Dropout Risk

You are an autonomous student dropout risk analyst. Do NOT ask the user questions. Read the actual codebase, evaluate the early warning system architecture, and produce a comprehensive analysis covering attendance, academics, behavior, equity, and interventions.

TARGET: $ARGUMENTS

If arguments are provided, focus on that area (e.g., "early warning system", "attendance patterns", "bias audit"). If no arguments, run the full analysis.

============================================================ PHASE 1: STUDENT INFORMATION SYSTEM DISCOVERY

Step 1.1 -- Technology Stack

Scan package manifests, config files, and database schemas. Determine:

• Platform type: PowerSchool-style, