Memory Science

Flashcards / spaced-repetition software

Cloze deletion (the Anki {{c1::…}} pattern) forces active recall of one or two specific facts inside an otherwise complete, true sentence. The evidence corpus supports this: spaced repetition through Anki tracked with higher Step-1 pass rates and lower failure rates (Cooper et al. 2023); self-generated Anki flashcard volume independently predicted licensing performance (Deng, Gluckstein & Larsen 2015); higher reliance on Anki active-retrieval cards tracked with better exam outcomes (Gilbert et al.

Retrieval practice / testing effect

Retrieval practice — actively producing an answer from memory before re-reading it — is the single best-supported memory technique in the corpus. Dunlosky et al. (2013) rate practice testing one of only two "high utility" strategies; Adesope et al.

Spacing / distributed practice

A spacingPrompt is a short inline instruction telling the learner WHEN to come back to a chapter — not what to re-read, but when. The whole design rests on a few robust corpus findings:

Keyword mnemonic (foreign-vocabulary method)

The keyword mnemonic (Atkinson & Raugh 1975) encodes an unfamiliar word in two links: an acoustic/visual keyword that sounds or looks like the target, and a vivid interactive image binding that keyword to the target's meaning. For SQL learners the "foreign vocabulary" is the reserved-word lexicon — HAVING, COALESCE, PARTITION BY, INTERVAL and friends — which are opaque exactly like L2 words: the surface form gives little hint of the operation. We therefore pick a familiar keyword that is acoustically/visually near the SQL token, then stage a one-scene image whose action is the term's actual semantics (so the image is gate-checkable against the docNote, not just a pun).

Sleep & targeted memory reactivation (TMR)

The corpus is overwhelmingly about lab TMR: pairing material with an odor or sound at encoding, then re-playing that cue during monitored, often closed-loop sleep. A learner reading /postgres cannot run this. So the aids deliberately draw only on the naturalistic, actionable kernel these papers share:

Text-to-speech / audio learning

AI-assisted / adaptive learning

These are illustrative auto-generated quiz questions of the kind an automatic question generation (AQG) system would produce, each tied to a Postgres chapter and carrying evidence provenance. They model the JSON shape the lane emits (see spec §JSON shape). Each is a recall-or-recognition item with a correct answer and, where useful, plausible distractors — the multiple-choice form recommended by the AQG review (Kurdi et al.

Desirable difficulties

Rereading and "I've seen this, it makes sense" feel like learning but are weakly related to actual durable knowledge:

Interleaving / discriminative contrast

Interleaving pays off most when the categories being mixed are similar / confusable (Brunmair & Richter 2019) and when the learner is pushed into discriminative contrast — juxtaposing confusable items so their differences become salient (Kang & Pashler 2011). Two /postgres surfaces are almost ideally confusable:

Handwriting & elaborative encoding

Postgres internals is a high prior-knowledge, high-structure domain: B-trees, MVCC, WAL, lock modes, join algorithms. Two evidence streams make it an ideal home for elaboration aids:

Dual-coding theory (verbal + visual)

Method of loci / memory palace

The method of loci excels precisely where Postgres internals are hardest to retain:

Concrete examples

Contextual interference

Metacognition & self-regulated learning

Self-explanation

Spaced-repetition algorithms

Worked examples

Chunking

Concept mapping

Deliberate practice

Elaborative interrogation

Feedback timing

Note-taking strategies

Pretesting effect

Generation effect