feat: more comprehensive cohort report
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+229
-1
@@ -1020,6 +1020,13 @@ pub struct CohortDiagnostic {
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pub predictions: PredictionSummary,
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/// Questions worth revisiting before reuse, worst first.
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pub revise: Vec<CohortQuestionRow>,
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/// The dropped items, described but not measured.
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///
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/// Kept out of [`CohortDiagnostic::questions`] so that no statistic above
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/// silently includes an item that was thrown out, and reported alongside it
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/// in the evidence section so that dropping a question does not erase the
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/// evidence for having dropped it.
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pub dropped_detail: Vec<CohortQuestionRow>,
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/// One row per form, when more than one was given.
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#[serde(skip_serializing_if = "Vec::is_empty")]
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pub forms: Vec<FormRow>,
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@@ -1138,6 +1145,26 @@ pub struct CohortQuestionRow {
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/// What those targets ask, in the course's own words.
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#[serde(skip_serializing_if = "Vec::is_empty")]
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pub target_texts: Vec<String>,
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/// Whether the item was dropped from scoring.
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///
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/// A dropped item carries no p, r, or D, and appears in none of the
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/// statistics above. It still appears in the evidence section, because the
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/// option spread that justified dropping it is the record of why, and that
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/// record should survive re-running the report afterwards.
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#[serde(skip_serializing_if = "std::ops::Not::not")]
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pub dropped: bool,
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/// Whether the drop was applied as full credit to everyone.
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#[serde(skip_serializing_if = "std::ops::Not::not")]
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pub dropped_full_credit: bool,
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/// The question as written.
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///
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/// The instructor report reads better with it than without: a row of option
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/// shares says a distractor drew 44% of the class, and only the stem says
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/// whether that is a second defensible reading. Absent when the item has
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/// left the bank, and withheld from any report that is not the instructor
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/// copy.
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#[serde(skip_serializing_if = "Option::is_none")]
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pub stem: Option<String>,
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/// Where the item was taught, as lecture titles and slide numbers.
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#[serde(skip_serializing_if = "Vec::is_empty")]
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pub taught_in: Vec<String>,
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@@ -1185,8 +1212,25 @@ pub struct CohortQuestionRow {
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#[derive(Debug, Clone, Serialize)]
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#[serde(rename_all = "kebab-case")]
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pub struct OptionRow {
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/// The bank letter.
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/// The bank letter, which is the one every statistic is keyed by.
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pub letter: String,
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/// The option as written, for a report that shows the question.
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///
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/// Absent when the item is no longer in the bank, which is why this is an
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/// option rather than an empty string: a missing option and an empty one
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/// are different facts.
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#[serde(skip_serializing_if = "Option::is_none")]
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pub text: Option<String>,
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/// What this option was lettered on each printed form, worst case one entry
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/// per form.
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///
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/// Shuffling means the bank's option C is a different letter on every form,
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/// so a statistic reported against C cannot be checked against a student's
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/// paper without this map. It is the first thing anyone needs when a student
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/// brings a paper to office hours, and working it out by hand from a seal is
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/// the kind of task that gets done wrong once and then trusted.
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#[serde(skip_serializing_if = "Vec::is_empty")]
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pub printed: Vec<PrintedLetter>,
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/// How many chose it.
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pub count: usize,
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/// The share who chose it.
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@@ -1200,6 +1244,16 @@ pub struct OptionRow {
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pub nonfunctioning: bool,
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}
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/// What one bank option was lettered on one form.
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#[derive(Debug, Clone, Serialize)]
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#[serde(rename_all = "kebab-case")]
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pub struct PrintedLetter {
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/// The form id.
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pub form: String,
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/// The letter this option carried on that form's paper.
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pub letter: String,
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}
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/// One form's summary.
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#[derive(Debug, Clone, Serialize)]
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#[serde(rename_all = "kebab-case")]
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@@ -1502,11 +1556,17 @@ pub fn cohort(
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})
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.collect();
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// The printed lettering per form, computed from the same two functions the
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// exporter and the seal use, so the letters here are the letters on the
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// paper rather than a second guess at them.
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let forms: Vec<&crate::assessment::Form> = record.forms.iter().collect();
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let questions: Vec<CohortQuestionRow> = analysis
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.items
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.iter()
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.map(|item| {
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let meta = item_meta.get(&item.number);
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let entry = item.item_ref.as_deref().and_then(|uid| catalog.get(uid));
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CohortQuestionRow {
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number: item.number,
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item: item.item_ref.clone(),
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@@ -1533,6 +1593,9 @@ pub fn cohort(
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.collect()
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})
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.unwrap_or_default(),
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dropped: false,
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dropped_full_credit: false,
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stem: entry.map(|e| e.item.stem.clone()),
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difficulty_band: difficulty_band(item.p_value).to_string(),
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discrimination_band: discrimination_band(item.point_biserial).to_string(),
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p_value: item.p_value,
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@@ -1544,6 +1607,23 @@ pub fn cohort(
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.options
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.values()
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.map(|option| OptionRow {
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text: entry.and_then(|e| {
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e.item
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.options
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.iter()
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.find(|o| o.id == option.letter)
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.map(|o| o.text.clone())
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}),
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printed: entry
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.map(|e| {
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printed_letters(
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&forms,
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&e.item,
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item.item_ref.as_deref(),
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&option.letter,
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)
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})
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.unwrap_or_default(),
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letter: option.letter.clone(),
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count: option.count,
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rate: option.rate,
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@@ -1582,6 +1662,106 @@ pub fn cohort(
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.collect();
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dropped_questions.sort_by_key(|d| d.number);
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// The dropped items, described from the responses rather than from the
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// scoring. Dropping an item overrides its credit, so p, r, and D are
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// meaningless for it and are left out. What students actually marked is
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// untouched by the drop, and that spread is the evidence that justified it.
