Design pre-class, in-class, and follow-up practice
Author: AILesson9 min setupTested with:Reviewed: 2026-08-29
Quick answer
Sequence preparation, guided application, feedback, and later retrieval around one learning target. Provide: Learning target and evidence, Learners and access, Course constraints. Expected result: A three-phase practice sequence with workload, feedback, access, and completion evidence.
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Design connected pre-class, in-class, and follow-up practice for one learning target.
Objective, success criteria, prior evidence, misconceptions, and transfer:
[target]
Level, group size, language, accessibility, device or home access, workload, and safeguarding:
[learners]
Class time, pre/follow-up time, materials, feedback capacity, grading, deadlines, and alternatives:
[course]
Give each phase a distinct function. Pre-class should activate or diagnose prerequisites without making unequal home access determine success. In class, use the evidence to model, practise, receive feedback, and attempt again. Follow-up should retrieve or transfer the same target, not introduce unsupported new content. Keep total workload within supplied limits and provide equivalent access routes. Do not invent completion data or assume non-completion means low motivation. Return phase map, learner instructions, time budget, evidence flow, feedback route, access alternatives, non-completion response, and instructor review triggers.
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From input to outcome
A worked example
See how concrete context turns this recipe into a usable result
Actual input
Learning target and evidence
Introductory statistics. Target: choose mean or median for a supplied dataset and justify with distribution/extreme-value evidence. Success: correct calculation and conditional justification. Prior work shows many learners say median is always better when an outlier exists.
Learners and access
24 adult learners. Some have phone access but home internet cannot be assumed. Printed alternative required. Calculators available in class. No personal financial disclosure.
Course constraints
60-minute class. Pre-class maximum 10 minutes, ungraded diagnostic. Follow-up maximum 15 minutes due in three days, low stakes. Teacher can give whole-class and checklist feedback, not individual written comments to all.
Example output
Pre-class, 8–10 minutes: paper or downloadable-identical sheet with two small datasets. Learners calculate mean/median, choose one for 'typical', and tick a reason. Purpose: diagnose procedure versus conditional-choice gaps. Non-completion is Unknown, not low motivation; provide the same two questions in the first five class minutes.
In class, 60 minutes: 0–5 access/diagnostic completion; 5–12 anonymous response pattern and model of one calculation; 12–22 contrast two datasets where an extreme is a valid observation versus a suspected entry error; 22–34 pairs calculate and justify using a four-item checklist; 34–41 whole-class feedback on the 'always median' misconception; 41–50 learners revise the same justification; 50–57 unseen individual choice; 57–60 exit reason. Evidence flows from diagnostic grouping of needs to model emphasis, then from checklist feedback to revision.
Follow-up, 12–15 minutes: one new non-personal dataset; calculate both measures, choose conditionally, state one limitation, then self-check against the same checklist. Paper and digital versions are identical. Instructor trigger: if more than one-third still use an unconditional rule, begin next class with a new contrast case rather than adding harder calculations.
Why this works
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Distinct phase functions prevent homework, classwork, and follow-up from repeating the same low-value task.
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Evidence flow lets each phase change what happens next instead of being an isolated activity.
Check the result
Does each phase serve a different but connected learning function?
Can learners without assumed home technology access an equivalent route?
Does feedback lead to another attempt on the same target?
Use it with confidence
Frequently asked questions
Practical answers about when to use this recipe, what to provide, and where human review still matters
What should I prepare before using “Design pre-class, in-class, and follow-up practice”?
For “Design pre-class, in-class, and follow-up practice,” prepare Learning target and evidence, Learners and access, and Course constraints. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.
When is the “Design pre-class, in-class, and follow-up practice” result not ready to use?
The result is not ready if it does not yet deliver the stated outcome—A three-phase practice sequence with workload, feedback, access, and completion evidence—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.
Which AI tools have recorded tests for “Design pre-class, in-class, and follow-up practice”?
No model run is recorded for “Design pre-class, in-class, and follow-up practice” as of 2026-08-29. Treat it as a model-portable template rather than a compatibility claim. Run the worked example first, keep every constraint visible, and compare the output with the result checks before using it on real material.
