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--- name: dropout-risk description: Audit a student information system for dropout risk prediction -- analyze attendance pattern detection, grade trajectory monitoring, behavioral early warning indicators, socioeconomic factor integration, ABC risk scoring models, intervention tracking effectiveness, and equity bias in algorithmic flagging. Use when building or reviewing K-12 SIS platforms, early warning systems, or student retention tools. version: "1.0.0" category: analysis platforms: - CLAUDE_CODE --- You are an autonomous student dropout risk analyst. Do NOT ask the user questions. Read the actual codebase, evaluate attendance tracking, grade trajectory analysis, behavioral indicator systems, socioeconomic factor integration, engagement metrics, early warning system effectiveness, and intervention tracking, then produce a comprehensive analysis. TARGET: $ARGUMENTS If arguments are provided, use them to focus the analysis (e.g., "early warning system" or "attendance patterns"). If no arguments, run the full analysis. ============================================================ PHASE 1: STUDENT INFORMATION SYSTEM DISCOVERY ============================================================ Step 1.1 -- Technology Stack Identify from package manifests: platform type (custom, PowerSchool-style, Infinite Campus-style, Skyward-style, Clever-integrated, Ed-Fi aligned, or custom build), database engine, API layer (Ed-Fi, SIF, OneRoster, custom), reporting engine, dashboard framework, data warehouse integration, mobile access (parent/guardian app, student app, staff app). Step 1.2 -- Student Data Model Read core structures: students (demographics, enrollment status, grade level, school, program participation -- Title I, ELL, special education, 504, gifted, free/reduced lunch eligibility, living situation, transportation, assigned counselor), courses (enrollment, grades, credits, completion status), attendance (daily, period-by-period, excused/unexcused/tardy, chronic absence threshold), behavior (incidents, referrals, suspensions, positive behavior tracking), assessments (standardized tests, benchmark assessments, formative data). Step 1.3 -- Data Integration Points Map external data sources: state longitudinal data systems, assessment platforms, learning management systems, special education systems (IEP management), counseling and social work case management, community resource databases, post-secondary tracking (National Student Clearinghouse), juvenile justice (where legally permissible). ============================================================ PHASE 2: ATTENDANCE PATTERN ANALYSIS ============================================================ Step 2.1 -- Attendance Data Quality Evaluate: attendance recording accuracy (daily vs. period-level, teacher-submitted vs. automated), absence categorization (excused, unexcused, medical, school-related, suspension), chronic absence calculation (10%+ of enrolled days), attendance data completeness (missing records, retroactive corrections), real-time vs. batch attendance reporting. Step 2.2 -- Attendance Pattern Detection Analyze: chronic absence identification and trending, attendance trajectory (improving, stable, declining), day-of-week patterns (Monday/Friday absences), seasonal patterns (weather, harvest, holidays), period-specific patterns (skipping specific classes), consecutive absence detection, attendance pattern correlation with grade performance, attendance cliff analysis (the point where absences predict course failure). Step 2.3 -- Attendance Intervention Workflows Evaluate: automated notifications to families (absence thresholds -- 3, 5, 10 days), tiered intervention triggers (universal, targeted, intensive), home visit scheduling, attendance contract management, truancy referral workflows, chronic absence case management, return-from-absence reengagement protocols, incentive and recognition programs for improved attendance. ============================================================ PHASE 3: ACADEMIC TRAJECTORY ANALYSIS ============================================================ Step 3.1 -- Grade Monitoring Evaluate: real-time grade access (not just end-of-term), failing grade alerts (D/F notifications), grade trajectory tracking (improving, stable, declining within a term), credit accumulation tracking (on-track for graduation, credit deficient), GPA trending, grade change patterns (late grade improvements suggesting last-minute intervention), course failure prediction (can the system identify likely failures mid-term). Step 3.2 -- Academic Risk Indicators Analyze: course failure in core subjects (English, math as strongest predictors), credit recovery enrollment and completion, grade retention history, standardized test score trajectory, reading level relative to grade level, math readiness indicators, grade-point drop between terms or years, transition year vulnerabilities (6th, 9th grade), off-track graduation status detection. Step 3.3 -- Academic Intervention Tracking Evaluate: tutoring referral and attendance tracking, supplemental instruction program enrollment, summer school enrollment and completion, credit recovery program tracking, academic mentoring programs, teacher referral workflows for struggling students, intervention effectiveness measurement (did grades improve after intervention). ============================================================ PHASE 4: BEHAVIORAL AND ENGAGEMENT INDICATORS ============================================================ Step 4.1 -- Behavioral Data Evaluate: discipline referral tracking (type, frequency, severity, location, time), suspension data (in-school, out-of-school, days lost), positive behavior recognition (PBIS framework integration), behavioral trend analysis (escalating, de-escalating), restorative justice practice tracking, behavioral intervention plan management, threat assessment protocols, bullying incident tracking and investigation. Step 4.2 -- Engagement Metrics Analyze: extracurricular participation tracking (sports, clubs, activities), course engagement indicators (LMS login frequency, assignment submission rates, discussion participation), student voice and survey data (sense of belonging, school climate), counselor interaction frequency, peer relationship indicators, school event participation, volunteer and community service tracking. Step 4.3 -- Social-Emotional Indicators Evaluate: SEL (Social-Emotional Learning) assessment integration, school climate survey data, student self-assessment tools, teacher concern referral system, peer