-feedback test phase than receiving no feedback at all [-W]
• Several sophisticated metacognitive regulation activities showed little to no variance among grade 5 students during think-aloud problem solving [~W]
• In-service teachers more often followed socioemotional challenges with monitoring, and the groups differed in recurrent self-loops of controlling versus monitoring [~W]
• Students in differentiated classes grow significantly more on elaboration, critical thinking, metacognitive self-regulation and effort regulation [+W]
• Learners in the matched condition exhibited more meta-cognitive and cognitive activities—monitoring, preparation, and ending discussions—than learners in other conditions [+W]
• In-service teachers used motivational regulation significantly more frequently than pre-service teachers during collaborative problem-solving [~W]
• Lower-SES children showed greater pre-to-post gains in on-task self-regulation than higher-SES children [~W]
• In Han students, higher initial metacognitive knowledge predicted both a higher starting point and a faster growth rate of vocabulary breadth [+W]
• The choice of online versus offline measures of SRL moderates the strength of the relationship between SRL processes and achievement [~W]
• Micro-analytic self-report questions predict exam performance better than the MSLQ, which did not correlate with exam scores [~W]
• Students' strategy use formed mixed constellations: organisation and elaboration correlated strongly (r = 0.64), and effective strategies were also positively related to less effective ones such as rehearsal [~W]
• Metacognitive monitoring deficits are unlikely to be the primary source of academic performance differences between native and non-native speaking students [~W]
• During collaborative exam work, monitoring of cognition is the most common monitoring target, followed by monitoring of behavior, with monitoring of motivation or emotion scarce [~W]
• Students' monitoring judgments predict their regulation judgments before and after self-scoring [+W]
• Students' monitoring frequency during the collaborative exam was significantly and positively correlated with their number of EDA peaks. [~W]
• Monitoring of interactive metadiscourse has the strongest predictive power among the three MRIM strategies for L2 argumentative writing [~W]
• In the main study, NCC, metacognitive strategies, and intrinsic motivation moderated contribution frequency but showed no significant conditional effects on learning gains [~W]
• Students with good-quality or moderately positive profiles self-assessed more accurately after studying video modeling examples than students with poor-quality or moderately negative profiles [~W]
• Metacognitive monitoring bias during competitive Kahoots did not differ by competitive preference, though bias shifted across time [~W]
• Performance-approach goals are directly related to the use of surface cognitive and resource management strategies in effective self-regulators [~W]
• Successful hypermedia learners showed more orientation and planning before processing content, deeper elaboration, constant monitoring, and evaluation compared to less successful students [+W]
• Monitoring accuracy in exam score predictions and in key-term definition judgments during the Monitoring Exercise were uncorrelated, suggesting different cues underlie the two measures [~W]
• Exam score predictions decreased from the start to the end of the course for all performance quartiles, leaving poor performers overconfident and high performers underconfident [~W]
• Groups reacted to the large majority of monitoring events, but negative-valence monitoring of motivation or emotion received the lowest reaction rate [~W]
• High-competence-profile teachers outperform low-competence-profile teachers on SRL knowledge, beliefs, and self-efficacy, but not on self-reported situated SRL approaches [~W]
• Prior CBLEs for programming education have mostly supported SRL individually while disregarding the social context's influence on regulation [~W]
• Regulation-of-learning interventions in programming education show an average effect of 0.59 on programming learning [+W]
• Statistical discourse analysis of collaborative learning shows metacognitive planning organizes subsequent cognitive activity while relational activities help enact it [+W]
• Metacognitive scaffolding did not significantly affect group performance of collaborating triads, though the effect size indicated a small to medium positive effect [~W]
• Students' situation-specific interpretations of a learning situation as challenging or favourable affect their SRL and strategy use [+W]
• In Experiment 1, self-regulation skills significantly predicted socio-discursive MAS only in the adaptable CSCL script condition [~W]
• Novice students selecting their own tasks choose more examples than practice problems, with example selection decreasing as the learning phase progresses [~W]
• In Experiment 1, receiving self-assessment feedback during the learning phase led to less accurate task selection than no feedback, despite feedback stating actual performance [-W]
• The authors hypothesize that self-regulated learning acts as a mediating factor linking video annotation advantages to acting performance improvement [~W]
• SRL components show moderate correlations within one assessment level but no correlations across different assessment methods within one component [~W]
• Students showed significant learning gains on domain knowledge (large effect) and concept maps (medium effect) after a 45-minute SRL-rich learning task [+W]
• SRL writing-stage clusters differed in production and revision but not in pausing [~W]
• Teacher homework involvement positively predicts students' use of homework management strategies in secondary school students [+W]
• Teachers show stronger regulation than students: both select lower-JOL texts for restudy, but the JOL–restudy correlation is significantly stronger for teachers (Experiment 1). [+W]
• SRL intervention groups had significantly lower dropout than control in a MOOC, with no difference between the two intervention modes [+W]
• Students self-report help-seeking as their strongest SRL skill and reflection for action as their weakest [~W]
• Training activity scores in cognitive, metacognitive, and behavioral/environmental regulation each significantly related to subsequent exam performance after controlling for prior knowledge [+W]
• Elementary teachers more often instruct cognitive strategies than metacognitive strategies during a standardized lesson [-W]
• Adaptive help seeking is a self-regulatory behavioral strategy, distinct from dependent help seeking [+W]
• ADHD coaching helped college students with ADHD set more specific goals broken into component steps with incremental deadlines [+W]
• Academic self-regulation in 8th grade robustly predicts both GPA and school adjustment in 9th grade [~W]
• AI applications contributed to STEM students' higher-order thinking, including problem-solving, computational thinking, and self-regulated learning [+W]
• Students engaged deeply with LLM feedback, with coded evidence of reflection, rebuttal, and acceptance in the large majority of responses [+W]
• University students who choose e-textbooks report greater use of cognitive strategies than students who choose print textbooks [+W]
• Empirical studies support Zimmerman's SRL models, including expert-novice differences and phase-training effects [+W]
• Most interviewees attributed writing improvement to explicit instruction, scaffolding and collaborative learning, and self-regulated writing, though some felt uncomfortable in pair work [+W]
• Some interventions brought more improvements in self-regulation measures of those with lower baseline scores, offering promise for resource-constrained settings. [+W]
• The robotics task prompted longer group-level regulation of emotion–motivation than the conceptual physics task [~W]
• Exploratory learning is expected to raise metacognitive activity, which in turn increases strategic knowledge and adaptive transfer [+W]
• Low-prior-knowledge students struggle to deploy key self-regulatory processes during learning [+W]
• There is a lack of consensus regarding student-, intervention-, and setting-level variables supporting self-monitoring use in schools [~W]
• L1 writing metacognitive strategies transfer to L2 writing, invariant across L2 proficiency and academic major groups [+W]
• SRL training programs based on a metacognitive theoretical background show larger effects on academic achievement than programs based on cognitive theories [~W]
• Instructional approaches do not differ significantly in monitoring or reflecting, and omnibus differences in orienting and planning show no significant pairwise differences [~W]
• Metacognition is significantly positively correlated with math performance in adolescence (pooled r = .37) [+W]
• No group differences appear in self-regulation, strategies, or motivation across the four mathematics achievement groups [~W]
• Adequacy of regulation decisions did not depend on misconceptions or instructional method [~W]
• Case analysis shows regulation strategies did not always align with the challenges faced, leading to recurring difficulties within the group [-W]
• Physiological synchrony among collaborating students related only to cognitive regulation, while motivational regulation changes related to achievement [~W]
• Self-assessment accuracy in PBL depends on achievement level: low achievers graded generously, high achievers accurately [~W]
• At scale level, the questionnaire's Elaboration & evaluation scale correlated significantly with the same think-aloud scale (r=.60) while the Orientation & planning scale did not (r=.24) [~W]
• Within-group analyses show significant increases on several affective and metacognitive dimensions in Scrum classes, while the comparison group showed no significant increases or decreases. [~W]
• Conceptions of self-regulated learning had small, non-significant relationships with actual reported self-regulation activities [~W]
• Self-regulation interventions showed positive associations with distal outcomes: academic achievement in 11 of 13 studies (85%), substance abuse in 4 of 5 (80%), and conduct disorders, social skills, depression, behavioral problems, and school suspensions in all reporting studies. [+M]
• The SKT-SRL shows low, mostly non-significant correlations with SRL microanalysis (online measure) [~W]
• Self-regulatory activities mediate the relationship between person, context, and achievement [+W]
