Add pure correction and pedagogy logic with full test coverage

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>
2026-10-13 02:11:58 +02:00 · 2026-10-11 17:03:14 +02:00
parent d057c8ee77
commit 2fe455f5d1
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+class_name TextNormalizer
+extends RefCounted
+## Normalisation de texte avant comparaison dans les dictees : espaces (dont
+## insecables), apostrophes typographiques, et sequences Unicode decomposees
+## des caracteres francais ramenees a leur forme composee. GDScript n'expose
+## pas de normalisation NFC/NFD native : table explicite ici, comme prevu par
+## les "Points durs" du prompt de lancement.
+
+const NBSP := " "
+const NARROW_NBSP := " "
+const TYPOGRAPHIC_APOSTROPHE := "’"
+
+## Sequences decomposees (lettre de base + marque combinante) -> caractere
+## compose. Couvre les caracteres francais courants : accents, cedille, trema.
+const DECOMPOSED_TO_COMPOSED := {
+    "é": "é",
+    "è": "è",
+    "ê": "ê",
+    "ë": "ë",
+    "É": "É",
+    "È": "È",
+    "Ê": "Ê",
+    "Ë": "Ë",
+    "à": "à",
+    "â": "â",
+    "À": "À",
+    "Â": "Â",
+    "ù": "ù",
+    "û": "û",
+    "ü": "ü",
+    "Ù": "Ù",
+    "Û": "Û",
+    "Ü": "Ü",
+    "î": "î",
+    "ï": "ï",
+    "Î": "Î",
+    "Ï": "Ï",
+    "ô": "ô",
+    "ö": "ö",
+    "Ô": "Ô",
+    "Ö": "Ö",
+    "ç": "ç",
+    "Ç": "Ç",
+}
+
+
+static func normalize(text: String) -> String:
+    var result := text
+    result = result.replace(NBSP, " ")
+    result = result.replace(NARROW_NBSP, " ")
+    result = result.replace(TYPOGRAPHIC_APOSTROPHE, "'")
+    for decomposed: String in DECOMPOSED_TO_COMPOSED:
+        result = result.replace(decomposed, DECOMPOSED_TO_COMPOSED[decomposed])
+    while result.find("  ") != -1:
+        result = result.replace("  ", " ")
+    return result.strip_edges()
+
+
+static func split_words(text: String) -> Array[String]:
+    var normalized := normalize(text)
+    var words: Array[String] = []
+    if normalized.is_empty():
+        return words
+    var parts: PackedStringArray = normalized.split(" ", false)
+    for part in parts:
+        words.append(part)
+    return words