Self-Regulated Learning (SRL) Overview

Updated 20 November 2025

Self-regulated learning (SRL) is defined as a process where learners actively plan, monitor, and adjust cognitive, metacognitive, and motivational strategies to meet specific goals.
Empirical research shows SRL is dynamic, recursive, and non-linear, with behaviors modeled using methodologies like HMMs, clustering, and network analysis.
AI-driven platforms now integrate SRL theories to deliver real-time analytics, reflective prompts, and adaptive scaffolding that enhance academic performance.

Self-regulated learning (SRL) refers to the processes by which learners actively plan, monitor, control, and adapt their cognitive, metacognitive, motivational, and behavioral strategies to achieve learning goals. SRL is widely considered foundational for academic achievement, lifelong learning, and productive engagement in increasingly technology-enhanced educational environments. The emergence of generative AI and data-rich learning ecosystems has led to significant advances in SRL theory, measurement, modeling, and adaptive support, challenging classical sequential models and introducing new methods for real-time scaffolding and analytics.

1. Conceptual Foundations and Theoretical Frameworks

SRL encompasses multiple, often cyclical phases and subprocesses. Canonical frameworks include:

Zimmerman’s three-phase cyclical model: Forethought (goal setting, strategic planning), Performance (self-control, monitoring, strategy use), and Self-reflection (self-evaluation, causal attribution, adaptation) form an iterative feedback loop, with each phase both informing and depending on the others (Nieto-Cardenas et al., 13 Nov 2025, Yang et al., 14 Aug 2025, Chatti et al., 2023).
Bannert’s process framework: Decomposes SRL into seven observable subprocesses: Orientation, Planning, Monitoring, Evaluation, First-Reading, Re-Reading, and Elaboration/Organisation, capturing both metacognitive and cognitive dimensions (Cheng et al., 12 Dec 2024, Yang et al., 14 Aug 2025).
COPES model (Winne & Hadwin): Articulates SRL as a continuous cycling through Conditions (C), Operations (O), Products (P), Evaluations (E), and Standards (S), supporting AI-driven trace analytics for targeted scaffolding (Qian et al., 8 Aug 2025).
Pintrich’s four-phase model: Forethought, Monitoring, Control, and Reflection underscore SRL’s recursive, regulatory structure (Lyu et al., 30 Sep 2025).
SRL is further subdivided into cognitive (e.g., rehearsal, elaboration), metacognitive (e.g., planning, monitoring), motivational (e.g., self-efficacy), and resource management strategies (e.g., time management, help seeking) (Vogelsmeier et al., 16 Jun 2025, Viberg et al., 2021).

Recent empirical research demonstrates that SRL is highly dynamic, recursive, and often non-linear, challenging older assumptions of strictly sequential phase progression (Yang et al., 14 Aug 2025, Lyu et al., 30 Sep 2025). In generative AI-augmented and chatbot-powered environments, regulation ecologies may become “control-centric” and phase-skipping, with students frequently offloading planning and reflection to AI systems (Lyu et al., 30 Sep 2025).

2. Modeling and Measurement of SRL

SRL can be assessed at multiple levels of granularity:

