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2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x | /**
* Session Planner - Generates adaptive practice session plans
*
* Creates a three-part plan based on:
* - Part 1: Abacus practice (use physical abacus, vertical format)
* - Part 2: Visualization (mental math by picturing beads, vertical format)
* - Part 3: Linear (mental math in sentence format, e.g., "45 + 27 = ?")
*
* Each part's duration is based on configured weights.
* Skills and difficulty are determined by:
* - Student's current curriculum position
* - Skill mastery levels (what needs work vs. what's mastered)
* - Spaced repetition needs (when skills were last practiced)
*/
import { createId } from '@paralleldrive/cuid2'
import { and, desc, eq, inArray, isNull, sql } from 'drizzle-orm'
import { db, schema } from '@/db'
import { isActive, isVisualReady, type PlayerSkillMastery } from '@/db/schema/player-skill-mastery'
import type { GameResultsReport } from '@/lib/arcade/game-sdk/types'
import {
calculateMasteryWeight,
calculateSessionHealth,
DEFAULT_GAME_BREAK_SETTINGS,
DEFAULT_PLAN_CONFIG,
type GameBreakSettings,
initRetryState,
isInRetryEpoch,
MAX_RETRY_EPOCHS,
type NewSessionPlan,
type PartSummary,
type PlanGenerationConfig,
type ProblemSlot,
type SessionHealth,
type SessionPart,
type SessionFlowState,
type SessionPartType,
type SessionPlan,
type SessionRetryState,
type SessionSummary,
type SlotResult,
} from '@/db/schema/session-plans'
import {
buildStudentSkillHistoryFromRecords,
calculateMaxSkillCost,
createSkillCostCalculator,
type SkillCostCalculator,
} from '@/utils/skillComplexity'
import { computeBktFromHistory, type SkillBktResult } from './bkt'
import {
BKT_INTEGRATION_CONFIG,
DEFAULT_PROBLEM_GENERATION_MODE,
MIN_SECONDS_PER_PROBLEM,
WEAK_SKILL_THRESHOLDS,
type ProblemGenerationMode,
} from './config'
import {
type CurriculumPhase,
getPhase,
getPhaseDisplayInfo,
type getPhaseSkillConstraints,
} from './definitions'
import { generateProblemFromConstraints } from './problem-generator'
import {
getAllSkillMastery,
getPlayerCurriculum,
getRecentSessions,
recordSkillAttemptsWithHelp,
} from './progress-manager'
import { getWeakSkillIds, type SessionMode } from './session-mode'
import { revokeSharesForSession } from '@/lib/session-share'
import {
computeComfortLevel,
computeComfortLevelByMode,
applyTermCountOverride,
} from './comfort-level'
import {
computeTermCountRange,
parseTermCountScaling,
type TermCountExplanation,
type TermCountScalingConfig,
} from './config/term-count-scaling'
import { appSettings } from '@/db/schema'
import { applyFlowEvent, type SessionFlowEvent } from './session-flow'
// ============================================================================
// Plan Generation
// ============================================================================
/**
* Which session parts to include in the generated plan
*/
export interface EnabledParts {
abacus: boolean
visualization: boolean
linear: boolean
}
export interface GenerateSessionPlanOptions {
playerId: string
durationMinutes: number
config?: Partial<PlanGenerationConfig>
enabledParts?: EnabledParts
confidenceThreshold?: number
problemGenerationMode?: ProblemGenerationMode
sessionMode?: SessionMode
gameBreakSettings?: GameBreakSettings
/** Override the number of problems per part (for debug/testing) */
overrideProblemsPerPart?: number
/** When true, shuffle problem purposes within each part (default: true) */
shufflePurposes?: boolean
/** Comfort level adjustment from problem length preference (shorter=-0.3, recommended=0, longer=+0.2) */
comfortAdjustment?: number
/** Optional progress callback for background task integration */
onProgress?: (event: SessionPlanProgressEvent) => void
}
/** Progress events emitted during session plan generation */
export type SessionPlanProgressEvent =
| { type: 'plan_loading_data'; message: string }
| { type: 'plan_analyzing_skills'; message: string; skillCount: number }
| {
type: 'plan_structure_ready'
message: string
parts: Array<{ type: string; problemCount: number }>
}
| {
type: 'plan_generating_problem'
partType: string
partLabel: string
current: number
total: number
}
| { type: 'plan_part_complete'; partType: string; problemCount: number; durationMs: number }
| { type: 'plan_saving'; message: string }
| {
type: 'plan_complete'
planId: string
timing: {
totalMs: number
loadDataMs: number
bktMs: number
parts: Array<{
type: string
totalMs: number
problemCount: number
avgProblemMs: number
}>
saveMs: number
}
}
/**
* Error thrown when a player already has an active session
*/
export class ActiveSessionExistsError extends Error {
code = 'ACTIVE_SESSION_EXISTS' as const
existingSession: SessionPlan
constructor(existingSession: SessionPlan) {
super('An active session already exists for this player')
this.name = 'ActiveSessionExistsError'
this.existingSession = existingSession
}
}
/**
* Error thrown when trying to generate a session for a student with no skills enabled
*/
export class NoSkillsEnabledError extends Error {
code = 'NO_SKILLS_ENABLED' as const
constructor() {
super(
'Cannot generate a practice session: no skills are enabled for this student. ' +
'Please enable at least one skill in the skill selector before starting a session.'
)
this.name = 'NoSkillsEnabledError'
}
}
/**
* Generate a three-part session plan for a student
*
* @throws {ActiveSessionExistsError} If the player already has an active session that hasn't timed out
* @throws {NoSkillsEnabledError} If the player has no skills enabled for practice
*/
export async function generateSessionPlan(
options: GenerateSessionPlanOptions
): Promise<SessionPlan> {
const {
playerId,
durationMinutes,
config: configOverrides,
enabledParts,
problemGenerationMode = DEFAULT_PROBLEM_GENERATION_MODE,
sessionMode,
gameBreakSettings = DEFAULT_GAME_BREAK_SETTINGS,
overrideProblemsPerPart,
shufflePurposes = true,
comfortAdjustment = 0,
onProgress,
} = options
const totalStartTime = performance.now()
const config = { ...DEFAULT_PLAN_CONFIG, ...configOverrides }
// Default: all parts enabled
const partsToInclude: EnabledParts = enabledParts ?? {
abacus: true,
visualization: true,
linear: true,
}
// Check for existing active session (one active session per kid rule)
const existingActive = await getActiveSessionPlan(playerId)
if (existingActive) {
const sessionAgeMs = Date.now() - new Date(existingActive.createdAt).getTime()
const timeoutMs = config.sessionTimeoutHours * 60 * 60 * 1000
if (sessionAgeMs > timeoutMs) {
// Session has timed out - auto-abandon it
await abandonSessionPlan(existingActive.id)
} else {
// Session is still active - throw error with session data
throw new ActiveSessionExistsError(existingActive)
}
}
onProgress?.({ type: 'plan_loading_data', message: 'Loading student data...' })
// 1. Load student state and app config (in parallel for performance)
const loadDataStart = performance.now()
const [curriculum, skillMastery, recentSessions, problemHistory, dbSettings] = await Promise.all([
getPlayerCurriculum(playerId),
getAllSkillMastery(playerId),
getRecentSessions(playerId, 10),
// Only load problem history for BKT in adaptive modes
problemGenerationMode === 'adaptive' || problemGenerationMode === 'adaptive-bkt'
? getRecentSessionResults(playerId, BKT_INTEGRATION_CONFIG.sessionHistoryDepth)
: Promise.resolve([]),
// Load term count scaling config from DB
db.select().from(appSettings).where(eq(appSettings.id, 'default')).limit(1),
])
const loadDataMs = performance.now() - loadDataStart
// Parse DB term count scaling config (falls back to defaults if null/invalid)
const termCountScalingConfig = parseTermCountScaling(dbSettings[0]?.termCountScaling ?? null)
// Compute BKT if in adaptive modes and we have problem history
const bktStart = performance.now()
let bktResults: Map<string, SkillBktResult> | undefined
let byModeBkt: ReturnType<typeof computeBktFromHistory>['byMode']
const usesBktTargeting =
