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Deep Processing Practice

2 min read

Part of Deep Processing

Deep Processing Practice

Canonical dimension hub: Deep Processing

Deep processing is meaning-making: comparing, evaluating, connecting, simplifying, and organizing information into a usable schema.

Summary

Understanding and memory are treated in this corpus as byproducts of processing quality. The learner has to perform thought operations that create relationships and meaning.

Deep Processing Behaviors

  • Compare ideas.
  • Rate importance.
  • Judge which relationships matter most.
  • Ask what problem the information solves.
  • Generate examples and analogies.
  • Teach the idea in simpler form.
  • Map dependencies and relationships.
  • Connect new information to prior knowledge.
  • Convert facts into a structure that supports use.

Shallow Processing Behaviors

  • Repeating information.
  • Copying notes.
  • Highlighting without judgment.
  • Reading for line-by-line understanding only.
  • Watching explanations without transforming them.

Metacognitive Test

Ask:

  • What did I do with the information?
  • What relationship did I create?
  • What changed in my mental model?
  • Could I use this knowledge in the target situation?

Bear Hunter System is the encoding-side implementation of Deep Processing. Spaced Interleaved Retrieval tests whether that processing produced knowledge that can be reconstructed and used.

Open Questions

  • Which deep processing operations are most reliable for technical learning?
  • How should deep processing be represented in Obsidian notes?

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