04abd78380
Three things the card system did not have. **Spaced repetition.** There was none. "Known" and "to review" were React state that vanished on reload, so a deck of two hundred was two hundred cards every time and the only spacing was whichever cards a learner remembered to skip. Verdicts are now kept, and the deck comes back in the order the learner's own history calls for: due first, most decayed first, then never seen, then the rest — because somebody who has met the whole deck recently should still get a deck rather than a screen saying come back on Thursday. It borrows the question player's arithmetic rather than choosing its own. `recall_probability`, `DUE_RECALL`, the thirty-day half-life: two schedulers with two ideas of "due", in one product that shows a learner one readiness number, is how the number stops meaning anything. Two outcomes and no four-point scale — a scale asks a learner to rate their own recall in units they have never calibrated, and the extra resolution is noise. **Cards are prose.** Both faces go through the same renderer as everything else, so a card can carry `[[264|respiratory failure]]`, a `==key point==`, a teaching tip or a figure. That is most of what "link cards to things" turns out to mean. **A deck is reachable from the question.** Beside the topic-reading chip under the correct answer, one chip per linked deck. Read from the question's end only, deliberately: a card that listed the questions it belongs to would hand a learner revising the deck the shape of the exam, and the answer with it. Migration m3d4e5f6a7b8. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
"""Cards remember when they were last answered
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"Known" and "to review" were React state: they vanished on reload, so a deck of
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two hundred was two hundred cards every time and the only spacing was whichever
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cards a learner remembered to skip. This is the log the scheduler reads.
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A log rather than a row per card, because what the scheduler needs is the
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latest verdict and its age — and keeping the history means a card missed three
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times running can later be treated differently from one missed once, without a
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migration to add the column that would have recorded it.
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Revision ID: m3d4e5f6a7b8
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Revises: l2c3d4e5f6a7
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "m3d4e5f6a7b8"
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down_revision = "l2c3d4e5f6a7"
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branch_labels = None
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depends_on = None
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def upgrade() -> None:
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if "flashcard_reviews" in sa.inspect(op.get_bind()).get_table_names():
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return
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op.create_table(
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"flashcard_reviews",
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sa.Column("id", sa.Integer(), primary_key=True),
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sa.Column("user_id", sa.Integer(),
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sa.ForeignKey("users.id", ondelete="CASCADE"), nullable=False),
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sa.Column("flashcard_id", sa.Integer(),
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sa.ForeignKey("flashcards.id", ondelete="CASCADE"), nullable=False),
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sa.Column("outcome", sa.String(length=10), nullable=False),
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sa.Column("created_at", sa.DateTime(), nullable=True),
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)
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op.create_index("ix_flashcard_reviews_user_id", "flashcard_reviews", ["user_id"])
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op.create_index("ix_flashcard_reviews_flashcard_id", "flashcard_reviews", ["flashcard_id"])
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# The scheduler asks "this learner's latest verdict per card", which is this
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# index read backwards.
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op.create_index("ix_flashcard_reviews_recent", "flashcard_reviews",
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["user_id", "flashcard_id", "created_at"])
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def downgrade() -> None:
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op.drop_table("flashcard_reviews")
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