problemGenerationMode === 'adaptive' || problemGenerationMode === 'adaptive-bkt'
if (usesBktTargeting && problemHistory.length > 0) {
const bktResult = computeBktFromHistory(problemHistory)
bktResults = new Map(bktResult.skills.map((s) => [s.skillId, s]))
byModeBkt = bktResult.byMode
// Debug: Log BKT usage
if (process.env.DEBUG_SESSION_PLANNER === 'true') {
console.log(
`[SessionPlanner] Mode: ${problemGenerationMode}, BKT skills: ${bktResult.skills.length}`
)
for (const skill of bktResult.skills.slice(0, 3)) {
console.log(
` ${skill.skillId}: pKnown=${(skill.pKnown * 100).toFixed(0)}%, ` +
`confidence=${skill.confidence.toFixed(2)}, opportunities=${skill.opportunities}`
)
}
}
} else if (process.env.DEBUG_SESSION_PLANNER === 'true') {
console.log(
`[SessionPlanner] Mode: ${problemGenerationMode}, no BKT (usesBktTargeting=${usesBktTargeting}, history=${problemHistory.length})`
)
}
const bktMs = performance.now() - bktStart
const practicingCount = skillMastery.filter((s) => isActive(s.practiceLevel)).length
onProgress?.({
type: 'plan_analyzing_skills',
message: `Analyzing ${practicingCount} skill${practicingCount !== 1 ? 's' : ''}...`,
skillCount: practicingCount,
})
// Build student-aware cost calculator for complexity budgeting
const studentHistory = buildStudentSkillHistoryFromRecords(skillMastery)
const costCalculator = createSkillCostCalculator(studentHistory, {
bktResults,
mode: problemGenerationMode,
})
// Debug: Show multipliers for all modes (not just when BKT data exists)
if (process.env.DEBUG_SESSION_PLANNER === 'true') {
console.log(`[SessionPlanner] Multiplier comparison (mode=${problemGenerationMode}):`)
const practicingIds = skillMastery
.filter((s) => isActive(s.practiceLevel))
.map((s) => s.skillId)
for (const skillId of practicingIds.slice(0, 5)) {
const multiplier = costCalculator.getMultiplier(skillId)
const isPracticing = costCalculator.getIsPracticing(skillId)
const bkt = costCalculator.getBktResult(skillId)
console.log(
` ${skillId}: mult=${multiplier.toFixed(2)} inRotation=${isPracticing} ` +
`bkt_pKnown=${bkt?.pKnown.toFixed(2) ?? 'N/A'} bkt_conf=${bkt?.confidence.toFixed(2) ?? 'N/A'}`
)
}
}
// Calculate max skill cost for dynamic visualization budget
// This ensures the student's most expensive skill can appear in visualization
const practicingSkillIds = skillMastery
.filter((s) => isActive(s.practiceLevel))
.map((s) => s.skillId)
const studentMaxSkillCost = calculateMaxSkillCost(costCalculator, practicingSkillIds)
// Compute comfort level for dynamic term count scaling
const rawComfortResult = sessionMode
? computeComfortLevel(bktResults, practicingSkillIds, sessionMode)
: {
comfortLevel: 0.3,
factors: {
avgMastery: null,
sessionMode: 'maintenance',
modeMultiplier: 1.0,
skillCountBonus: 0,
} as TermCountExplanation['factors'],
}
// Compute per-mode comfort levels
const comfortByMode = sessionMode
? computeComfortLevelByMode(byModeBkt, practicingSkillIds, sessionMode)
: undefined
// Apply comfort adjustment from problem length preference
const rawComfortLevel = rawComfortResult.comfortLevel
const adjustedComfortLevel = Math.max(0, Math.min(1, rawComfortLevel + comfortAdjustment))
const comfortResult = {
...rawComfortResult,
comfortLevel: adjustedComfortLevel,
}
if (process.env.DEBUG_SESSION_PLANNER === 'true') {
console.log(
`[SessionPlanner] Comfort level: ${(comfortResult.comfortLevel * 100).toFixed(0)}% ` +
`(raw=${(rawComfortLevel * 100).toFixed(0)}%, adj=${comfortAdjustment}, ` +
`avgMastery=${comfortResult.factors.avgMastery?.toFixed(2) ?? 'N/A'}, ` +
`mode=${comfortResult.factors.sessionMode}, mult=${comfortResult.factors.modeMultiplier}, ` +
`bonus=${comfortResult.factors.skillCountBonus.toFixed(3)})`
)
}
// Get current phase
const currentPhaseId = curriculum?.currentPhaseId || 'L1.add.+1.direct'
const currentPhase = getPhase(currentPhaseId)
// 2. Calculate personalized timing
// Clamp to MIN_SECONDS_PER_PROBLEM to prevent generating excessive problems
// when student timing data is anomalously low (see session-timing.ts for rationale)
const avgTimeSeconds = Math.max(
MIN_SECONDS_PER_PROBLEM,
calculateAvgTimePerProblem(recentSessions) || config.defaultSecondsPerProblem
)
// 3. Build skill constraints from the student's ACTUAL practicing skills
// Two constraint sets: one for abacus parts (all active skills) and one for
// visualization/linear parts (only visual-ready skills)
const activeSkills = skillMastery.filter((s) => isActive(s.practiceLevel))
const visualReadySkills = skillMastery.filter((s) => isVisualReady(s.practiceLevel))
// Cannot generate a session without any skills enabled
if (activeSkills.length === 0) {
throw new NoSkillsEnabledError()
}
const abacusConstraints = buildConstraintsFromPracticingSkills(activeSkills)
const visualConstraints =
visualReadySkills.length > 0
? buildConstraintsFromPracticingSkills(visualReadySkills)
: abacusConstraints // Fallback: if no visual skills, use abacus constraints
// Legacy alias for compatibility within this function
const practicingSkillConstraints = abacusConstraints
// Find skills needing spaced repetition review
const needsReview = findSkillsNeedingReview(skillMastery, config.reviewIntervalDays)
// Identify weak skills for targeting
// When sessionMode is provided, use its pre-computed weak skills (single source of truth)
// This ensures the session targets exactly what was shown in the UI (no "rug-pulling")
let weakSkills: string[]
if (sessionMode) {
// Use pre-computed weak skills from sessionMode
weakSkills = getWeakSkillIds(sessionMode)
if (process.env.DEBUG_SESSION_PLANNER === 'true') {
console.log(
`[SessionPlanner] Using weak skills from sessionMode (${sessionMode.type}): ${weakSkills.length}`
)
}
} else {
// Fallback: compute locally (for backwards compatibility)
weakSkills = usesBktTargeting ? identifyWeakSkills(bktResults) : []
}
if (process.env.DEBUG_SESSION_PLANNER === 'true' && weakSkills.length > 0) {
console.log(`[SessionPlanner] Targeting ${weakSkills.length} weak skills:`)
for (const skillId of weakSkills) {
const bkt = bktResults?.get(skillId)
console.log(` ${skillId}: pKnown=${(bkt?.pKnown ?? 0 * 100).toFixed(0)}%`)
}
}
// 4. Build parts using STUDENT'S MASTERED SKILLS (only enabled parts)
// Auto-disable visualization/linear parts when no visual-ready skills exist
const hasVisualSkills = visualReadySkills.length > 0
const enabledPartTypes = (['abacus', 'visualization', 'linear'] as const).filter(
(type) => partsToInclude[type] && (type === 'abacus' || hasVisualSkills)
)
const totalEnabledWeight = enabledPartTypes.reduce(
(sum, type) => sum + config.partTimeWeights[type],
0
)
// Build slot structures (without problems) first to report structure
const partSlotStructures: Array<{
partType: SessionPartType
normalizedWeight: number
adjustedModeComfortLevel: number
modeComfortFactors: TermCountExplanation['factors']
rawModeComfortLevel: number
}> = []
for (const partType of enabledPartTypes) {
const normalizedWeight = config.partTimeWeights[partType] / totalEnabledWeight
const modeComfort = comfortByMode?.[partType] ?? rawComfortResult
const rawModeComfortLevel = modeComfort.comfortLevel
const adjustedModeComfortLevel = Math.max(
0,
Math.min(1, rawModeComfortLevel + comfortAdjustment)
)
partSlotStructures.push({
partType,
normalizedWeight,
adjustedModeComfortLevel,
modeComfortFactors: modeComfort.factors,
rawModeComfortLevel,
})
}
// Build only enabled parts with normalized time weights
const parts: SessionPart[] = []
let partNumber = 1 as 1 | 2 | 3
const partTimings: Array<{
type: string
totalMs: number
problemCount: number
avgProblemMs: number
}> = []
// Build slot structures first (fast) to get problem counts for progress reporting
const previewParts: Array<{ type: string; problemCount: number }> = []
for (const { partType, normalizedWeight } of partSlotStructures) {
const partDurationMinutes = durationMinutes * normalizedWeight
const partProblemCount =
overrideProblemsPerPart ??
Math.max(2, Math.floor((partDurationMinutes * 60) / avgTimeSeconds))
previewParts.push({ type: partType, problemCount: partProblemCount })
}
onProgress?.({
type: 'plan_structure_ready',
message: `Generating ${previewParts.reduce((s, p) => s + p.problemCount, 0)} problems...`,
parts: previewParts,
})
const PART_TYPE_LABELS: Record<string, string> = {
abacus: 'Abacus',
visualization: 'Visualization',
linear: 'Linear',
}
for (const {
partType,
normalizedWeight,
adjustedModeComfortLevel,
modeComfortFactors,
rawModeComfortLevel,
} of partSlotStructures) {
const partStartTime = performance.now()
// Use visual constraints for visualization/linear parts, abacus constraints for abacus parts
const partConstraints = partType === 'abacus' ? abacusConstraints : visualConstraints
const part = await buildSessionPartAsync(
partNumber,
partType,
durationMinutes,
avgTimeSeconds,
{
...config,
partTimeWeights: {
...config.partTimeWeights,
[partType]: normalizedWeight,
},
},
partConstraints,
needsReview,
currentPhase,
normalizedWeight,
costCalculator,
studentMaxSkillCost,
weakSkills,
overrideProblemsPerPart,
shufflePurposes,
adjustedModeComfortLevel,
modeComfortFactors,
comfortAdjustment,
rawModeComfortLevel,
termCountScalingConfig,
onProgress
? (current, total) => {
onProgress({
type: 'plan_generating_problem',
partType,
partLabel: PART_TYPE_LABELS[partType] ?? partType,
current,
total,
})
}
: undefined
)
parts.push(part)
const partMs = performance.now() - partStartTime
partTimings.push({
type: partType,
totalMs: partMs,
problemCount: part.slots.length,
avgProblemMs: part.slots.length > 0 ? partMs / part.slots.length : 0,
})
onProgress?.({
type: 'plan_part_complete',
partType,
problemCount: part.slots.length,
durationMs: partMs,
})
partNumber = (partNumber + 1) as 1 | 2 | 3
}
// 5. Build summary
const summary = buildSummary(parts, currentPhase, durationMinutes)
// 6. Calculate total problems
const totalProblemCount = parts.reduce((sum, part) => sum + part.slots.length, 0)
onProgress?.({ type: 'plan_saving', message: 'Saving session plan...' })
const saveStart = performance.now()
const plan: NewSessionPlan = {
id: createId(),
playerId,
targetDurationMinutes: durationMinutes,
estimatedProblemCount: totalProblemCount,
avgTimePerProblemSeconds: avgTimeSeconds,
gameBreakSettings,
parts,
summary,
masteredSkillIds: activeSkills.map((s) => s.skillId),
status: 'draft',
currentPartIndex: 0,
currentSlotIndex: 0,
sessionHealth: null,
adjustments: [],
results: [],
createdAt: new Date(),
}
const [savedPlan] = await db.insert(schema.sessionPlans).values(plan).returning()
const saveMs = performance.now() - saveStart
const totalMs = performance.now() - totalStartTime
const timing = {
totalMs,
loadDataMs,
bktMs,
parts: partTimings,
saveMs,
}
if (process.env.DEBUG_SESSION_PLANNER === 'true') {
console.log(
`[SessionPlanner] Timing: total=${totalMs.toFixed(0)}ms, ` +
`loadData=${loadDataMs.toFixed(0)}ms, bkt=${bktMs.toFixed(0)}ms, ` +
`save=${saveMs.toFixed(0)}ms`
)
for (const pt of partTimings) {
console.log(
` ${pt.type}: ${pt.totalMs.toFixed(0)}ms (${pt.problemCount} problems, ` +
`avg ${pt.avgProblemMs.toFixed(1)}ms/problem)`
)
}
}
onProgress?.({
type: 'plan_complete',
planId: savedPlan.id,
timing,
})
return savedPlan
}
/**
* Build a single session part with the appropriate slots
*
* @param weakSkills - Skills identified by BKT as weak (low P(known)).
* These are used for both focus and reinforce slots to prioritize practice.
*/
function buildSessionPart(
partNumber: 1 | 2 | 3,
type: SessionPartType,
totalDurationMinutes: number,
avgTimeSeconds: number,
config: PlanGenerationConfig,
phaseConstraints: ReturnType<typeof getPhaseSkillConstraints>,
needsReview: PlayerSkillMastery[],
currentPhase: CurriculumPhase | undefined,
normalizedWeight?: number,
costCalculator?: SkillCostCalculator,
studentMaxSkillCost?: number,
weakSkills?: string[],
overrideProblemsPerPart?: number,
shufflePurposes = true,
comfortLevel = 0.3,
comfortFactors?: TermCountExplanation['factors'],
comfortAdjustment?: number,
rawComfortLevel?: number,
termCountScalingConfig?: TermCountScalingConfig
): SessionPart {
// Get time allocation for this part (use normalized weight if provided)
const partWeight = normalizedWeight ?? config.partTimeWeights[type]
const partDurationMinutes = totalDurationMinutes * partWeight
const partProblemCount =
overrideProblemsPerPart ?? Math.max(2, Math.floor((partDurationMinutes * 60) / avgTimeSeconds))
// Calculate slot distribution using unified 4-weight proportional allocation.
//
// IMPORTANT: Challenge slots require min complexity budget of 1.
// Basic skills have cost 0, so students with ONLY basic skills can't generate challenge problems.
// When canDoChallenge is false, challenge weight is set to 0 and its share
// automatically redistributes to the other 3 purposes via normalization.
const challengeMinBudget = config.purposeComplexityBounds.challenge[type].min ?? 0
const canDoChallenge =
studentMaxSkillCost !== undefined && studentMaxSkillCost >= challengeMinBudget
const weights = {
focus: config.focusWeight,
reinforce: config.reinforceWeight,
review: config.reviewWeight,
challenge: canDoChallenge ? (config.challengeWeight ?? 0.2) : 0,
}
const totalWeight = weights.focus + weights.reinforce + weights.review + weights.challenge
const focusCount = Math.round((partProblemCount * weights.focus) / totalWeight)
const reinforceCount = Math.round((partProblemCount * weights.reinforce) / totalWeight)
const reviewCount = Math.round((partProblemCount * weights.review) / totalWeight)
// Challenge absorbs rounding remainder (clamped to 0 if canDoChallenge is false)
const challengeCount = canDoChallenge
? Math.max(0, partProblemCount - focusCount - reinforceCount - reviewCount)
: 0
// If no challenge, give remainder to focus
const adjustedFocusCount =
focusCount + (canDoChallenge ? 0 : partProblemCount - focusCount - reinforceCount - reviewCount)
// Build slots
const slots: ProblemSlot[] = []
// Build constraints for focus slots that prioritize weak skills
// This adds BKT-identified weak skills to targetSkills so problem generator prefers them
const focusConstraints =
weakSkills && weakSkills.length > 0
? addWeakSkillsToTargets(phaseConstraints, weakSkills)
: phaseConstraints
if (process.env.DEBUG_SESSION_PLANNER === 'true' && weakSkills && weakSkills.length > 0) {
const targetSkillsList = Object.entries(focusConstraints.targetSkills || {}).flatMap(
([cat, skills]) =>
Object.entries(skills)
.filter(([, v]) => v)
.map(([s]) => `${cat}.${s}`)
)
console.log(`[SessionPlanner] Focus slots will target: ${targetSkillsList.join(', ')}`)
}
// Focus slots: current phase, primary skill (with weak skill targeting)
// Uses adjustedFocusCount which includes redistributed challenge slots for basic-skill-only students
for (let i = 0; i < adjustedFocusCount; i++) {
slots.push(
createSlot(
slots.length,
'focus',
focusConstraints,
type,
config,
costCalculator,
studentMaxSkillCost,
comfortLevel,
comfortFactors,
comfortAdjustment,
rawComfortLevel,
termCountScalingConfig
)
)
}
// Reinforce slots: weak skills (from BKT) get extra practice
// Uses same targeting as focus slots - problems that exercise weak skills
for (let i = 0; i < reinforceCount; i++) {
slots.push(
createSlot(
slots.length,
'reinforce',
focusConstraints, // Same weak skill targeting as focus slots
type,
config,
costCalculator,
studentMaxSkillCost,
comfortLevel,
comfortFactors,
comfortAdjustment,
rawComfortLevel,
termCountScalingConfig
)
)
}
// Review slots: spaced repetition of mastered skills
for (let i = 0; i < reviewCount; i++) {
const skill = needsReview[i % Math.max(1, needsReview.length)]
slots.push(
createSlot(
slots.length,
'review',
skill ? buildConstraintsForSkill(skill, phaseConstraints) : phaseConstraints,
type,
config,
costCalculator,
studentMaxSkillCost,
comfortLevel,
comfortFactors,
comfortAdjustment,
rawComfortLevel,
termCountScalingConfig
)
)
}
// Challenge slots: use same mastered skills constraints (all problems should use student's skills)
for (let i = 0; i < challengeCount; i++) {
slots.push(
createSlot(
slots.length,
'challenge',
phaseConstraints,
type,
config,
costCalculator,
studentMaxSkillCost,
comfortLevel,
comfortFactors,
comfortAdjustment,
rawComfortLevel,
termCountScalingConfig
)
)
}
// Optionally shuffle to interleave purposes (default: shuffled)
const shuffledSlots = shufflePurposes ? intelligentShuffle(slots) : slots
// Generate problems for each slot (persisted in DB for resume capability)
const slotsWithProblems = shuffledSlots.map((slot) => ({
...slot,
problem: generateProblemFromConstraints(slot.constraints, costCalculator),
}))
return {
partNumber,
type,
format: type === 'linear' ? 'linear' : 'vertical',
useAbacus: type === 'abacus',
slots: slotsWithProblems,
estimatedMinutes: Math.round(partDurationMinutes),
}
}
/**
* Async version of buildSessionPart that yields the event loop periodically
* to allow Socket.IO broadcasts to flush during problem generation.
