27a1679774
Exit asked "Suspend quiz?" and offered Stay. Nobody presses Exit by accident, nothing is lost — the answers are saved and the clock pauses, which the dialog was explaining rather than deciding — and a confirmation for a reversible act is a step, not a safeguard. One press now, and if the save fails it stays put and says so, because leaving then would lose the answers. Resume and Start go straight in for the same reason. The overview that asks whether you meant it belongs to opening a link to a session you have not seen, not to a button you came to this page and chose. The actions moved out of the page heading and into the card that holds the result — Repeat session, Review answers, Resume session — because a row of buttons beside the title puts the decision as far from the figures it follows from as the layout allows. Delete session stays, but not as a red button next to Resume. It throws away answers the analysis is built from, and it is the one thing here AMBOSS has no need of — sessions are made freely, so a mis-made one is clutter worth removing. It is a quiet control at the end of the row. The session rail was sticky at top:0 with height:100vh, which started it underneath the 98px header and ran it past the bottom of the window, so the end of the list and the scroll that would reach it were never on screen. It measures from --app-header now, and a wheel over the list moves the list rather than carrying on into the page. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
214 lines
9.9 KiB
Markdown
214 lines
9.9 KiB
Markdown
# How the study recommendations and the adaptive session work
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Two questions were asked and owed an answer: *how do the study recommendations
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work?* and *how would an adaptive session work?* Both are built; this is what
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the code actually does, including the parts that are weaker than they look.
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---
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## 1. Study recommendations
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`GET /study-tools/recommendations` — `backend/app/routers/study_tools.py`.
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### What goes in
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Only the learner's **completed, non-expired, non-course** answers:
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```python
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QuizAttempt.user_id == user.id,
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QuizAttempt.completed_at.isnot(None),
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or_(QuizAttempt.expired == 0, QuizAttempt.expired.is_(None)),
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Quiz.course_id.is_(None),
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*([exam_filter] if exam_filter is not None else []),
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```
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`exam_filter` is the learner's chosen study objective. Answers to questions
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linked to a *different* exam are excluded, and so is that material from the
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denominator — someone revising for a paediatrics board who happens to sit a
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step-exam plan does not have it steer their recommendations. Questions linked
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to no exam stay in: unlinked means unclassified, not excluded.
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### Membership: the same answers, three ways
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Every answer is attributed to a set of **group keys**, and the grouping decides
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what a key is:
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| Tab | Key | How a question reaches it |
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|---|---|---|
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| Articles | article id | the question's categories → the published article filed under one |
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| Disciplines | top-level category id | the question's categories, rolled up to the top of the tree |
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| Systems | organ-system tag id | the question's symptom keyword → the system that keyword is filed under |
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A question counts towards **every** ancestor it has, not just its primary
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category, and `question_category_links` adds cross-filed categories on top. So
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one answer on *Kawasaki disease* raises the count for Kawasaki, for Cardiology,
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and for the Kawasaki article.
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Systems is the odd one: no question is tagged with an organ system directly.
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It carries a symptom keyword (`question_tags.type = 'keyword'`) and 726 of
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those keywords have a system as `parent_id`. That path reaches **1,502 of
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2,948 questions**, which is why that tab states its own coverage instead of
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implying it can see the whole bank.
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### The four numbers per row
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```python
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accuracy = 100 * correct / answered # what you actually scored
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coverage = 100 * seen / available # how much of it you have met
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relevance = 100 * available / denominator # how much of the bank it is
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readiness = 100 * (c + 8 * overall) / (n + 8) # accuracy, shrunk
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```
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**Readiness** is the only one that is not arithmetic on raw counts. It is a
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plain empirical-Bayes shrinkage: a category's accuracy is pulled toward the
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learner's own overall accuracy in proportion to how few answers it has. With
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`READINESS_PRIOR_ANSWERS = 8`, one lucky question in a category does not read
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as mastery and one unlucky one does not read as a gap — the category has to
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earn its distance from your average. It stays locked until
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`READINESS_UNLOCK_ANSWERS = 40` answers exist at all, because before that the
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learner's own average is not a stable thing to shrink toward.
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It is **not** a psychometric score and not a prediction of any exam. The
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`basis` string returned with every response says so, in those words, because a
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number on a dashboard acquires authority it has not earned unless something
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tells the reader what it is.
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**Relevance** divides by `bank_total` normally, but by `grouped_total` for
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Systems — dividing by the whole bank when the grouping can only see half of it
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would make every system look half as relevant as it is.
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### Ranking
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```python
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gap = max(0, (baseline - score) / 100) # how far below your own average
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unseen = 1 - coverage / 100
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priority = gap * relevance + 0.25 * unseen * relevance
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```
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`baseline` is the learner's overall accuracy (70% before there is one). So a
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topic rises when it is **weak** *and* **a big part of the bank**. Material never
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touched has no gap to measure, so it enters at `gap = 0.5` and is carried by
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the `unseen` term at quarter weight — enough to surface, not enough to crowd
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out a topic you are demonstrably failing.