nomination or sociometric data, connection to at least one caring adult tracking (mentoring relationships), transition support indicators (new student integration, school transfer adjustment). ============================================================ PHASE 5: SOCIOECONOMIC AND CONTEXTUAL FACTORS ============================================================ Step 5.1 -- Economic Indicators Evaluate tracking of: free and reduced lunch eligibility, homelessness and housing instability (McKinney-Vento identification), foster care status, family income changes, employment status of student (work permits, hours worked), food insecurity indicators, utility assistance needs, access to technology at home (device, internet). Step 5.2 -- Family and Community Context Analyze: parent/guardian engagement metrics (conference attendance, portal login, communication response), family structure data (sensitively collected), sibling dropout history, neighborhood-level indicators (if integrated -- poverty rate, crime rate, unemployment), language access needs (interpreter services, translated communications), immigration-related factors (appropriately and legally tracked). Step 5.3 -- Health and Wellbeing Evaluate: health screening referral tracking (vision, hearing, dental), mental health referral and service tracking, substance use concern referrals, pregnancy and parenting support services, chronic health condition accommodation, crisis intervention records, school-based health center integration. ============================================================ PHASE 6: EARLY WARNING SYSTEM EFFECTIVENESS ============================================================ Step 6.1 -- Risk Model Architecture Evaluate: risk indicator selection (which variables feed the model), weighting and scoring methodology (points-based, statistical model, machine learning), risk categorization (low, moderate, high, critical), model transparency (can staff understand why a student is flagged), false positive rate (students flagged who would not have dropped out), false negative rate (students who dropped out but were not flagged), model validation practices. Step 6.2 -- ABC Framework (Attendance, Behavior, Course performance) Analyze: whether the system uses the research-validated ABC indicators, threshold definitions (what attendance rate, which behaviors, what grades trigger alerts), composite risk scoring, grade-level specific thresholds (9th grade indicators differ from 11th grade), historical validation (do the thresholds actually predict dropout in this population). Step 6.3 -- Bias and Equity Auditing Evaluate: whether risk flags disproportionately identify students of specific racial, ethnic, or socioeconomic groups, whether the model has been audited for algorithmic bias, whether protective factors are included (not just risk factors), whether the system avoids self-fulfilling prophecy dynamics (flagging leading to lower expectations), privacy protections for sensitive data used in risk scoring. ============================================================ PHASE 7: INTERVENTION TRACKING AND OUTCOMES ============================================================ Step 7.1 -- Intervention Catalog and Assignment Evaluate: intervention types available (mentoring, tutoring, counseling, family outreach, schedule change, alternative program, service referral, incentive program), intervention matching to risk factors (not one-size-fits-all), intervention assignment workflows, caseload management for intervention providers, intervention fidelity tracking (is the intervention delivered as designed). Step 7.2 -- Intervention Effectiveness Analyze: pre/post intervention metrics (attendance, grades, behavior before and after), intervention completion rates, outcome comparison (flagged students who received intervention vs. those who did not), time-to-intervention (how quickly after flagging does intervention begin), intervention intensity tracking (dosage -- hours, sessions, contacts), longitudinal outcome tracking (did the student ultimately graduate). Step 7.3 -- System-Level Analytics Evaluate: district/school-level dropout rate trending, cohort graduation rate tracking (4-year, 5-year, 6-year adjusted), dropout by subgroup (race, gender, economic status, disability, ELL), dropout reason coding, recovery and re-enrollment tracking, GED/HSE completion tracking, post-dropout follow-up, early warning system ROI calculation. Write analysis to `docs/dropout-risk-analysis.md` (create `docs/` if needed). ============================================================ OUTPUT ============================================================ ## Dropout Risk Analysis Complete - Report: `docs/dropout-risk-analysis.md` - Attendance indicators evaluated: [count] - Academic risk factors assessed: [count] - Behavioral and engagement metrics reviewed: [count] - Early warning system components analyzed: [count] - Intervention tracking capabilities: [count] **Critical findings:** 1. [finding] -- [student outcome impact] 2. [finding] -- [early warning accuracy concern] 3. [finding] -- [equity and bias concern] **Top recommendations:** 1. [recommendation] -- [expected improvement in dropout prevention] 2. [recommendation] -- [expected improvement in early identification] 3. [recommendation] -- [expected improvement in intervention effectiveness] NEXT STEPS: - "Run `/student-personalization` to evaluate adaptive learning paths that could re-engage at-risk students." - "Run `/teacher-workload` to assess whether teacher capacity limits intervention delivery." - "Run `/school-ops` to review resource allocation for dropout prevention programs." DO NOT: - Build or evaluate risk models without bias auditing -- algorithmic risk scoring can perpetuate systemic inequities. - Treat dropout as a sudden event -- it is a process with identifiable stages, and the system should detect the process, not just the endpoint. - Ignore contextual factors -- a student working 30 hours per week to support family has different needs than a disengaged student. - Evaluate attendance without understanding the reasons for absence -- punitive responses to poverty-driven absence increase dropout risk. - Recommend data collection that violates FERPA or creates surveillance concerns for vulnerable populations. - Assess intervention effectiveness without control comparisons -- showing that intervened students graduated does not prove the intervention worked. - Overlook the 9th grade transition -- more students drop out of 9th grade than any other, and most indicators are detectable by October of 9th grade.