*/
async function buildSessionPartAsync(
partNumber: 1 | 2 | 3,
type: SessionPartType,
totalDurationMinutes: number,
avgTimeSeconds: number,
config: PlanGenerationConfig,
phaseConstraints: ReturnType<typeof getPhaseSkillConstraints>,
needsReview: PlayerSkillMastery[],
currentPhase: CurriculumPhase | undefined,
normalizedWeight?: number,
costCalculator?: SkillCostCalculator,
studentMaxSkillCost?: number,
weakSkills?: string[],
overrideProblemsPerPart?: number,
shufflePurposes = true,
comfortLevel = 0.3,
comfortFactors?: TermCountExplanation['factors'],
comfortAdjustment?: number,
rawComfortLevel?: number,
termCountScalingConfig?: TermCountScalingConfig,
onProblemGenerated?: (current: number, total: number) => void
): Promise<SessionPart> {
// Get time allocation for this part (use normalized weight if provided)
const partWeight = normalizedWeight ?? config.partTimeWeights[type]
const partDurationMinutes = totalDurationMinutes * partWeight
const partProblemCount =
overrideProblemsPerPart ?? Math.max(2, Math.floor((partDurationMinutes * 60) / avgTimeSeconds))
const challengeMinBudget = config.purposeComplexityBounds.challenge[type].min ?? 0
const canDoChallenge =
studentMaxSkillCost !== undefined && studentMaxSkillCost >= challengeMinBudget
const weights = {
focus: config.focusWeight,
reinforce: config.reinforceWeight,
review: config.reviewWeight,
challenge: canDoChallenge ? (config.challengeWeight ?? 0.2) : 0,
}
const totalWeight = weights.focus + weights.reinforce + weights.review + weights.challenge
const focusCount = Math.round((partProblemCount * weights.focus) / totalWeight)
const reinforceCount = Math.round((partProblemCount * weights.reinforce) / totalWeight)
const reviewCount = Math.round((partProblemCount * weights.review) / totalWeight)
const challengeCount = canDoChallenge
? Math.max(0, partProblemCount - focusCount - reinforceCount - reviewCount)
: 0
const adjustedFocusCount =
focusCount + (canDoChallenge ? 0 : partProblemCount - focusCount - reinforceCount - reviewCount)
const slots: ProblemSlot[] = []
const focusConstraints =
weakSkills && weakSkills.length > 0
? addWeakSkillsToTargets(phaseConstraints, weakSkills)
: phaseConstraints
for (let i = 0; i < adjustedFocusCount; i++) {
slots.push(
createSlot(
slots.length,
'focus',
focusConstraints,
type,
config,
costCalculator,
studentMaxSkillCost,
comfortLevel,
comfortFactors,
comfortAdjustment,
rawComfortLevel,
termCountScalingConfig
)
)
}
for (let i = 0; i < reinforceCount; i++) {
slots.push(
createSlot(
slots.length,
'reinforce',
focusConstraints,
type,
config,
costCalculator,
studentMaxSkillCost,
comfortLevel,
comfortFactors,
comfortAdjustment,
rawComfortLevel,
termCountScalingConfig
)
)
}
for (let i = 0; i < reviewCount; i++) {
const skill = needsReview[i % Math.max(1, needsReview.length)]
slots.push(
createSlot(
slots.length,
'review',
skill ? buildConstraintsForSkill(skill, phaseConstraints) : phaseConstraints,
type,
config,
costCalculator,
studentMaxSkillCost,
comfortLevel,
comfortFactors,
comfortAdjustment,
rawComfortLevel,
termCountScalingConfig
)
)
}
for (let i = 0; i < challengeCount; i++) {
slots.push(
createSlot(
slots.length,
'challenge',
phaseConstraints,
type,
config,
costCalculator,
studentMaxSkillCost,
comfortLevel,
comfortFactors,
comfortAdjustment,
rawComfortLevel,
termCountScalingConfig
)
)
}
const shuffledSlots = shufflePurposes ? intelligentShuffle(slots) : slots
// Generate problems with async yielding for Socket.IO broadcast flushing
const slotsWithProblems: ProblemSlot[] = []
for (let i = 0; i < shuffledSlots.length; i++) {
const slot = shuffledSlots[i]
slotsWithProblems.push({
...slot,
problem: generateProblemFromConstraints(slot.constraints, costCalculator),
})
onProblemGenerated?.(i + 1, shuffledSlots.length)
// Yield event loop every 3 problems to allow Socket.IO broadcasts to flush
if (onProblemGenerated && (i + 1) % 3 === 0 && i < shuffledSlots.length - 1) {
await new Promise<void>((r) => setTimeout(r, 0))
}
}
return {
partNumber,
type,
format: type === 'linear' ? 'linear' : 'vertical',
useAbacus: type === 'abacus',
slots: slotsWithProblems,
estimatedMinutes: Math.round(partDurationMinutes),
}
}
// ============================================================================
// Plan Management
// ============================================================================
/**
* Get a session plan by ID
*/
export async function getSessionPlan(planId: string): Promise<SessionPlan | null> {
const result = await db.query.sessionPlans.findFirst({
where: eq(schema.sessionPlans.id, planId),
})
return result ?? null
}
/**
* Check if a session plan has pre-generated problems for all slots
* Old sessions created before problem pre-generation was added will fail this check
*/
function sessionHasPreGeneratedProblems(plan: SessionPlan): boolean {
for (const part of plan.parts) {
for (const slot of part.slots) {
if (!slot.problem) {
return false
}
}
}
return true
}
/**
* Get active session plan for a player (in_progress status)
* Returns the most recent in_progress session if multiple exist
* Auto-abandons sessions that are missing pre-generated problems (legacy data)
*/
export async function getActiveSessionPlan(playerId: string): Promise<SessionPlan | null> {
// Find any session that's not completed or abandoned
// This includes: draft, approved, in_progress
// IMPORTANT: Also check completedAt IS NULL to handle inconsistent data
// where status may be in_progress but completedAt is set
const result = await db.query.sessionPlans.findFirst({
where: and(
eq(schema.sessionPlans.playerId, playerId),
inArray(schema.sessionPlans.status, ['draft', 'approved', 'in_progress']),
isNull(schema.sessionPlans.completedAt)
),
orderBy: (plans, { desc }) => [desc(plans.createdAt)],
})
if (!result) {
return null
}
// Validate session has pre-generated problems
// Old sessions may not have them - auto-abandon those
if (!sessionHasPreGeneratedProblems(result)) {
console.warn(
`[getActiveSessionPlan] Session ${result.id} missing pre-generated problems, auto-abandoning`
)
await abandonSessionPlan(result.id)
// Recursively check for another active session
return getActiveSessionPlan(playerId)
}
return result
}
/**
* Get the most recently completed session plan for a player
* Used for the summary page after completing a session
*/
export async function getMostRecentCompletedSession(playerId: string): Promise<SessionPlan | null> {
const result = await db.query.sessionPlans.findFirst({
where: and(
eq(schema.sessionPlans.playerId, playerId),
eq(schema.sessionPlans.status, 'completed')
),
orderBy: (plans, { desc }) => [desc(plans.completedAt)],
})
return result ?? null
}
/**
* Result from a problem with session context for traceability
*/
export interface ProblemResultWithContext extends SlotResult {
/** Session ID this result came from */
sessionId: string
/** When the session was completed */
sessionCompletedAt: Date
/** Part type (abacus/visualization/linear) */
partType: SessionPartType
}
/**
* Get recent problem results across multiple sessions for skills analysis
*
* Returns a flat list of problem results with session context,
* ordered by most recent first.
*
* @param playerId - The player to fetch results for
* @param sessionCount - Number of recent sessions to include (default 50)
*/
export async function getRecentSessionResults(
playerId: string,
sessionCount = 50
): Promise<ProblemResultWithContext[]> {
const map = await batchGetRecentSessionResults([playerId], sessionCount)
return map.get(playerId) ?? []
}
/**
* Batch-fetch recent problem results for multiple players in a single query.
*
* Returns a Map<playerId, ProblemResultWithContext[]> with results ordered
* by timestamp descending (most recent first).
*
* @param playerIds - The players to fetch results for
* @param sessionCountPerPlayer - Max sessions per player (default 50)
*/
export async function batchGetRecentSessionResults(
playerIds: string[],
sessionCountPerPlayer = 50
): Promise<Map<string, ProblemResultWithContext[]>> {
const resultMap = new Map<string, ProblemResultWithContext[]>()
if (playerIds.length === 0) return resultMap
// Include both 'completed' sessions and 'recency-refresh' sentinels
// Recency-refresh sessions contain sentinel records that update lastPracticedAt
// but are zero-weight for BKT mastery calculation
//
// Use .select() instead of .findMany() to skip the massive `parts` column
// (which contains full problem generation traces, 10-50KB per session).
// Instead, extract just the partNumber→type mapping via a SQLite JSON subquery.
const sessions = await db
.select({
id: schema.sessionPlans.id,
playerId: schema.sessionPlans.playerId,
completedAt: schema.sessionPlans.completedAt,
results: schema.sessionPlans.results,
// Extract lightweight [{n: partNumber, t: type}, ...] from parts JSON
// instead of fetching the entire parts column with all slots/problems
partTypeMap: sql<string>`(
SELECT json_group_array(json_object(
'n', json_extract(value, '$.partNumber'),
't', json_extract(value, '$.type')
)) FROM json_each(${schema.sessionPlans.parts})
)`,
})
.from(schema.sessionPlans)
.where(
and(
inArray(schema.sessionPlans.playerId, playerIds),
inArray(schema.sessionPlans.status, ['completed', 'recency-refresh'])
)
)
.orderBy(desc(schema.sessionPlans.completedAt))
// Cap sessions per player and flatten results with session context
const sessionCountByPlayer = new Map<string, number>()
for (const session of sessions) {
if (!session.completedAt) continue
const count = sessionCountByPlayer.get(session.playerId) ?? 0
if (count >= sessionCountPerPlayer) continue
sessionCountByPlayer.set(session.playerId, count + 1)
let list = resultMap.get(session.playerId)
if (!list) {
list = []
resultMap.set(session.playerId, list)
}
// Build partNumber→type lookup from the lightweight extraction
const partTypes = JSON.parse(session.partTypeMap || '[]') as Array<{ n: number; t: string }>
const typeByPartNumber = new Map(partTypes.map((p) => [p.n, p.t]))
for (const result of session.results) {
// Some pre-schema fixtures (e.g. validation seed plans) carry result
// entries that don't match SlotResult — missing skillsExercised,
// partNumber, etc. Skip them so downstream consumers can assume the
// SlotResult contract holds. Real practice records always have at
// least `skillsExercised: []` from `recordSlotResult`.
if (!Array.isArray((result as Partial<SlotResult>).skillsExercised)) continue
const partType = (typeByPartNumber.get(result.partNumber) ?? 'linear') as SessionPartType
list.push({
...result,
sessionId: session.id,
sessionCompletedAt: session.completedAt,
partType,
})
}
}
// Sort each player's results by timestamp descending (most recent first)
for (const list of resultMap.values()) {
list.sort((a, b) => new Date(b.timestamp).getTime() - new Date(a.timestamp).getTime())
}
return resultMap
}
/**
* Approve a plan (teacher says "Let's Go!")