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The top three rows with any answers are flagged `is_focus_area`.
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---
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## 2. The adaptive session
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`adaptive_select()` — `backend/app/services/quiz_builder.py`.
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Four steps:
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1. **Candidates.** The filtered bank for the chosen categories/state/difficulty.
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2. **Unanswered first**, sorted by the accuracy of their category — so a
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question you have never seen, in the area you are worst at, comes first. A
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category you have never answered in scores `0.5`, which puts it mid-pack.
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3. **Then recycle.** If there are not enough unanswered, previously answered
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questions are pulled back in, weighted `1 - 0.85` if you got it right and
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`1 - 0.25` if you got it wrong — so a question you failed is roughly five
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times likelier to return than one you passed. Ties break toward the oldest
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incorrect answer.
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4. **Damping.** Each time a category is picked, its weight is halved
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(`damping[category] *= 0.5`). Without this a single weak category would fill
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the whole session; with it, the session walks across your weak areas.
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### Where it is weaker than it looks
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- **`.limit(2000)` on the candidate query.** The bank is 2,948 questions, so
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adaptive selection cannot currently see about a third of it, and which third
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depends on database order. This is a real cap, not a tuning choice.
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- **`accuracy()` is computed per category from a linear scan** —
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`next((r[1] for r in rows if r[0] == qid), None)` inside a loop over every
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answer. That is O(answers × candidates); at 2,000 candidates and a few
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thousand answers it is the slowest part of building a session.
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- **Difficulty is not used in the ordering**, only as a filter. A genuine
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adaptive scheme would move up and down the difficulty range as you do well
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or badly; this one does not.
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- **Step 2 sorts by raw category accuracy, not readiness.** The shrinkage that
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the recommendations page applies precisely so small samples do not mislead is
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not applied here, so a category with one wrong answer can sort first.
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The first two are defects and are worth fixing. The last two are honest
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limitations of the current design and would be the substance of a better one.
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---
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## 3. What the adaptive session should be
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Written 2026-09-11, after the exam blueprint landed. The design below is not
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built; this is the argument for it, so the decision is on paper before the code
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is.
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### The thing that was missing
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Every version of this so far has ranked questions by **how badly you are doing**
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and nothing else. That is half a question. The other half is **how much it
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matters**, and until today there was nothing in the database that could answer
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it — so the code guessed, by treating every category as equally worth an hour.
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`exam_blueprints.weight` answers it now. The ABP publishes that preventive care
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is 12% of a general paediatrics paper and rheumatology is 2%. Six points of
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weakness in preventive care costs six times what the same weakness costs in
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rheumatology, and a session that does not know this will spend your evening in
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the wrong place while looking perfectly reasonable.
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### The score
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For each topic, one number — the marks you would expect to gain by studying it:
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```
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expected_gain = relevance × headroom × confidence
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```
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* **relevance** — the domain's published weight, divided among the topics under
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it. From the blueprint. This is the part that is a fact rather than a model.
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* **headroom** — `1 − readiness`. How much of that share you are currently
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losing. Readiness is the shrunk accuracy the recommendations page already
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computes, *not* raw accuracy: one wrong answer out of one must not read as
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"you know nothing about neonatology".
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* **confidence** — how much the estimate can be trusted, `n / (n + k)`. A topic
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you have answered twice cannot outrank one you have answered forty times on
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the strength of a bad afternoon. This is what stops the session chasing noise.
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Sort topics by expected gain; fill the session from the top, damped as now so
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one topic cannot take the whole session.
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### Then, within a topic: difficulty that moves
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The current session filters by difficulty and then ignores it. It should walk:
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start near the learner's demonstrated level for that topic, step up after two
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right, step down after one wrong. The point is not to be hard, it is to sit
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where the information is — a question you would get right nine times in ten
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teaches nothing, and neither does one you would get right once in ten.
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### And a floor on coverage
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A pure gain ranking will never show you a topic you are already good at, which
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is how people arrive at an exam having forgotten something they knew in March.
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Reserve a share of every session — a fifth, say — for **spaced return**: topics
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you were right about, longest ago first. This is the one part that should not be
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optimised, because its whole purpose is to be unwelcome.
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### What has to be fixed first
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The two defects named in §2 are load-bearing here, not tidying:
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* **`.limit(2000)`** means a third of the bank is invisible to selection. A
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scheme that reasons carefully about which question matters most, over an
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arbitrary two-thirds of the questions, is a scheme that reasons carefully
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about the wrong set.
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* **The O(answers × candidates) accuracy scan** is already the slowest part of
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building a session. This design asks for per-topic readiness *and* per-topic
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difficulty, which makes it worse. It needs to become one grouped query.
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### What this deliberately does not do
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No item-response theory, no per-question difficulty estimated from other
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learners' answers. Both would be better with enough data and worse without it,
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and a bank this size with a handful of learners does not have it. The weights
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are published, the readiness is measured, and the arithmetic above can be
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explained to a learner in two sentences — which is the point, because a session
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that cannot say why it chose a question is asking to be trusted rather than
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earning it.
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