How learners encode, store, and retrieve information — and the teaching moves that exploit how memory actually works. All interventions cross-referenced to peer-reviewed meta-analyses.
Avg. effect (d): 0.51 across 8 skillsStrongest: Retrieval Practice (d = 0.67)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.
Memory & Learning Science
Spaced Practice Schedule Builder
Schedule retrieval intervals across a unit so learning is distributed, not massed.
+5 monthsEEF Toolkit — Spaced learning strand2021
d = 0.60Latimier, Peyre & Ramus, Educational Psychology Review2021
LowImplementation
Memory & Learning Science
Cognitive Load Analyser
Analyse a learning task for cognitive load problems and recommend specific design improvements.
no EEF strand
d = 0.51Noetel, Griffith, Delaney et al., Review of Educational Research2022
MediumImplementation
Memory & Learning Science
Dual Coding Designer
Design a visual complement to verbal content using dual coding principles for stronger encoding.
no EEF strand
d = 0.37Cromley & Chen, Educational Research Review2025
MediumImplementation
Memory & Learning Science
Elaborative Interrogation Prompt Generator
Generate elaborative interrogation prompts that deepen encoding through targeted why and how questions.
no EEF strand
d = 0.56Donoghue & Hattie, Frontiers in Education2021
LowImplementation
Memory & Learning Science
Feedback Quality Analyser & Rewriter
Analyse existing written feedback for quality, specificity, actionability, and impact on student learning.
+6 monthsEEF Toolkit — Feedback strand2021
d = 0.48Wisniewski, Zierer & Hattie, Frontiers in Psychology2020
LowImplementation
Memory & Learning Science
Interleaving Unit Planner
Redesign a blocked topic sequence into an interleaved plan with mixed practice across related topics.
no EEF strand
d = 0.42Brunmair & Richter, Psychological Bulletin2019
MediumImplementation
Memory & Learning Science
Retrieval Practice Question Generator
Generate retrieval practice questions at varied difficulty levels for a topic or concept.
no EEF strand
d = 0.67Adesope, Trevisan & Sundararajan, Review of Educational Research2017
LowImplementation
Memory & Learning Science
Worked Example Designer with Completion Fading
Design a worked example fading sequence from fully worked examples through to independent practice.
no EEF strand
d = 0.48Barbieri, Miller-Cotto, Clerjuste & Chawla, Educational Psychology Review2023
MediumImplementation
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.