Micro-level (process/tactic) measurement: Fine-grained logging and action coding, e.g. via trace data from digital platforms, allows mapping user behaviors into SRL subprocesses. Action and process libraries, as in FLoRA and Bannert’s frameworks, provide the bridge between raw clicks/keystrokes and theoretical constructs (Yang et al., 14 Aug 2025, Cheng et al., 12 Dec 2024, Li et al., 12 Dec 2024).
Hidden Markov Models (HMM): Layered models infer latent “hidden tactics” from sequences of observed SRL processes. Yang et al. formalize SRL as a sequence of emissions Oₜ (observed processes) generated by an underlying sequence Sₜ (hidden tactics), parameterized by λ = (A transition, B emission, π initial distribution), with model selection via AIC, BIC, and likelihood (Yang et al., 14 Aug 2025).
Clustering and Profile Discovery: K-means on Levenshtein distances between hidden-tactic sequences reveals distinct SRL strategy profiles (“GenAI-Integrated Writers,” “Conventional Strategic Writers,” “Intensive Material Reviewers”) that correlate with performance (Yang et al., 14 Aug 2025).
Network analysis (ENA): Epistemic network analysis quantifies and visualizes co-occurrences of SRL subprocesses (e.g., Re-Reading ⟷ Elaboration) at the group or individual level, aligning higher-order patterns with performance and level of education (Cheng et al., 12 Dec 2024).
Psychometric surveys and latent trait models: Self-report instruments (e.g., MSLQ) operationalize SRL as an aptitude with multivariate latent structure, supported by confirmatory/exploratory factor analysis and psychological network analysis (Vogelsmeier et al., 16 Jun 2025).
Process mining: Process mining techniques are used to extract dominant behavioral patterns and non-sequential regulation pathways, notably in chatbot-mediated interactions characterized by heavy control-phase dominance (Lyu et al., 30 Sep 2025).
Machine learning feature engineering: SRL features (e.g. quiz history, reading speed, backscrolls) can be engineered from trace data for supervised models, with cyclical dependencies modeled in phase-aware AI architectures (Schwabe et al., 25 Jun 2025).

3. Analytics, Dashboards, and Indicators

Analytics platforms and dashboards provide real-time or retrospective feedback on SRL to both learners and educators:

Theory-driven learning analytics (SCLA-SRL): Dashboards with feedforward (planning, specification) and feedback (monitoring, reflection) indicators, such as Learning Success, Help-Seeking Ratio, Error Frequency, and Exam-Prep Index, link analytic visualization idioms (bar chart, heatmap, node-link) directly to SRL subprocesses (Chatti et al., 2023).
Workflow and allocation visualizations: Tools such as TrackThinkDashboard integrate web and programming logs into unified workflow flowcharts and action-allocating pie charts, enabling detection and reflection on common SRL strategies—trial-and-error, trial-and-search, time-management, and cautious planning (Watanabe et al., 25 Mar 2025).
Open learner models and reflection tools: Responsive Open Learning Environments (ROLE) embed modular SRL and domain widgets for planning, self-evaluation, and reflection, supported by recommendation systems and usage analytics (Nussbaumer et al., 2014).

The design of these analytics tools is typically grounded in SRL phase models and informed by human–computer interaction (HCI) and information visualization (InfoVis) theory, with iterative participatory design processes to ensure alignment with learner needs (Chatti et al., 2023).

4. Adaptive and AI-Driven SRL Scaffolding

Generative AI and LLMs are increasingly used to scaffold SRL through various modalities:

LLM-powered feedback platforms: LEAP employs pre-prompted LLMs to generate formative feedback scaffolds targeting different SRL processes: sense-making, elaboration, self-explanation, partial task solution, metacognitive, and motivational scaffolds. Prompt structure is systematically aligned with SRL theory (Steinert et al., 2023).
Gamified adaptive systems: SRLAgent integrates goal-setting, strategy execution, and self-reflection—mapped to Zimmerman’s phases—within a gamified environment, combining just-in-time agent support with progression mechanics and adaptive feedback. Empirical evaluation demonstrates significant SRL gains (p < .001, d = 0.234) compared to baseline conditions (Ge et al., 11 Jun 2025).
AI-driven reflective question generation: Systems like Owlgorithm leverage dual LLM agents for context-aware reflective questioning, directly post-submission, with alignment to Bloom’s taxonomy and dynamic adaptation based on correctness or failure (Nieto-Cardenas et al., 13 Nov 2025).
Just-in-time insight recall: The Irec prototype combines a knowledge graph of personal “insights” with hybrid recall, LLM-based similarity filtering, and Socratic dialogue for metacognitive scaffolding at the precise moment of need (JITAI) (Hou et al., 25 Jun 2025).
Reliability and hallucination mitigation: Multi-agent pipelines and LLM-as-a-Judge techniques evaluate and select LLM-generated scaffolds for SRL according to COPES process alignment and hallucination avoidance, outperforming single-agent and baseline ML models in agreement with human expert labels (Qian et al., 8 Aug 2025).
Scaffold design in open-ended environments: Betty’s Brain OELE uses pattern mining, inflection-point detection, and conversation-tree scaffolds targeting specific SRL subprocess breakdowns, with strategic hints showing significant performance gains—especially for debugging and assessment cycles (Munshi et al., 2022).

