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https://github.com/deunix-educ/aventuriers-des-mots.git
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2fe455f5d1
scripts/correction/: AnswerValidator (multiple choice, sentence correction via tap-the-faulty-word + retype) and DictationAnalyzer build the CorrectionResult structure from cahier des charges §6. Dictation analysis aligns words with WordAligner (Wagner-Fischer edit distance) before any character comparison, so an omitted or added word doesn't cascade into false errors on every following word. TextNormalizer handles non-breaking spaces, typographic apostrophes, and decomposed-vs-composed French accented characters via an explicit table (GDScript has no built-in NFC/NFD normalization). Error classification follows the data annotations only (rule_id present -> grammatical, absent -> unclassified), never a runtime linguistic guess. scripts/pedagogy/: DifficultyManager (per-skill difficulty from recent results), AttemptFlow (3-attempt graduated aid: hint, then explanation, then reveal, matching exercise-lifecycle.md's second gap), and ReviewScheduler (the five mastery states from §9, with session-counted spaced review intervals [1, 3, 6, 12] rather than wall-clock dates, since a family device's clock isn't trustworthy). All of it is pure: no scene tree, no clock or RNG read directly, every effectful input (session number, attempt outcome) is a parameter. 65 headless tests across both layers, including the word-omission-doesn't-cascade regression and the full decouverte -> acquise -> a_reviser -> acquise path. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
129 lines
4.9 KiB
GDScript
129 lines
4.9 KiB
GDScript
class_name DictationAnalyzer
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extends RefCounted
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## Analyse la production ecrite d'un enfant pour une dictee : alignement mot a
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## mot (WordAligner) avant comparaison, normalisation (TextNormalizer), puis
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## classification de chaque erreur via les annotations de la donnee cible
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## (dictation["words"]). Sans annotation (pas de rule_id), l'erreur reste non
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## classee (voir "Points durs" du prompt de lancement). Code pur, sans horloge
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## ni hasard. L'explication complete (texte de la regle) est composee par la
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## couche d'affichage a partir des rule_id retournes, pas ici.
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static func analyze(dictation: Dictionary, submitted_text: String) -> CorrectionResult:
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var normalized_submitted := TextNormalizer.normalize(submitted_text)
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var normalized_target := TextNormalizer.normalize(dictation["target_text"])
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if _matches_target_or_variant(dictation, normalized_submitted, normalized_target):
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return CorrectionResult.correct(_build_explanation(0))
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var target_words := TextNormalizer.split_words(normalized_target)
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var submitted_words := TextNormalizer.split_words(normalized_submitted)
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var words_meta: Array = dictation["words"]
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# L'alignement se fait insensible a la casse : une majuscule manquante est
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# une erreur de capitalisation a part, pas un mot substitue (voir
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# docs/specs/decisions.md point 6).
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var target_words_for_align := _to_lower_all(target_words)
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var submitted_words_for_align := _to_lower_all(submitted_words)
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var ops := WordAligner.align(target_words_for_align, submitted_words_for_align)
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var errors: Array[Dictionary] = []
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for op: Dictionary in ops:
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match op["op"]:
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"match":
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var capital_error: Variant = _check_capitalization(
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words_meta, op["target_index"], submitted_words[op["submitted_index"]]
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)
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if capital_error != null:
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errors.append(capital_error)
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"substitute":
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(
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errors
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. append(
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_classify_error(
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words_meta,
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op["target_index"],
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submitted_words[op["submitted_index"]],
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)
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)
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)
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"delete":
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errors.append(_classify_error(words_meta, op["target_index"], ""))
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"insert":
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(
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errors
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. append(
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{
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"position": target_words.size(),
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"error_type": "unclassified",
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"rule_id": "",
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"found": submitted_words[op["submitted_index"]],
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"expected": "",
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}
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)
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)
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if errors.is_empty():
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return CorrectionResult.correct(_build_explanation(0))
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return CorrectionResult.incorrect(
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errors, _build_explanation(errors.size()), "", [dictation["target_text"]]
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)
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static func _matches_target_or_variant(
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dictation: Dictionary, normalized_submitted: String, normalized_target: String
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) -> bool:
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if normalized_submitted == normalized_target:
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return true
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for variant: String in dictation.get("accepted_variants", []):
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if normalized_submitted == TextNormalizer.normalize(variant):
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return true
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return false
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static func _classify_error(words_meta: Array, target_index: int, found: String) -> Dictionary:
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var meta: Dictionary = words_meta[target_index] if target_index < words_meta.size() else {}
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var expected: String = meta.get("text", "")
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var rule_id: String = meta.get("rule_id", "")
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return {
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"position": target_index,
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"error_type": "grammatical" if not rule_id.is_empty() else "unclassified",
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"rule_id": rule_id,
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"found": found,
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"expected": expected,
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}
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static func _check_capitalization(
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words_meta: Array, target_index: int, submitted_word: String
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) -> Variant:
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if target_index >= words_meta.size() or submitted_word.is_empty():
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return null
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var meta: Dictionary = words_meta[target_index]
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if not meta.get("expects_capital", false):
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return null
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var first_letter := submitted_word.substr(0, 1)
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if first_letter == first_letter.to_upper():
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return null
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return {
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"position": target_index,
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"error_type": "capitalization",
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"rule_id": meta.get("rule_id", ""),
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"found": submitted_word,
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"expected": meta.get("text", submitted_word),
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}
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static func _to_lower_all(words: Array[String]) -> Array[String]:
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var result: Array[String] = []
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for word in words:
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result.append(word.to_lower())
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return result
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static func _build_explanation(error_count: int) -> String:
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if error_count == 0:
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return "Dictee correcte."
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return "Dictee corrigee : %d erreur(s) a revoir." % error_count
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