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41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
"""Mastery scoring policy — intentionally simple and swappable.
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``compute_mastery`` maps a knowledge point's attempt history to a 0..1 mastery
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score. The current policy is a recency-weighted accuracy with a low-confidence
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cap: a single lucky answer cannot "master" a point — mastery is capped until
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there is enough evidence.
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This is the one place the pedagogy math lives. To plug in a richer model
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(e.g. an IRT/BKT estimate or a tuned spec), replace ``compute_mastery`` alone;
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callers (`LearningService.calculate_mastery`) need not change.
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"""
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from __future__ import annotations
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# Recency weights for the most recent attempts (oldest -> newest). Newer
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# attempts count more, so recovery after early mistakes is rewarded.
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_RECENCY_WEIGHTS: tuple[float, ...] = (0.5, 0.7, 0.85, 0.95, 1.0)
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# Mastery cannot exceed this until enough attempts accumulate, so one or two
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# correct answers cannot declare a point "mastered".
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_CONFIDENCE_CAP: dict[int, float] = {1: 0.5, 2: 0.8}
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def compute_mastery(correctness: list[bool]) -> float:
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"""Return a 0..1 mastery score from a knowledge point's attempt outcomes.
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Args:
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correctness: per-attempt correctness in chronological order.
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"""
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if not correctness:
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return 0.0
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recent = correctness[-len(_RECENCY_WEIGHTS) :]
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weights = _RECENCY_WEIGHTS[-len(recent) :]
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score = sum(w * (1.0 if c else 0.0) for w, c in zip(recent, weights, strict=True)) / sum(
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weights
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)
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return min(score, _CONFIDENCE_CAP.get(len(recent), 1.0))
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__all__ = ["compute_mastery"]
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