Teach students to plan, monitor, and evaluate their own learning — through metacognitive prompts, scaffolds, study-strategy guidance, self-explanation, and if-then implementation intentions. All interventions cross-referenced to peer-reviewed meta-analyses where one exists.
Avg. effect (d): 0.45 across all 5 cards (all fully verified)Strongest: Self-Explanation Prompt Designer (d = 0.55)Last reviewed: May 2026
How to read these numbers
"Months of progress" is a teacher-friendly shorthand from the EEF Toolkit. It is not directly comparable across studies — different meta-analyses use different baselines, age groups, and outcome measures. Treat it as a magnitude indicator, not a precise prediction.
Cohen's d is the standardised effect size used in the original meta-analyses. d ≈ 0.40 is Hattie's "hinge point" — the average effect of a year of schooling. Higher = larger relative effect, but context matters more than the number.
Effect sizes are averages. A skill that shows large average effects can still produce small or negative effects in a specific classroom. Use these as a starting point for professional judgment, not a substitute for it.
Sources are dated. Where multiple meta-analyses exist, we lead with the most recent quality study and cross-reference EEF where available.
Implementation cost (Low / Medium / High) is a practical signal — not an exact science — of what your school needs to invest in teacher time, training, and structural changes to actually run the intervention well. It is the editorial team's reading of what the intervention typically requires in practice. Use it to gauge whether something is straightforward to introduce or a larger undertaking — not as a budget figure.
Some interventions are also priced in £ (UK) by the EEF Toolkit. For monetary cost data, see the EEF Teaching & Learning Toolkit.
Colour bands signal calibration, not value. An intervention in the "below typical" band is not "bad" — it means the intervention's average effect is below the typical effect of a year of schooling. That can still be appropriate for specific contexts the average does not capture.
Self-Regulated Learning
Metacognitive Prompt Library
Build a library of metacognitive prompts that target planning ("What's my goal here?"), monitoring ("Is this working?"), or evaluation ("What worked, what didn't?") — so students develop the habit of asking themselves the right cognitive question at the right moment.
+7 monthsEEF Toolkit — Metacognition and self-regulation strand2021
d = 0.40Guo, Journal of Computer Assisted Learning2022
LowImplementation
Self-Regulated Learning
Self-Regulation Scaffold Generator
Generate scaffolds — planning sheets, monitoring checklists, evaluation rubrics — that support students through the three phases of self-regulated learning before, during, and after a task.
+7 monthsEEF Toolkit — Metacognition and self-regulation strand2021
d = 0.50Shao, Chen, Wei, Li & Li, Frontiers in Psychology2023
MediumImplementation
Self-Regulated Learning
Study Strategy Selector & Guide
Select evidence-based study strategies — spaced practice, retrieval, interleaving, elaboration — matched to the material type, the learning goal, and the student's current habits, so students stop highlighting and re-reading and start doing what actually works.
+7 monthsEEF Toolkit — Metacognition and self-regulation strand2021
d = 0.38Theobald, Contemporary Educational Psychology2021
LowImplementation
Self-Regulated Learning
Self-Explanation Prompt Designer
Design prompts that get students to self-explain — 'Why does this step make sense?', 'How does this connect to what you knew before?' — so they generate inferences instead of just absorbing information.
+7 monthsEEF Toolkit — Metacognition and self-regulation strand2021
Help students design if-then plans — "When situation X occurs, I will respond by Y" — so good intentions translate into action. Gollwitzer's classic mechanism for closing the gap between wanting to study and actually studying.
+7 monthsEEF Toolkit — Metacognition and self-regulation strand2021
d = 0.40Sheeran, Listrom & Gollwitzer, European Review of Social Psychology2024
LowImplementation
Interrogate any educational claim
Heard a claim somewhere else? Type it here — we'll build a prompt you can paste into ChatGPT, Claude, or any chatbot. The prompt asks the model to cite real meta-analyses, separate strong from weak evidence, name boundary conditions, flag exaggerations, and discuss cost-effectiveness.
💡 For best results, enable your chatbot's reasoning / thinking mode before pasting — Claude's "Extended thinking", ChatGPT's "Think" mode, or Gemini's "Thinking" / 2.5 Pro reasoning. The prompt is more demanding than a typical Q&A and benefits from slower deliberation.