The classroom formative cycle — setting clear targets, eliciting evidence of learning, diagnosing gaps, and closing them with usable feedback. All interventions cross-referenced to peer-reviewed meta-analyses where one exists, and to the EEF Toolkit where a matching strand exists.
Unpack a competency or standard into the underlying knowledge, skills, and observable success criteria students need to demonstrate.
Author student-friendly learning targets that tie each target to its broader purpose — what students will be able to do, and why it matters.
Design a single multiple-choice question, placed at a hinge point in a lesson, whose distractors diagnose specific misconceptions and tell you whether to move on, pause, or reteach.
Design a structured protocol of formative checks across a lesson — cold call, mini-whiteboards, exit tickets, ABCD cards, hand signals — so every student's understanding is sampled before the lesson ends.
Select the most appropriate formative assessment technique for a specific learning goal, lesson phase, and class context — matching technique to purpose rather than defaulting to one habit.
Analyse a set of student work for systematic gaps in understanding — patterns, not one-off errors — then translate the patterns into specific next-step teaching moves.
Structure how students examine their own and others' errors — identifying what went wrong, why, and what to do differently — so mistakes become deliberate learning material rather than something to hide.
Map the typical sequence of how understanding develops in a topic — what students typically know first, what comes next, where the predictable plateaus and misconceptions sit — so teaching, assessment, and feedback can target the right step.
Generate a criterion-referenced rubric with explicit performance criteria, distinguishing descriptive language across levels, so students and teachers share a common picture of what quality looks like.
Design a single-point rubric — one column describing proficient performance, with open spaces for noting 'areas to grow' and 'areas of strength' — for feedback that's faster to write and more growth-oriented than traditional analytic rubrics.
Audit and rebuild a rubric for internal logical coherence — checking that performance levels meaningfully distinguish, criteria are mutually independent, and descriptive language scales consistently so the rubric actually discriminates quality.
Design a structured goal-setting protocol where students set specific, near-term learning goals tied to success criteria, track progress, and revise the goal based on evidence — turning self-regulated learning from a slogan into a routine.
Audit an assessment for construct validity, content coverage, and freedom from construct-irrelevant variance — checking that the test actually measures what it claims to measure before the results are used to make decisions.
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.