Inquiry-Change Learning Loop¶
Core Idea¶
An inquiry-change learning loop uses a current model to select a deliberate change in a real setting, measures the changed setting as evidence, durably revises both understanding and action, and carries those updates into a subsequent pass. Inquiry and practice learn from a situation they jointly alter. A retained model, deliberate real-setting action, systematic observation, durable model revision, next-action selection, and recurrence with carried state are required. The target and frame may evolve rather than converge monotonically.
Broad Use¶
PDSA, build–measure–learn, design-based research, continuous discovery, pragmatic trials, and action research preserve the same model–change– evidence–revision cycle.
Clarity¶
Feedback provides a return path but not durable inquiry. Iteration repeats but need not measure evidence. Experimental design emphasizes identification through assignment and comparison. Action research adds participatory and social-science commitments.
Manages Complexity¶
The abstraction turns scattered cases into one role-based test: identify the required elements, test their relation, and reject the classification when a constitutive commitment is missing.
Abstract Reasoning¶
Use the structural signature rather than the label, then challenge the nearest counterexample. Repeating an intervention until performance improves, without retaining an explanatory update or using evidence to select the next action, is iteration but not an inquiry-change learning loop.
Knowledge Transfer¶
PDSA, build–measure–learn, design-based research, continuous discovery, pragmatic trials, and action research preserve the same model–change– evidence–revision cycle.
Example¶
A case qualifies only when the definition and every load-bearing role remain present. Repeating an intervention until performance improves, without retaining an explanatory update or using evidence to select the next action, is iteration but not an inquiry-change learning loop.
Relationships to Other Abstractions¶
Current abstraction Inquiry-Change Learning Loop Prime
Parents (3) — more general patterns this builds on
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Inquiry-Change Learning Loop is part of Feedback Prime
The loop contains feedback because observed consequences must return to alter the next model-guided action.
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Inquiry-Change Learning Loop is part of Iteration Prime
The loop contains iteration because revised practical and epistemic state is carried into a subsequent pass.
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Inquiry-Change Learning Loop is part of Learning Prime
The loop contains learning because evidence must durably update the retained model or capability that selects later action.
Children (1) — more specific cases that build on this
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Action Research Domain-specific is a decomposition of Inquiry-Change Learning Loop
Action research is the participatory social-science form of the neutral inquiry-change learning loop, adding researcher-participant overlap, a dual deliverable, and a participation-rigor frontier.
Hierarchy paths (4) — routes to 4 parentless roots
- Inquiry-Change Learning Loop → Feedback
- Inquiry-Change Learning Loop → Iteration
- Inquiry-Change Learning Loop → Learning → Adaptation
- Inquiry-Change Learning Loop → Learning → Memory Consolidation
Not to Be Confused With¶
Feedback provides a return path but not durable inquiry. Iteration repeats but need not measure evidence. Experimental design emphasizes identification through assignment and comparison. Action research adds participatory and social-science commitments.
Notes¶
(Canonical first draft; queued for Claude house-style re-authoring and citation review.)