• Stronger self-reported SRL skills manifest in more revisiting of previously completed course materials, especially assessments [~W]
• SRL scaffolds in computer-based learning environments produce a medium, significantly positive effect on academic performance (ES = 0.438) [+W]
• Training effects vary across specific SRL strategies, ranging from .23 (rehearsal) to .61 (attention and concentration) [+W]
• SRL interventions are effective in improving both SRL activity and achievement in higher education [+W]
• Extended SRL training programs enhance university students' academic performance, SRL strategies, and motivation (overall g = .38) [+W]
• Students use strategies to regulate their own motivation and affect [+W]
• Self-reported SRL strategies vary by individual learner characteristics including demographics, prior experience, and motivations [~W]
• SRL training programs targeting older students and students with lower prior academic achievement show larger effects on resource management strategies [~W]
• Retrospective metacognitive accuracy for strategy selection is higher in the selection-only task than the selection-execution task and on homogeneous than heterogeneous unit-digit problems [~W]
• Superficial information use with limited processing is the most used technology-based SRL strategy, while self-evaluation is among the least used [~W]
• Self-regulated learning facets that are conceptually common across three popular questionnaires appear empirically disparate [~W]
• Teachers' SRL&T implementation efficacy depended on developing SRL understanding and autonomy in designing SRL-enriched modules [~W]
• Transformative feedback attenuated the positive association between joy and regulatory behavior, and adaptive feedback moderation was otherwise limited, not supporting H2 [~W]
• Cluster analysis identifies two antagonistic student profiles differing in level of technology-supported self-regulated learning [+W]
• Learners learning with generative AI risk offloading monitoring and control of learning to the AI, suppressing learner agency [~W] — attached 2026-10-19 from Joni Lämsä et al. (2026), which proposed "Human-AI interaction as shared regulation: offloading metacognitive monitoring to AI and onloading metacognitive control to learners".
• Effort appraisals correlate negatively with monitoring judgments: high mental effort is associated with poorer performance judgments [+W] — attached 2026-10-19 from Tina Seufert (2020), which proposed "Effort Monitoring and Regulation (EMR) framework linking self-regulated learning with mental effort and cognitive load".
• Training learners with an effort-and-performance algorithm via video modeling improved monitoring accuracy, task selection accuracy, and learning outcomes [+W] — attached 2026-10-19 from Tina Seufert (2020), which proposed "Effort Monitoring and Regulation (EMR) framework linking self-regulated learning with mental effort and cognitive load".
• Hints that instruction is suboptimal trigger compensatory self-regulation, but self-management carries cognitive and motivational costs that deplete over time [~W] — attached 2026-10-19 from Tina Seufert (2020), which proposed "Self-regulation as a function of resources and imposed cognitive load: an inverted-u relationship with task difficulty".
• Patterns of SRL and SSRL behaviors among medical trainees predict electrodermal activity at distinct stages of high-fidelity simulation training [~W]
• Mind wandering can both prompt SRL and function as a regulatory strategy, and multimodal machine-learning sensing offers scalable assessment of attentive states [~W]
• Perceived teacher scaffolding is associated with more surface- and deep-level strategy use during the performance phase of SRL [+W]
• AI-directed problem selection may diminish the metacognitive dimension of self-testing while preserving retrieval practice [~W]
• The learning patterns model differs from SRL models by being multi-dimensional, non-normative, and including external regulation and lack of regulation [~W]
• Daily SR of students is positively associated with their perceived daily academic success at the within-person level [+W]
• SRL strategies in continuing education are significantly and positively related to learning performance and learner engagement, and negatively to avoidance behavior [+W] — attached 2026-10-21 from Yvonne M. Hemmler (2024), which proposed "Four-category framework of factors associated with SRL strategies in continuing education".
• Demographic and experience-related learner factors show no or only weak relationships with SRL strategies in continuing education [~W] — attached 2026-10-21 from Yvonne M. Hemmler (2024), which proposed "Four-category framework of factors associated with SRL strategies in continuing education".
• Work-related factors (organizational learning culture, job control, job demands) are significantly positively related to SRL strategies in continuing education [~W] — attached 2026-10-21 from Yvonne M. Hemmler (2024), which proposed "Four-category framework of factors associated with SRL strategies in continuing education".
• Instructional design features of CE activities (peer interaction, feedback, self-assessments, flipped classroom) are positively associated with SRL strategies [+M] — attached 2026-10-21 from Yvonne M. Hemmler (2024), which proposed "Four-category framework of factors associated with SRL strategies in continuing education".