The practical implication is the emergence of real-time, dynamic, and highly adaptive scaffolding architectures that can personalize support to micro-level SRL behaviors, detected via trace analytics and process modeling, thereby closing the regulation loop at multiple timescales and degrees of granularity.

5. SRL Differences Across Contexts and Levels

Empirical SRL studies document substantial variation in SRL behaviors by educational context, learner age, and domain:

Secondary education: Network analysis reveals that secondary students predominantly engage in orientation, re-reading, and elaboration/organisation, with high performers demonstrating more iterative re-reading–elaboration cycles, and little evidence of evaluation/self-assessment. Scaffolding tools and teacher training in metacognitive monitoring are strongly indicated (Cheng et al., 12 Dec 2024).
Higher education: University students display more diverse process networks, with increased monitoring and evaluation, and higher cross-process connectivity, particularly among high performers. SRL support in these settings may focus on enhancing self-reflection and synthesis, especially in complex tasks such as academic writing (Cheng et al., 12 Dec 2024, Yang et al., 14 Aug 2025).
Programming and STEM domains: SRL patterns such as time management, cautious planning, and trial-and-error are associated with experience level and prior knowledge; dashboard analytics and reflective prompts can help calibrate strategy use (Watanabe et al., 25 Mar 2025, Nieto-Cardenas et al., 13 Nov 2025).
Chatbot/LLM-mediated environments: SRL profiles become heavily biased toward control-phase (execution and help-seeking), with underrepresented planning and reflection. Process mining confirms non-sequential, self-looping regulation states, highlighting the need for adaptive generative scaffolds to promote metacognitive engagement (Lyu et al., 30 Sep 2025).

6. Implications, Challenges, and Future Directions

Current research demonstrates the methodological, architectural, and pedagogical advances in SRL support, but also surfaces persistent and emergent challenges:

Nonlinearity and temporal complexity: SRL is best conceived as a recursive, layered, and non-linear process with frequent phase-skipping, self-loops, and strategy switches—especially in AI-augmented contexts (Yang et al., 14 Aug 2025, Lyu et al., 30 Sep 2025).
Modeling adaptation and causal inference: While phase-aware feature engineering greatly improves prediction accuracy in ML models (e.g., 65%→88% with cyclic SRL features) (Schwabe et al., 25 Jun 2025), true cyclic and phase-aware causal models remain in development.
Quality and trust in AI-driven scaffolds: Reliable, hallucination-free adaptive scaffolding requires multi-agent evaluation, position-bias mitigation, and human-auditability, especially in high-stakes contexts (Qian et al., 8 Aug 2025).
Performance differentiation and equity: Adaptive scaffolding must be tuned to learner profiles and prior knowledge, as low performers often need repeated or more explicit strategic scaffolds to internalize effective SRL routines (Munshi et al., 2022).
Instrument development and assessment: LLMs can simulate a surprising range of SRL survey responses and latent structures, but synthetic data cannot replace human-validated measures for high-stakes or affective assessment (Vogelsmeier et al., 16 Jun 2025).
Sociotechnical integration: Open, modular analytics and scaffold architectures such as FLoRA and ROLE provide extensible, integrable pipelines for lifelong, self-regulated learning across formal, informal, and mobile contexts (Li et al., 12 Dec 2024, Nussbaumer et al., 2014, Khalil, 2020).
Theory and practice convergence: There is consensus on the need to tightly couple SRL theory (cyclical, recursive, multi-component), analytics (trace-based, process-mining, ML), design (HCI/InfoVis), and AI-driven scaffolding to produce scalable and effective self-regulatory learning environments (Chatti et al., 2023, Steinert et al., 2023).

The ongoing evolution of SRL research incorporates longitudinal multimodal analytics, dynamic generative scaffolding, and collaborative, participatory design to support diverse learners in increasingly complex, AI-enhanced educational settings. These developments demand continuous methodological innovation and theoretical refinement, with a focus on transparency, adaptivity, and empirical rigor.