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let mut dropped_detail: Vec<CohortQuestionRow> = dropped_questions
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.iter()
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.map(|dropped| {
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let placement = record.placement(dropped.number);
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let uid = placement.map(|p| p.item.clone());
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let entry = uid.as_deref().and_then(|uid| catalog.get(uid));
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let responses = set.for_item(dropped.number);
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let answered = responses
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.iter()
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.filter(|r| !r.chosen().is_empty())
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.count()
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.max(1);
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let keyed: BTreeSet<String> = placement
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.map(|p| p.key.iter().cloned().collect())
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.filter(|k: &BTreeSet<String>| !k.is_empty())
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.or_else(|| entry.map(|e| e.item.key_letters().into_iter().collect()))
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.unwrap_or_default();
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// One row per option the item declares, so an option nobody
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// marked still shows as unchosen rather than vanishing.
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let options: Vec<OptionRow> = entry
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.map(|e| {
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e.item
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.options
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.iter()
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.map(|option| {
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let count = responses
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.iter()
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.filter(|r| r.chosen().iter().any(|l| *l == option.id))
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.count();
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OptionRow {
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text: Some(option.text.clone()),
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printed: printed_letters(
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&forms,
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&e.item,
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uid.as_deref(),
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&option.id,
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),
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letter: option.id.clone(),
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count,
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rate: count as f64 / answered as f64,
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is_key: keyed.contains(&option.id),
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point_biserial: None,
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nonfunctioning: false,
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}
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})
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.collect()
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})
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.unwrap_or_default();
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let targets = placement
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.map(|p| p.learning_targets.clone())
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.unwrap_or_default();
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CohortQuestionRow {
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number: dropped.number,
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item: uid.clone(),
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level: placement.and_then(|p| p.level).map(|l| l.code()),
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target_texts: targets.iter().map(|id| course.text_for(id)).collect(),
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targets,
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dropped: true,
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dropped_full_credit: dropped.full_credit,
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stem: entry.map(|e| e.item.stem.clone()),
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taught_in: uid
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.as_deref()
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.map(|uid| taught_in(catalog, uid))
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.unwrap_or_default(),
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lectures: entry
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.map(|e| e.item.sources.iter().map(|s| s.lecture.clone()).collect())
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.unwrap_or_default(),
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difficulty_band: String::new(),
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discrimination_band: String::new(),
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// The keyed share before the override, which is the closest
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// honest reading of how the item performed. It is not a
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// p-value: it counts marks, not credit.
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p_value: options
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.iter()
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.filter(|o| o.is_key)
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.map(|o| o.rate)
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.sum::<f64>()
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.min(1.0),
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point_biserial: None,
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discrimination: None,
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blank_rate: responses.iter().filter(|r| r.chosen().is_empty()).count() as f64
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/ responses.len().max(1) as f64,
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key: keyed.iter().cloned().collect(),
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options,
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flags: Vec::new(),
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notes: Vec::new(),
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prediction_notes: Vec::new(),
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calibrated: false,
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by_form: BTreeMap::new(),
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}
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})
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.collect();
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dropped_detail.sort_by_key(|q| q.number);
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let default_options = course.policy.options_per_item;
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let triage = triage(&questions, threshold, default_options);
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let predictions = prediction_summary(analysis);
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@@ -1609,6 +1789,7 @@ pub fn cohort(
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gaps,
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questions,
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revise,
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dropped_detail,
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forms: form_rows(set, cohort),
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blueprint: crate::select::check_blueprint(record, course),
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patterns: cohort
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@@ -1669,6 +1850,53 @@ fn taught_in(catalog: &Catalog, uid: &str) -> Vec<String> {
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.collect()
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}
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/// Where one bank option landed on each printed form.
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///
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/// # Arguments
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///
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/// * `forms` - the record's forms, in declaration order.
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/// * `item` - the bank item, for its option count and lettering.
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/// * `uid` - the item's global id, which salts the permutation.
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/// * `letter` - the bank letter to locate.
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///
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/// # Returns
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///
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/// One entry per form that permutes its options. Forms printing the bank order
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/// unchanged are left out, since an entry saying C was printed as C is noise on
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/// every row.
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fn printed_letters(
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forms: &[&crate::assessment::Form],
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item: &crate::item::Item,
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uid: Option<&str>,
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letter: &str,
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) -> Vec<PrintedLetter> {
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let Some(uid) = uid else {
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return Vec::new();
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};
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let Some(source) = item.options.iter().position(|o| o.id == letter) else {
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return Vec::new();
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};
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let n = item.options.len();
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let mut out = Vec::new();
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for form in forms {
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if !form.shuffle_options {
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continue;
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}
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// The same permutation the exporter and the seal use, so these are the
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// letters on the paper rather than a second guess at them.
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let order = crate::select::option_order(form, uid, n);
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// `order[position] == source` means the option printed in that slot is
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// the one being asked about.
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if let Some(position) = order.iter().position(|index| *index == source) {
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out.push(PrintedLetter {
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form: form.id.clone(),
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letter: crate::seal::printed_letter(position),
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});
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}
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}
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out
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}
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/// The difficulty band a p-value falls in.
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///
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/// Three bands rather than five. The only distinction that changes what you do
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