3279e14bb2
There will be no courses. What was there: one draft called "jk" with two empty lessons, and 4,000 lines of code around it — courses, modules, lessons, enrolments, per-lesson progress, SCORM, BigBlueButton, completion certificates, three React pages, a router, two models. Its real cost was everywhere else. Every query that measured practice had to remember `Quiz.course_id.is_(None)`, and forgetting it in one place would have silently mixed course attempts into a learner's analytics; the bank predicate carried a subquery to exclude a course's own questions from every search, recommendation and share; quiz access had a second, parallel rule about enrolment. All of that is gone, so the remaining rules say what they mean. `quizzes.allow_review` goes with it. It was only ever enforced for a course quiz, so it had become a promise nothing keeps — the public session page was still offering "no answer review" about sessions that review fine. The fixtures' question 5 lived in a course quiz and stood for "a question that exists but is not in your bank". There is no such thing now — a question is in the bank unless it is deleted — so the counts it kept out of the numbers are back in, and the tests that turned on it now turn on deletion or on the attempt that actually holds a question. Files the LMS uploaded stay on disk and stay protected: LEGACY_LMS_PREFIXES in app/utils/upload_access.py is what keeps them unreachable. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
79 lines
2.1 KiB
Python
79 lines
2.1 KiB
Python
from datetime import datetime
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from pydantic import BaseModel, Field
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class AnswerSubmission(BaseModel):
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question_id: int
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user_answer: str
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class AttemptSubmit(BaseModel):
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answers: list[AnswerSubmission]
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# {question_id: seconds}. Absent for a client that does not measure, which is
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# why the column is nullable rather than defaulted to zero.
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timings: dict[int, int] | None = None
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# Question ids where the learner opened a tip before answering. Getting it
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# right after a nudge is not the same as getting it right, and the two are
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# worth telling apart without calling either of them wrong.
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hints: list[int] | None = None
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class AnswerDetail(BaseModel):
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question_id: int
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question_text: str
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question_type: str
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options: list[str] | None = None
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user_answer: str
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correct_answer: str
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is_correct: bool
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explanation: str | None
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explanation_image_path: str | None = None
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image_path: str | None = None
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page_reference: int | None = None
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category_breadcrumbs: list[dict] = Field(default_factory=list)
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figures: list[dict] = Field(default_factory=list)
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class Config:
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from_attributes = True
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class AttemptResponse(BaseModel):
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id: int
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quiz_id: int
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score: int
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total_questions: int
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percentage: float
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started_at: datetime
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completed_at: datetime | None
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mode: str | None = None
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class Config:
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from_attributes = True
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class AttemptDetail(AttemptResponse):
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answers: list[AnswerDetail] = []
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# Stats schemas
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class QuizStats(BaseModel):
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quiz_id: int
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quiz_title: str
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attempts_count: int
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best_score: float
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latest_score: float
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average_score: float
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class DashboardStats(BaseModel):
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total_documents: int
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total_quizzes: int
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total_attempts: int
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average_score: float
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quiz_stats: list[QuizStats] = []
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# Question-centred figures. The quiz counts above describe how the material
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# happens to be packaged; these describe what the learner has worked through.
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questions_seen: int = 0
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questions_correct: int = 0
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bank_total: int = 0
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