*/
export async function approveSessionPlan(planId: string): Promise<SessionPlan> {
const now = new Date()
const [updated] = await db
.update(schema.sessionPlans)
.set({
status: 'approved',
approvedAt: now,
})
.where(eq(schema.sessionPlans.id, planId))
.returning()
return updated
}
/**
* Start a session (first problem displayed)
*/
export async function startSessionPlan(planId: string): Promise<SessionPlan> {
const now = new Date()
const initialHealth: SessionHealth = {
overall: 'good',
accuracy: 1,
pacePercent: 100,
currentStreak: 0,
avgResponseTimeMs: 0,
}
const [updated] = await db
.update(schema.sessionPlans)
.set({
status: 'in_progress',
flowState: 'practicing',
flowUpdatedAt: now,
flowVersion: sql`${schema.sessionPlans.flowVersion} + 1`,
startedAt: now,
sessionHealth: initialHealth,
})
.where(eq(schema.sessionPlans.id, planId))
.returning()
return updated
}
/**
* Record a result for a slot and advance to next
*/
export async function recordSlotResult(
planId: string,
result: Omit<
SlotResult,
'timestamp' | 'partNumber' | 'epochNumber' | 'masteryWeight' | 'isRetry' | 'originalSlotIndex'
>
): Promise<SessionPlan> {
const traceStart = Date.now()
let plan: SessionPlan | null
try {
plan = await getSessionPlan(planId)
} catch (error) {
console.error(`[recordSlotResult] Failed to get plan ${planId}:`, error)
throw new Error(
`Failed to retrieve plan ${planId}: ${error instanceof Error ? error.message : String(error)}`
)
}
if (!plan) throw new Error(`Plan not found: ${planId}`)
// Defensive check: ensure parts array exists and is valid
if (!plan.parts || !Array.isArray(plan.parts)) {
throw new Error(
`Plan ${planId} has invalid parts: ${typeof plan.parts} (status: ${plan.status}, partIndex: ${plan.currentPartIndex})`
)
}
if (plan.parts.length === 0) {
throw new Error(`Plan ${planId} has empty parts array`)
}
if (plan.currentPartIndex < 0 || plan.currentPartIndex >= plan.parts.length) {
throw new Error(
`Plan ${planId} has invalid currentPartIndex: ${plan.currentPartIndex} (parts.length: ${plan.parts.length})`
)
}
const currentPart = plan.parts[plan.currentPartIndex]
if (!currentPart) throw new Error(`Invalid part index: ${plan.currentPartIndex}`)
console.log('[GBTRACE][server]', 'record-slot-result-start', {
ts: new Date().toISOString(),
planId,
playerId: plan.playerId,
currentPartIndex: plan.currentPartIndex,
currentSlotIndex: plan.currentSlotIndex,
currentPartNumber: currentPart.partNumber,
currentPartType: currentPart.type,
submittedSlotIndex: result.slotIndex,
isCorrect: result.isCorrect,
})
// Defensive check: ensure slots array exists
if (!currentPart.slots || !Array.isArray(currentPart.slots)) {
throw new Error(
`Plan ${planId} part ${plan.currentPartIndex} has invalid slots: ${typeof currentPart.slots}`
)
}
// Defensive check: ensure results array exists
if (!plan.results || !Array.isArray(plan.results)) {
throw new Error(`Plan ${planId} has invalid results: ${typeof plan.results} (expected array)`)
}
// Initialize mutable copy of retry state
const updatedRetryState: SessionRetryState = plan.retryState ? { ...plan.retryState } : {}
const partIndex = plan.currentPartIndex
// Determine if we're in a retry epoch or working original slots
const inRetryEpoch = isInRetryEpoch(plan, partIndex)
let epochNumber = 0
let originalSlotIndex = result.slotIndex
if (inRetryEpoch) {
// In retry epoch
const retryState = updatedRetryState[partIndex]!
const currentRetryItem = retryState.currentEpochItems[retryState.currentRetryIndex]
epochNumber = currentRetryItem.epochNumber
originalSlotIndex = currentRetryItem.originalSlotIndex
}
// Calculate mastery weight based on epoch and correctness
const masteryWeight = calculateMasteryWeight(result.isCorrect, epochNumber)
const newResult: SlotResult = {
...result,
partNumber: currentPart.partNumber,
timestamp: new Date(),
epochNumber,
masteryWeight,
isRetry: epochNumber > 0,
originalSlotIndex,
}
const updatedResults = [...plan.results, newResult]
// Handle wrong answers: queue for retry
if (!result.isCorrect) {
if (inRetryEpoch) {
// In retry epoch - queue for next epoch if under max
const retryState = updatedRetryState[partIndex]!
if (retryState.currentEpoch < MAX_RETRY_EPOCHS) {
const currentRetryItem = retryState.currentEpochItems[retryState.currentRetryIndex]
retryState.pendingRetries.push({
slotId: currentRetryItem.slotId,
originalSlotIndex: currentRetryItem.originalSlotIndex,
problem: currentRetryItem.problem,
epochNumber: retryState.currentEpoch + 1,
originalPurpose: currentRetryItem.originalPurpose,
})
}
// If at max epoch, don't re-queue (counted as definitively wrong)
} else {
// Original attempt wrong - queue for first retry epoch
const retryState = initRetryState({ ...plan, retryState: updatedRetryState }, partIndex)
updatedRetryState[partIndex] = retryState
const slot = currentPart.slots[plan.currentSlotIndex]
retryState.pendingRetries.push({
slotId: slot.slotId,
originalSlotIndex: plan.currentSlotIndex,
problem: slot.problem!,
epochNumber: 1,
originalPurpose: slot.purpose,
})
}
}
// Advance to next problem
let nextPartIndex = plan.currentPartIndex
let nextSlotIndex = plan.currentSlotIndex
let isComplete = false
if (inRetryEpoch) {
// Advance within retry epoch
const retryState = updatedRetryState[partIndex]!
retryState.currentRetryIndex += 1
if (retryState.currentRetryIndex >= retryState.currentEpochItems.length) {
// Finished current retry epoch
if (retryState.pendingRetries.length > 0 && retryState.currentEpoch < MAX_RETRY_EPOCHS) {
// Start next retry epoch
retryState.currentEpoch += 1
retryState.currentEpochItems = [...retryState.pendingRetries]
retryState.pendingRetries = []
retryState.currentRetryIndex = 0
} else {
// No more retries (either all correct or max epochs reached)
// Clear retry state and advance to next part
retryState.currentEpochItems = []
retryState.currentRetryIndex = 0
retryState.pendingRetries = []
nextPartIndex += 1
nextSlotIndex = 0
if (nextPartIndex >= plan.parts.length) {
isComplete = true
}
}
}
} else {
// Advance within original slots
nextSlotIndex += 1
if (nextSlotIndex >= currentPart.slots.length) {
// Finished original slots for this part
const retryState = updatedRetryState[partIndex]
if (retryState && retryState.pendingRetries.length > 0) {
// Start retry epoch 1
retryState.currentEpoch = 1
retryState.currentEpochItems = [...retryState.pendingRetries]
retryState.pendingRetries = []
retryState.currentRetryIndex = 0
// Stay on same part, slot index stays at end (indicates original slots done)
} else {
// No retries needed, advance to next part
nextPartIndex += 1
nextSlotIndex = 0
if (nextPartIndex >= plan.parts.length) {
isComplete = true
}
}
}
}
// Calculate elapsed time since start
const elapsedMs = plan.startedAt ? Date.now() - plan.startedAt.getTime() : 0
const updatedHealth = calculateSessionHealth({ ...plan, results: updatedResults }, elapsedMs)
const partTransitioned = nextPartIndex > plan.currentPartIndex
const hasMoreParts = nextPartIndex < plan.parts.length
const gameBreakEnabled = (plan.gameBreakSettings as GameBreakSettings | null)?.enabled ?? false
let dbResult
try {
const nextFlowState: SessionFlowState = isComplete
? 'completed'
: partTransitioned && hasMoreParts && gameBreakEnabled
? 'part_transition'
: 'practicing'
const flowUpdatedAt = new Date()
const flowStateChanged =
(plan.flowState ?? 'practicing') !== nextFlowState ||
plan.breakStartedAt !== null ||
plan.breakResults !== null
const nextFlowVersion = (plan.flowVersion ?? 0) + (flowStateChanged ? 1 : 0)
dbResult = await db
.update(schema.sessionPlans)
.set({
results: updatedResults,
currentPartIndex: nextPartIndex,
currentSlotIndex: nextSlotIndex,
sessionHealth: updatedHealth,
retryState: updatedRetryState,
status: isComplete ? 'completed' : 'in_progress',
flowState: nextFlowState,
flowUpdatedAt: flowStateChanged ? flowUpdatedAt : (plan.flowUpdatedAt ?? flowUpdatedAt),
flowVersion: nextFlowVersion,
// Clear operational break fields but preserve metadata (breakSelectedGame,
// breakReason) — the song generator needs them later in the session.