• Operationalization of variables, setting, and work-relatedness of the CE activity moderate the relationships between SRL strategies and associated factors [~W]
• Think-aloud protocols reveal that self-regulated reading is dynamic across time, responsive to context, and varies by reading purpose [+W]
• Metacognitive and composite SRL strategy measures show the strongest relationships with learning performance in continuing education [~W]
• Monitoring accuracy interventions are less effective for secondary school students than for elementary students and adults [~W]
• Timing interventions significantly decrease monitoring accuracy in problem-solving tasks and differ from all other intervention types [-W]
• GenAI fluency corrupts metacognitive monitoring by producing cognitive ease that is misread as comprehension, weakening the regulation needed to keep offloading beneficial [~W] — attached 2026-10-21 from Daiki Nakamura (2026), which proposed "Offloading regulation: a learning-oriented dimension of AI literacy comprising partition discernment, calibrated monitoring, disciplinary verification, and re-internalization".
• SRL interventions produce large average effects on academic and motivational outcomes, per meta-analytic syntheses [~W]
• SRL intervention effects are moderated by delivery agent, duration, domain, and student age, with inconsistent duration findings [~W]
• Cognitive resources play a dual role in SR, both enabling self-regulatory processes and constraining how Learning Activities unfold [~W]
• Students make substantial use of worked examples during self-regulated learning, but task selection patterns differ across studies and between high- and low-scoring students [~W]
• Metacognitive monitoring bias and precision modulate when students stop studying relative to their goals [~W]
• Perceived mental effort serves as a cue for monitoring judgments, which mediate the effort–outcome relation (David et al., 2024 meta-analysis) [+W] — attached 2026-10-21 from de Bruin et al. (2025), which proposed "The Effort Monitoring and Regulation (EMR) model integrates self-regulated learning and cognitive load theory".
• Negative performance feedback raises expected future mental effort and affects perceived effort and willingness to invest effort via challenge and threat (van Gog et al., 2024) [+W] — attached 2026-10-21 from de Bruin et al. (2025), which proposed "The Effort Monitoring and Regulation (EMR) model integrates self-regulated learning and cognitive load theory".
• Process mining shows VM conditions exhibit unique reasoning–self-regulation sequences and self-loops absent in the control condition [~W]
• Cognitive learning strategy training yields preliminary evidence of far metacognitive transfer to effort regulation (Wirth et al., 2025) [+W]
• General pre-warnings mostly show null effects, but monitoring with susceptibility feedback reduces susceptibility [+W]
• EF explains substantial achievement variance (about 20–60% in school children) while SRL shows positive but low-effect-size relations to achievement [~W]
• EF longitudinally predicts later SRL in children, supporting the notion that EF predicts SRL but not vice versa [~W]
• Metacognition mediates the relationship between EF and SRL [+W]
• SRL and EF show primarily low to moderate correlations across age groups, with no detectable pattern depending on age [~W]
• Effects of monitoring tools are highest when tools address both learning content and learning behavior and stimulate metacognitive monitoring [+W]
• Mental effort and judgments of learning show a medium-sized negative correlation, moderated by regulation mode and task type [~W]
• Monitoring tools show low effects on learning motivation (d = 0.17) [~W]
• Explicit strategy instruction, unlike implicit strategy instruction, is associated with a gain in student performance [+W] — attached 2026-10-22 from Charlotte Dignath et al. (2021), which proposed "Framework of direct and indirect teacher approaches to activate self-regulated learning".
• Teachers' direct instructional support is associated with students' use of self-regulation strategies, while autonomy as indirect activation was negatively associated with reading performance [+W]
• Adolescent digital self-regulation is enacted through a typology of four strategy types differing in executive effort versus external scaffolding [~W]
• Prior meta-analysis found a negative medium-sized effort–monitoring correlation (r = -0.35), attenuated under goal-driven manipulations [~W]
• Perceived task difficulty relates to metacognitive strategy use only linearly, with no significant quadratic component [~W]
• For metacognitive strategy use, resources competitively mediate the linear effect and cognitive load partially mediates the linear and mediates the quadratic relationship [~W]
• Contemporary SRL models largely omit the broader social context and implicitly assume all learners are available to learn [~W]
• SRL strategy interventions improve academic achievement, with meta-analytic effects that vary by domain [+W] — attached 2026-10-22 from Ulrich Trautwein et al. (2026), which proposed "Integrated multicomponent model of self-regulation competence unifying SRL, executive functions, and conscientiousness".

