mirror of
https://github.com/deunix-educ/aventuriers-des-mots.git
synced 2026-10-13 02:11:58 +02:00
Denis Defolie
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>
53 lines
2.0 KiB
GDScript
53 lines
2.0 KiB
GDScript
class_name SemanticChecks
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extends RefCounted
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## Contraintes croisees entre champs qu'un schema JSON (voir SchemaValidator) ne
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## peut pas exprimer : index valide, coherence entre deux tableaux, etc.
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## Appele uniquement apres une validation de schema reussie : ces fonctions
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## supposent une structure deja conforme.
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static func check_multiple_choice(data: Dictionary, path: String) -> Array[String]:
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var errors: Array[String] = []
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var answers: Array = data.get("answers", [])
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var correct_answer: int = data.get("correct_answer", -1)
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if correct_answer < 0 or correct_answer >= answers.size():
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errors.append(
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(
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"%s : correct_answer (%d) hors des bornes de answers (%d element(s))"
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% [path, correct_answer, answers.size()]
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)
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)
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return errors
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static func check_sentence_correction(data: Dictionary, path: String) -> Array[String]:
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var errors: Array[String] = []
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var tokens: Array = data.get("tokens", [])
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var has_error_token := false
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for i in tokens.size():
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var token: Dictionary = tokens[i]
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if token.get("correct", true) == false:
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has_error_token = true
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if not token.has("expected"):
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errors.append(
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"%s.tokens[%d] : token fautif sans correction attendue ('expected')" % [path, i]
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)
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if not has_error_token:
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errors.append("%s : aucun token fautif, rien a corriger" % path)
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return errors
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static func check_dictation(data: Dictionary, path: String) -> Array[String]:
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var errors: Array[String] = []
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var target_text: String = data.get("target_text", "")
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var words: Array = data.get("words", [])
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var expected_word_count := target_text.split(" ", false).size()
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if words.size() != expected_word_count:
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errors.append(
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(
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"%s : %d mot(s) annote(s) pour %d mot(s) dans target_text"
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% [path, words.size(), expected_word_count]
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)
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)
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return errors
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