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>
68 lines
2.6 KiB
GDScript
68 lines
2.6 KiB
GDScript
extends GutTest
|
||
## Tests de DictationAnalyzer : alignement, classification lexicale/grammaticale
|
||
## via annotation, majuscule, variantes acceptees.
|
||
|
||
var dictation: Dictionary = {
|
||
"target_text": "Les petits chats dorment.",
|
||
"words":
|
||
[
|
||
{"text": "Les", "expects_capital": true},
|
||
{"text": "petits", "rule_id": "regle_accord_adjectif"},
|
||
{"text": "chats"},
|
||
{"text": "dorment."},
|
||
],
|
||
"accepted_variants": [],
|
||
}
|
||
|
||
|
||
func test_exact_match_is_correct() -> void:
|
||
var result := DictationAnalyzer.analyze(dictation, "Les petits chats dorment.")
|
||
assert_true(result.is_correct)
|
||
assert_eq(result.errors, [])
|
||
|
||
|
||
func test_annotated_word_error_is_classified_as_grammatical() -> void:
|
||
var result := DictationAnalyzer.analyze(dictation, "Les petit chats dorment.")
|
||
assert_false(result.is_correct)
|
||
assert_eq(result.errors.size(), 1)
|
||
assert_eq(result.errors[0]["error_type"], "grammatical")
|
||
assert_eq(result.errors[0]["rule_id"], "regle_accord_adjectif")
|
||
assert_eq(result.errors[0]["found"], "petit")
|
||
assert_eq(result.errors[0]["expected"], "petits")
|
||
|
||
|
||
func test_unannotated_word_error_is_unclassified() -> void:
|
||
var result := DictationAnalyzer.analyze(dictation, "Les petits chat dorment.")
|
||
assert_false(result.is_correct)
|
||
assert_eq(result.errors.size(), 1)
|
||
assert_eq(result.errors[0]["error_type"], "unclassified")
|
||
assert_eq(result.errors[0]["rule_id"], "")
|
||
|
||
|
||
func test_missing_word_does_not_cascade_into_further_errors() -> void:
|
||
# Le mot "petits" est omis : sans alignement, cela provoquerait deux
|
||
# erreurs en cascade sur "chats" et "dorment.".
|
||
var result := DictationAnalyzer.analyze(dictation, "Les chats dorment.")
|
||
assert_false(result.is_correct)
|
||
assert_eq(result.errors.size(), 1)
|
||
assert_eq(result.errors[0]["rule_id"], "regle_accord_adjectif")
|
||
|
||
|
||
func test_missing_capital_is_reported_separately() -> void:
|
||
var result := DictationAnalyzer.analyze(dictation, "les petits chats dorment.")
|
||
assert_false(result.is_correct)
|
||
assert_eq(result.errors.size(), 1)
|
||
assert_eq(result.errors[0]["error_type"], "capitalization")
|
||
|
||
|
||
func test_accepted_variant_is_treated_as_correct() -> void:
|
||
var dictation_with_variant: Dictionary = dictation.duplicate(true)
|
||
dictation_with_variant["accepted_variants"] = ["Les petits chats dorment"]
|
||
var result := DictationAnalyzer.analyze(dictation_with_variant, "Les petits chats dorment")
|
||
assert_true(result.is_correct)
|
||
|
||
|
||
func test_normalizes_before_comparing() -> void:
|
||
var result := DictationAnalyzer.analyze(dictation, "Les petits chats dorment.")
|
||
assert_true(result.is_correct)
|