The evidence by question

• Does teaching and prompting self-regulation improve achievement over ordinary instruction? Self-regulated learning improves achievement [+S]
• Does a learner's self-regulation predict achievement, and how strongly? Self Regulated Learning Predicts Achievement [+S]
• Does the forethought phase pay off: do specific goals and if-then plans improve performance, and which goals suit novices? Goal setting improves performance [+S]
• What happens to regulation when learners have a generative AI assistant, and what counteracts offloading? Evidence suggests over-reliance on generative AI may negatively affect students' self-regulated learning behaviors, potentially harming retention and transfer to novel contexts [+M]

Related Theories

• Self-Determination Theory — motivation and autonomy shape whether learners engage in self-regulatory behaviors
• Cognitive Load Theory — working-memory demands can constrain learners' ability to monitor and regulate effectively, especially when they are novices
• Executive Function Development — self-regulation depends substantially on executive-function capacities (inhibitory control, working memory) that develop across childhood and adolescence
• Pintrich's four-phase, four-area paradigm of self-regulated learning
• Pintrich's four-category model of self-regulated learning strategies
• Three-phase SRL scaffolding framework (planning, task execution, post-task reflection) operationalized across iterative design cycles
• Learning readiness as an operationalization of self-regulated learning (SRL)
• Self-regulated learning as the priority outcome of remote independent work
• Four-category operationalization of Winne and Hadwin's four-stage SRL model for tutored problem-solving verbalizations

Examples

• Goal Setting & Monitoring
• Self-monitoring
• Formative Assessment
• Immediate Feedback
• Self-Regulation Questionnaire (SRQ) — a scored instrument built directly on the Miller & Brown seven-step model described above
• Place GenAI entry after the learner's initial task attempt rather than before it
• Study learning with AI by varying the interactional arrangement and measuring process and delayed reduced-support outcomes
• Teacher supports for self-regulated GenAI feedback engagement
• App-based metacognitive monitoring training program with paired-associates memory tasks and feedback
• Newell-inspired time-scale taxonomy for aligning SRL measurement to theorized process duration

Key Sources

• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70. https://doi.org/10.1207/s15430421tip4102_2
• Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7
• Miller, W. R., & Brown, J. M. (1991). Self-regulation as a conceptual basis for the prevention and treatment of addictive behaviours. In N. Heather, W. R. Miller, & J. Greeley (Eds.), Self-control and the addictive behaviours (pp. 3-79). Maxwell Macmillan.
• Brown, J. M. (1998). Self-regulation and the addictive behaviors. In W. R. Miller & N. Heather (Eds.), Treating addictive behaviors (2nd ed., pp. 61-73). Plenum Press.
• Brown, J. M., Miller, W. R., & Lawendowski, L. A. (1999). The Self-Regulation Questionnaire (SRQ). In L. VandeCreek & T. L. Jackson (Eds.), Innovations in clinical practice: A source book (Vol. 17, pp. 281-292). Professional Resource Press.
• Arduini-Van Hoose, N. (2020). Social cognitive learning theory. In Educational psychology. Retrieved from https://edpsych.pressbooks.sunycreate.cloud. CC BY-NC-SA 4.0.

Page metadata

Field	Value
type	theory
title	Self-Regulated Learning
description	Self-Regulated Learning (SRL) explains learning as a cyclical process in which learners set goals, choose strategies, monitor progress, and reflect on outcomes in order to improve later performance.
status	review
canonical	True
generated	by claude/unspecified on 2026-08-29
trust	unverified — no one has confirmed this page's content
sources	2 — zimmerman-2002, zimmerman-2000

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