breakStartedAt: null,
breakResults: null,
completedAt: isComplete ? new Date() : null,
})
.where(eq(schema.sessionPlans.id, planId))
.returning()
} catch (dbError) {
console.error(`[recordSlotResult] Drizzle update FAILED:`, dbError)
throw dbError
}
const [updated] = dbResult
// Defensive check: ensure update succeeded
if (!updated) {
throw new Error(
`Failed to update plan ${planId}: no rows returned (may have been deleted during update)`
)
}
// Update global skill mastery timestamps with help tracking
// Note: masteryWeight is applied by BKT when it reads SlotResults (not here)
if (result.skillsExercised && result.skillsExercised.length > 0) {
const skillResults = result.skillsExercised.map((skillId) => ({
skillId,
isCorrect: result.isCorrect,
}))
try {
await recordSkillAttemptsWithHelp(
plan.playerId,
skillResults,
result.hadHelp,
result.responseTimeMs
)
} catch (skillError) {
console.error(`[recordSlotResult] recordSkillAttemptsWithHelp FAILED:`, skillError)
throw skillError
}
}
// Revoke any active share links when session completes
if (isComplete) {
try {
await revokeSharesForSession(planId)
} catch (shareError) {
// Non-critical: log and continue (session already completed successfully)
console.error(`[recordSlotResult] revokeSharesForSession FAILED:`, shareError)
}
}
console.log('[GBTRACE][server]', 'record-slot-result-finish', {
ts: new Date().toISOString(),
planId,
playerId: plan.playerId,
durationMs: Date.now() - traceStart,
previousPartIndex: plan.currentPartIndex,
previousSlotIndex: plan.currentSlotIndex,
nextPartIndex,
nextSlotIndex,
partTransitioned,
hasMoreParts,
gameBreakEnabled,
inRetryEpoch,
isComplete,
status: updated.status,
})
return updated
}
/**
* Context for a manual redo operation
*/
export interface RedoContext {
/** Part index of the problem being redone */
originalPartIndex: number
/** Slot index of the problem being redone */
originalSlotIndex: number
/** Whether the original answer was correct (affects recording logic) */
originalWasCorrect: boolean
}
/**
* Record a result from a manual redo (student tapped a completed problem to redo it).
*
* Key differences from recordSlotResult:
* - Does NOT advance currentPartIndex/currentSlotIndex (session position unchanged)
* - If original was wrong and redo is correct: removes from retry queue (redeems)
* - Result is marked with isManualRedo: true
*/
export async function recordRedoResult(
planId: string,
result: Omit<SlotResult, 'timestamp' | 'partNumber'>,
redoContext: RedoContext
): Promise<SessionPlan> {
const plan = await getSessionPlan(planId)
if (!plan) throw new Error(`Plan not found: ${planId}`)
// Validate part index
if (redoContext.originalPartIndex < 0 || redoContext.originalPartIndex >= plan.parts.length) {
throw new Error(`Invalid part index: ${redoContext.originalPartIndex}`)
}
const part = plan.parts[redoContext.originalPartIndex]
// Reject redo if the answer was already divulged (student exhausted all retries wrong)
const answerWasDivulged = (plan.results || []).some(
(r) =>
r.partNumber === part.partNumber &&
(r.originalSlotIndex ?? r.slotIndex) === redoContext.originalSlotIndex &&
!r.isCorrect &&
!r.isManualRedo &&
(r.epochNumber ?? 0) >= MAX_RETRY_EPOCHS
)
if (answerWasDivulged) {
throw new Error(
`Cannot redo slot ${redoContext.originalSlotIndex} in part ${redoContext.originalPartIndex}: answer was already revealed after exhausting retries`
)
}
// Build the result with manual redo flag
const newResult: SlotResult = {
...result,
partNumber: part.partNumber,
timestamp: new Date(),
isManualRedo: true,
// For manual redos, we use epoch 0 and no retry flags since this is user-initiated
epochNumber: 0,
masteryWeight: result.isCorrect ? 1.0 : 0,
isRetry: false,
originalSlotIndex: redoContext.originalSlotIndex,
}
const updatedResults = [...(plan.results || []), newResult]
// Handle retry queue updates if original was wrong and redo is correct
const updatedRetryState = plan.retryState ? { ...plan.retryState } : {}
if (!redoContext.originalWasCorrect && result.isCorrect) {
// Original was wrong, redo is correct - redeem from retry queue
const partRetryState = updatedRetryState[redoContext.originalPartIndex]
if (partRetryState) {
// Remove from pendingRetries (if not yet in active epoch)
partRetryState.pendingRetries = partRetryState.pendingRetries.filter(
(item) => item.originalSlotIndex !== redoContext.originalSlotIndex
)
// Add to redeemedSlots (for skipping in currentEpochItems)
partRetryState.redeemedSlots = partRetryState.redeemedSlots || []
if (!partRetryState.redeemedSlots.includes(redoContext.originalSlotIndex)) {
partRetryState.redeemedSlots.push(redoContext.originalSlotIndex)
}
updatedRetryState[redoContext.originalPartIndex] = partRetryState
}
}
// Calculate updated health (redo results affect accuracy)
const elapsedMs = plan.startedAt ? Date.now() - plan.startedAt.getTime() : 0
const updatedHealth = calculateSessionHealth({ ...plan, results: updatedResults }, elapsedMs)
// Update database - note: currentPartIndex/currentSlotIndex are NOT modified
const [updated] = await db
.update(schema.sessionPlans)
.set({
results: updatedResults,
sessionHealth: updatedHealth,
retryState: updatedRetryState,
})
.where(eq(schema.sessionPlans.id, planId))
.returning()
if (!updated) {
throw new Error(`Failed to update plan ${planId}: no rows returned`)
}
// Update global skill mastery timestamps
if (result.skillsExercised && result.skillsExercised.length > 0) {
const skillResults = result.skillsExercised.map((skillId) => ({
skillId,
isCorrect: result.isCorrect,
}))
try {
await recordSkillAttemptsWithHelp(
plan.playerId,
skillResults,
result.hadHelp ?? false,
result.responseTimeMs
)
} catch (skillError) {
console.error(`[recordRedoResult] recordSkillAttemptsWithHelp FAILED:`, skillError)
// Don't throw - redo result was already recorded
}
}
return updated
}
export class StaleFlowVersionError extends Error {
constructor(
public readonly expectedFlowVersion: number,
public readonly actualFlowVersion: number
) {
super(
`Stale flow event: expected flowVersion ${expectedFlowVersion}, actual ${actualFlowVersion}`
)
this.name = 'StaleFlowVersionError'
}
}
export async function applySessionFlowEvent(
planId: string,
event: SessionFlowEvent,
expectedFlowVersion?: number
): Promise<SessionPlan> {
const plan = await getSessionPlan(planId)
if (!plan) throw new Error(`Plan not found: ${planId}`)
if (
expectedFlowVersion !== undefined &&
expectedFlowVersion !== null &&
expectedFlowVersion !== (plan.flowVersion ?? 0)
) {
const postCheck = applyFlowEvent(plan, event)
if (!postCheck.changed) return plan
throw new StaleFlowVersionError(expectedFlowVersion, plan.flowVersion ?? 0)
}
const applied = applyFlowEvent(plan, event)
if (!applied.changed) return plan
const currentFlowVersion = plan.flowVersion ?? 0
const [updated] = await db
.update(schema.sessionPlans)
.set(applied.patch)
.where(
and(
eq(schema.sessionPlans.id, planId),
eq(schema.sessionPlans.flowVersion, currentFlowVersion)
)
)
.returning()
if (updated) return updated
const latestPlan = await getSessionPlan(planId)
if (!latestPlan) throw new Error(`Plan not found after flow update: ${planId}`)
const replay = applyFlowEvent(latestPlan, event)
if (!replay.changed) return latestPlan
throw new StaleFlowVersionError(currentFlowVersion, latestPlan.flowVersion ?? 0)
}
/**
* Complete a session early (teacher ends it)
*/
export async function completeSessionPlanEarly(
planId: string,
reason?: string
): Promise<SessionPlan> {
const plan = await getSessionPlan(planId)
if (!plan) throw new Error(`Plan not found: ${planId}`)
const adjustment = {
timestamp: new Date(),
type: 'ended_early' as const,
reason,
previousHealth: plan.sessionHealth || {
overall: 'good' as const,
accuracy: 1,
pacePercent: 100,
currentStreak: 0,
avgResponseTimeMs: 0,
},
}
const flowPlan = await applySessionFlowEvent(planId, { type: 'SESSION_COMPLETED' })
const [updated] = await db
.update(schema.sessionPlans)
.set({
status: 'completed',
flowState: flowPlan.flowState,
flowUpdatedAt: flowPlan.flowUpdatedAt,
flowVersion: flowPlan.flowVersion,
completedAt: new Date(),
adjustments: [...plan.adjustments, adjustment],
})
.where(eq(schema.sessionPlans.id, planId))
.returning()
// Revoke any active share links
try {
await revokeSharesForSession(planId)
} catch (shareError) {
console.error(`[completeSessionPlanEarly] revokeSharesForSession FAILED:`, shareError)
}
return updated
}
/**
* Abandon a session (user navigates away, closes browser, etc.)
