Competency C7
Metacognition & Self-Regulated Learning
Metacognition & Self-Regulated Learning develops students' capacity to know how they learn — what helps them, what gets in their way, and which strategies suit which tasks — and to plan, monitor, and adjust their own learning across timescales and contexts. It sequences from naming a task as easy, tricky, or stuck and trying a different approach when stuck at Band A, through evidence-based study strategies and goal-directed plans with meaningful checkpoints in the middle bands, to an integrated account of how one learns (in Flavell's three categories: cognition, strategy, self) and an SRL cycle adapted to the task type at Band F.
The 2 learning targets
- LT 7.1Knowing How I Learn
I can describe how I learn — what helps me, what gets in my way, and which strategies suit which kinds of tasks.
BandsA–FT2Horizontal understanding
Conceptual understanding that integrates across contexts — how students reason and apply ideas. Assessed by the quality of the reasoning, not just recall.Open → - LT 7.2Planning, Monitoring & Adjusting
I can plan a piece of learning, check how it's going, and adjust my approach when I need to.
BandsA–FT2Horizontal understanding
Conceptual understanding that integrates across contexts — how students reason and apply ideas. Assessed by the quality of the reasoning, not just recall.Open →
Evidence base
Where the evidence stands
Strong evidenceThis is the most robustly evidenced competency in the framework. Multiple independent meta-analyses converge on medium-to-large effects — rare agreement in educational research. The EEF Toolkit rates metacognition and self-regulated learning at +7 to +8 months' additional progress with high evidence security and very low cost. Dignath and Büttner's (2008) meta-analysis of 84 studies found a mean effect size of 0.69.
Explicit metacognitive instruction — teaching students to plan, monitor, and evaluate their learning — produces effects only when embedded in the subject being studied. A bolt-on course delivered separately from any particular subject transfers poorly to real learning situations. The EEF's 2025 guidance places metacognitive talk — teachers thinking aloud through their reasoning, making the invisible process of expert thinking visible — as the lead recommendation. The IMPROVE method (Mevarech & Kramarski, 1997) applies this in mathematics through structured metacognitive questioning before, during, and after problem-solving. Dunlosky et al.'s (2013) review of ten study strategies found two stand out above all others: practice testing — retrieving information from memory rather than re-reading — and spaced practice — returning to material at intervals rather than in a single session. Both work in part because they force students to confront what they do not yet know — breaking the illusion of familiarity that re-reading creates — and that accurate self-knowledge is exactly what self-regulation depends on.
Two aspects of REAL School's context make this competency especially important. The first is Dream-to-Reality (D2R): long-term project work requires exactly the planning, monitoring, and adjusting cycle that self-regulated learning research describes — students who cannot regulate their own learning across weeks and months cannot sustain the D2R arc. The second is AI. Fan et al. (2024) document a 'metacognitive laziness' effect: AI tools that complete cognitive work for students improve immediate output but reduce metacognitive engagement. Tankelevitch et al. (2024) argue that generative AI raises metacognitive demands rather than lowering them. The implication is counter-intuitive: AI integration makes explicit metacognitive instruction more necessary, not less.
What the evidence supports
Model your thinking aloud — make your planning, monitoring, and revision process visible to students, not just the finished product. Teach the three-phase cycle explicitly: before a task, set goals and plan; during it, monitor progress and manage attention; after it, evaluate honestly and adjust strategy. Use practice testing and spaced review — both explained in the evidence section above. Teach students to seek help strategically, not as a last resort. Embed all of this in subject content; a standalone study-skills session transfers poorly.
Caveats
Generic study-skills courses and bolt-on 'learning to learn' sessions transfer poorly — metacognition must be embedded in the subject being learned. Learning styles — visual, auditory, kinaesthetic — have been comprehensively debunked and have no place in a metacognitive curriculum. AI tools that do the cognitive work for students actively reduce metacognitive engagement; they require more explicit metacognitive scaffolding, not less.
References
- Education Endowment Foundation. (2018). Metacognition and self-regulated learning: Guidance report.
- Dignath, C., & Büttner, G. (2008). Components of fostering self-regulated learning among students.
- Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques.
- Flavell, J. H. (1979). Metacognition and cognitive monitoring.
- Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview.
- Mevarech, Z. R., & Kramarski, B. (1997). IMPROVE: A multidimensional method for teaching mathematics in heterogeneous classrooms.
- Fan, Y., et al. (2024). Beware of metacognitive laziness: Effects of generative AI on learning motivation, processes, and performance.
- Tankelevitch, L., et al. (2024). The metacognitive demands and opportunities of generative AI.