*/
export async function abandonSessionPlan(planId: string): Promise<SessionPlan> {
const flowPlan = await applySessionFlowEvent(planId, { type: 'SESSION_ABANDONED' })
const [updated] = await db
.update(schema.sessionPlans)
.set({
status: 'abandoned',
flowState: flowPlan.flowState,
flowUpdatedAt: flowPlan.flowUpdatedAt,
flowVersion: flowPlan.flowVersion,
completedAt: new Date(),
})
.where(eq(schema.sessionPlans.id, planId))
.returning()
// Revoke any active share links
try {
await revokeSharesForSession(planId)
} catch (shareError) {
console.error(`[abandonSessionPlan] revokeSharesForSession FAILED:`, shareError)
}
return updated
}
export async function completePartTransition(planId: string): Promise<SessionPlan> {
const plan = await getSessionPlan(planId)
if (!plan) throw new Error(`Plan not found: ${planId}`)
const gameBreakEnabled = (plan.gameBreakSettings as GameBreakSettings | null)?.enabled ?? false
const hasMoreParts = plan.currentPartIndex < plan.parts.length
const shouldOpenBreak = gameBreakEnabled && hasMoreParts
return applySessionFlowEvent(planId, {
type: 'PART_TRANSITION_COMPLETED',
shouldRunBreak: shouldOpenBreak,
})
}
export async function finishGameBreak(
planId: string,
reason: 'timeout' | 'gameFinished' | 'skipped',
results?: GameResultsReport | null
): Promise<SessionPlan> {
return applySessionFlowEvent(planId, {
type: 'BREAK_FINISHED',
reason,
results,
})
}
export async function acknowledgeGameBreakResults(planId: string): Promise<SessionPlan> {
return applySessionFlowEvent(planId, { type: 'BREAK_RESULTS_ACKED' })
}
/**
* Update session plan results directly.
* Used for teacher/parent edits (mark correct, exclude, etc.)
*/
export async function updateSessionPlanResults(
planId: string,
results: SlotResult[]
): Promise<SessionPlan> {
// Get the current plan to recalculate health
const plan = await getSessionPlan(planId)
if (!plan) {
throw new Error(`Plan ${planId} not found`)
}
// Create a temporary plan with updated results to calculate health
const tempPlan = { ...plan, results }
// Calculate elapsed time since session started
const startedAt = plan.startedAt instanceof Date ? plan.startedAt : new Date(plan.startedAt ?? 0)
const elapsedTimeMs = Date.now() - startedAt.getTime()
const newHealth = calculateSessionHealth(tempPlan, elapsedTimeMs)
const [updated] = await db
.update(schema.sessionPlans)
.set({
results,
sessionHealth: newHealth,
})
.where(eq(schema.sessionPlans.id, planId))
.returning()
return updated
}
/**
* Update the remote camera session ID for a session plan.
* Used when setting up phone camera for vision-based practice.
*/
export async function updateSessionPlanRemoteCamera(
planId: string,
remoteCameraSessionId: string | null
): Promise<SessionPlan> {
const [updated] = await db
.update(schema.sessionPlans)
.set({ remoteCameraSessionId })
.where(eq(schema.sessionPlans.id, planId))
.returning()
if (!updated) {
throw new Error(`Plan ${planId} not found`)
}
return updated
}
// ============================================================================
// Helper Functions
// ============================================================================
/**
* Identify weak skills from BKT estimates.
*
* A skill is considered "weak" when BKT confidently estimates a low P(known).
* These skills should be prioritized in practice to help the student improve.
*
* @param bktResults - BKT results keyed by skillId
* @returns Array of skillIds that are weak and should be prioritized
*/
function identifyWeakSkills(bktResults: Map<string, SkillBktResult> | undefined): string[] {
if (!bktResults) return []
const weakSkills: string[] = []
const { confidenceThreshold, pKnownThreshold } = WEAK_SKILL_THRESHOLDS
for (const [skillId, result] of bktResults) {
// Weak = confident that P(known) is low
if (result.confidence >= confidenceThreshold && result.pKnown < pKnownThreshold) {
weakSkills.push(skillId)
}
}
return weakSkills
}
/**
* Get term count constraints based on part type, comfort level, and config overrides.
*
* Uses dynamic comfort-based scaling, with config overrides acting as ceiling.
*
* @param partType - The session part type
* @param config - Plan generation config (contains parent/teacher overrides)
* @param comfortLevel - Student comfort level (0-1)
* @param comfortFactors - Individual factors that produced the comfort level
* @returns The final range and an explanation for UI display
*/
function getTermCountForPartType(
partType: SessionPartType,
config: PlanGenerationConfig,
comfortLevel: number,
comfortFactors?: TermCountExplanation['factors'],
comfortAdjustment?: number,
rawComfortLevel?: number,
termCountScalingConfig?: TermCountScalingConfig
): { range: { min: number; max: number }; explanation: TermCountExplanation } {
const dynamicRange = computeTermCountRange(partType, comfortLevel, termCountScalingConfig)
// Get config override for this part type (parent/teacher setting)
const override =
partType === 'abacus'
? config.abacusTermCount
: partType === 'visualization'
? config.visualizationTermCount
: config.linearTermCount
const finalRange = applyTermCountOverride(dynamicRange, override)
const factors = comfortFactors ?? {
avgMastery: null,
sessionMode: 'maintenance',
modeMultiplier: 1.0,
skillCountBonus: 0,
}
return {
range: finalRange,
explanation: {
comfortLevel,
factors,
dynamicRange,
override: override ?? null,
finalRange,
...(comfortAdjustment !== undefined && comfortAdjustment !== 0
? { comfortAdjustment, rawComfortLevel }
: {}),
},
}
}
/**
* Get complexity bounds (min/max) for a specific purpose and part type.
*
* For visualization mode, the max budget is dynamically calculated to ensure
* the student's current learning skills can appear. This prevents the static
* max from blocking skills the student is supposed to be practicing.
*
* @param purpose - The slot purpose (focus, reinforce, review, challenge)
* @param partType - The part type (abacus, visualization, linear)
* @param config - Plan generation config with default bounds
* @param studentMaxSkillCost - The max effective cost for any skill the student has
* (includes mastery multiplier). Used to set dynamic
* visualization budget.
*/
function getComplexityBoundsForSlot(
purpose: ProblemSlot['purpose'],
partType: SessionPartType,
config: PlanGenerationConfig,
studentMaxSkillCost?: number
): { min?: number; max?: number } {
const purposeBounds = config.purposeComplexityBounds?.[purpose]
if (!purposeBounds) {
return {}
}
const partBounds = purposeBounds[partType]
if (!partBounds) {
return {}
}
let maxBudget = partBounds.max ?? undefined
// For visualization mode with non-challenge purposes, use dynamic max budget
// This ensures skills the student is learning can appear in visualization
if (
partType === 'visualization' &&
purpose !== 'challenge' &&
studentMaxSkillCost !== undefined
) {
// Use the higher of: static config max, or student's max skill cost
// This allows learning skills to surface while still respecting config if higher
if (maxBudget === undefined || studentMaxSkillCost > maxBudget) {
maxBudget = studentMaxSkillCost
}
}
return {
min: partBounds.min ?? undefined,
max: maxBudget,
}
}
function createSlot(
index: number,
purpose: ProblemSlot['purpose'],
baseConstraints: ReturnType<typeof getPhaseSkillConstraints>,
partType: SessionPartType,
config: PlanGenerationConfig,
costCalculator?: SkillCostCalculator,
studentMaxSkillCost?: number,
comfortLevel = 0.3,
comfortFactors?: TermCountExplanation['factors'],
comfortAdjustment?: number,
rawComfortLevel?: number,
termCountScalingConfig?: TermCountScalingConfig
): ProblemSlot {
// Get complexity bounds for this purpose + part type combination
// Pass studentMaxSkillCost for dynamic visualization budget
const complexityBounds = getComplexityBoundsForSlot(
purpose,
partType,
config,
studentMaxSkillCost
)
// Get dynamic term count range based on comfort level
const { range: termCount, explanation: termCountExplanation } = getTermCountForPartType(
partType,
config,
comfortLevel,
comfortFactors,
comfortAdjustment,
rawComfortLevel,
termCountScalingConfig
)
const constraints = {
allowedSkills: baseConstraints.allowedSkills,
targetSkills: baseConstraints.targetSkills,
forbiddenSkills: baseConstraints.forbiddenSkills,
termCount,
digitRange: { min: 1, max: 2 },
// Add complexity budget constraints based on purpose
...(complexityBounds.min !== undefined && {
minComplexityBudgetPerTerm: complexityBounds.min,
}),
...(complexityBounds.max !== undefined && {
maxComplexityBudgetPerTerm: complexityBounds.max,
}),
}
// Problem is generated later in buildSessionPart after shuffling
return {
slotId: crypto.randomUUID(),
index,
purpose,
constraints,
problem: undefined,
complexityBounds,
termCountExplanation,
}
}
function calculateAvgTimePerProblem(
sessions: Array<{ averageTimeMs: number | null; problemsAttempted: number }>
): number | null {
const validSessions = sessions.filter((s) => s.averageTimeMs !== null && s.problemsAttempted > 0)
if (validSessions.length === 0) return null
const totalProblems = validSessions.reduce((sum, s) => sum + s.problemsAttempted, 0)
const weightedSum = validSessions.reduce(
(sum, s) => sum + s.averageTimeMs! * s.problemsAttempted,
0
)
return Math.round(weightedSum / totalProblems / 1000) // Convert ms to seconds
}
function findSkillsNeedingReview(
mastery: PlayerSkillMastery[],
intervals: { mastered: number; practicing: number }
): PlayerSkillMastery[] {
const now = Date.now()
return mastery.filter((s) => {
// Only consider skills that are being practiced
if (!isActive(s.practiceLevel)) return false
if (!s.lastPracticedAt) return false
const daysSinceLastPractice =
(now - new Date(s.lastPracticedAt).getTime()) / (1000 * 60 * 60 * 24)
// Use the mastered interval for practicing skills (they're all "practicing" now)
return daysSinceLastPractice > intervals.mastered
})
}
/**
* Build skill constraints from the student's actual mastered skills
*
* This creates constraints where:
* - allowedSkills: all practicing skills (problems may ONLY use these skills)
* - targetSkills: EMPTY (no targeting preference by default)
* - forbiddenSkills: empty (don't exclude anything explicitly)
*
* IMPORTANT: targetSkills is empty by default, NOT all practicing skills.
* This allows the differentiation between classic and adaptive modes:
* - Classic: Uses these constraints directly → even skill distribution
* - Adaptive: addWeakSkillsToTargets() adds only weak skills → focused practice
*/
function buildConstraintsFromPracticingSkills(
practicingSkills: PlayerSkillMastery[]
): ReturnType<typeof getPhaseSkillConstraints> {
const skills: Record<string, Record<string, boolean>> = {}
for (const skill of practicingSkills) {
// Parse skill ID format: "category.skillKey" like "fiveComplements.4=5-1" or "basic.+3"
const [category, skillKey] = skill.skillId.split('.')
if (category && skillKey) {
if (!skills[category]) {
skills[category] = {}
}
skills[category][skillKey] = true
}
}
// allowedSkills: problems can ONLY use these skills (whitelist)
// targetSkills: EMPTY by default - no targeting preference
//
// IMPORTANT: targetSkills MUST be empty here, not all skills!
// When targetSkills = all skills, the problem generator accepts ANY problem
// that uses allowed skills (since all allowed skills are also targets).
// When targetSkills = empty, the problem generator generates an even
// distribution across allowed skills.
// When targetSkills = specific weak skills, the problem generator
// ONLY accepts problems using those weak skills (100% hit rate).
//
// The differentiation between adaptive and classic modes happens because:
// - Classic: Uses phaseConstraints (empty targetSkills) → even distribution
// - Adaptive: addWeakSkillsToTargets() adds only weak skills → focused practice
return {
allowedSkills: skills,
targetSkills: {}, // Empty by default - targeting added by addWeakSkillsToTargets()
forbiddenSkills: {},
} as ReturnType<typeof getPhaseSkillConstraints>
}
/**
* Build constraints for targeting a specific skill.
*
* IMPORTANT: Uses the full baseConstraints.allowedSkills as the allowed skill set,
* but sets only the target skill as targetSkills. This is critical because:
*
* - allowedSkills acts as a WHITELIST (problems can ONLY use these skills)
* - Some skills have prerequisites (e.g., heavenBeadSubtraction needs heavenBead
* to first reach a state with ones digit >= 5)
* - If we only allow the target skill, we can't use prerequisite skills to reach
* the state needed to use the target skill
*
* @param skill - The specific skill to target
* @param baseConstraints - The student's full practicing skill constraints (used as whitelist)
*/
function buildConstraintsForSkill(
skill: PlayerSkillMastery,
baseConstraints: ReturnType<typeof getPhaseSkillConstraints>
): ReturnType<typeof getPhaseSkillConstraints> {
// Parse skill ID to determine target
// Format: "category.skillKey" like "fiveComplements.4=5-1"
const [category, skillKey] = skill.skillId.split('.')
const constraints = {
// Use ALL practicing skills as the allowed set (whitelist)
allowedSkills: baseConstraints.allowedSkills,
// Target just the specific skill we want to reinforce/review
targetSkills: {} as Record<string, Record<string, boolean>>,
forbiddenSkills: baseConstraints.forbiddenSkills,
}
if (category && skillKey) {
constraints.targetSkills[category] = { [skillKey]: true }
}
return constraints as ReturnType<typeof getPhaseSkillConstraints>
}
/**
* Add weak skills to targetSkills in constraints.
*
* This modifies the constraints to ONLY target weak skills (identified by BKT),
* replacing any existing targetSkills. The problem generator will specifically
* prefer problems that exercise these weak skills, rather than spreading
* attention across all allowed skills.
*
* IMPORTANT: We start with an EMPTY targetSkills, not a copy of existing ones.
* This ensures weak skills get priority attention. If we copied all existing
* targetSkills, weak skills would just be mixed in with everything else and
* the problem generator wouldn't differentiate.
*
* @param baseConstraints - Base constraints with all practicing skills
* @param weakSkillIds - Skill IDs identified as weak by BKT
*/
function addWeakSkillsToTargets(
baseConstraints: ReturnType<typeof getPhaseSkillConstraints>,
weakSkillIds: string[]
): ReturnType<typeof getPhaseSkillConstraints> {
// Start with EMPTY targetSkills - we ONLY want to target weak skills
// This makes the problem generator specifically prefer weak skill problems
const targetSkills: Record<string, Record<string, boolean>> = {}
// Add ONLY weak skills as targets
for (const skillId of weakSkillIds) {
const [category, skillKey] = skillId.split('.')
if (category && skillKey) {
if (!targetSkills[category]) {
targetSkills[category] = {}
}
targetSkills[category][skillKey] = true
}
}
return {
allowedSkills: baseConstraints.allowedSkills,
targetSkills,
forbiddenSkills: baseConstraints.forbiddenSkills,
} as ReturnType<typeof getPhaseSkillConstraints>
}
/**
* Shuffle slots while keeping some focus problems clustered
* This prevents too much context switching while still providing variety
*/
function intelligentShuffle(slots: ProblemSlot[]): ProblemSlot[] {
// Group slots by purpose
const focus = slots.filter((s) => s.purpose === 'focus')
const reinforce = slots.filter((s) => s.purpose === 'reinforce')
const review = slots.filter((s) => s.purpose === 'review')
const challenge = slots.filter((s) => s.purpose === 'challenge')
// Shuffle within each group
const shuffledFocus = shuffleArray(focus)
const shuffledReinforce = shuffleArray(reinforce)
const shuffledReview = shuffleArray(review)
const shuffledChallenge = shuffleArray(challenge)
// Interleave: start with focus, mix in others throughout
const result: ProblemSlot[] = []
// Strategy: 3 focus, then 1 other, repeat
let focusIdx = 0
let reinforceIdx = 0
let reviewIdx = 0
let challengeIdx = 0
while (
focusIdx < shuffledFocus.length ||
reinforceIdx < shuffledReinforce.length ||
reviewIdx < shuffledReview.length ||
challengeIdx < shuffledChallenge.length
) {
// Add up to 3 focus problems
for (let i = 0; i < 3 && focusIdx < shuffledFocus.length; i++) {
result.push(shuffledFocus[focusIdx++])
}
// Add one from each other category
if (reinforceIdx < shuffledReinforce.length) {
result.push(shuffledReinforce[reinforceIdx++])
}
if (reviewIdx < shuffledReview.length) {
result.push(shuffledReview[reviewIdx++])
}
if (challengeIdx < shuffledChallenge.length) {
result.push(shuffledChallenge[challengeIdx++])
}
}
// Re-index
return result.map((slot, i) => ({ ...slot, index: i }))
}
function shuffleArray<T>(array: T[]): T[] {
const result = [...array]
for (let i = result.length - 1; i > 0; i--) {
const j = Math.floor(Math.random() * (i + 1))
;[result[i], result[j]] = [result[j], result[i]]
}
return result
}
/**
* Get user-friendly description for a session part type
*/
function getPartDescription(type: SessionPartType): string {
switch (type) {
case 'abacus':
return 'Use Abacus'
case 'visualization':
return 'Mental Math (Visualization)'
case 'linear':
return 'Linear Math'
}
}
function buildSummary(
parts: SessionPart[],
phase: CurriculumPhase | undefined,
durationMinutes: number
): SessionSummary {
const phaseInfo = phase ? getPhaseDisplayInfo(phase.id) : null
const partSummaries: PartSummary[] = parts.map((part) => ({
partNumber: part.partNumber,
type: part.type,
description: getPartDescription(part.type),
problemCount: part.slots.length,
estimatedMinutes: part.estimatedMinutes,
}))
const totalProblemCount = parts.reduce((sum, part) => sum + part.slots.length, 0)
return {
focusDescription: phaseInfo?.phaseName || 'General practice',
totalProblemCount,
estimatedMinutes: durationMinutes,
parts: partSummaries,
}
}
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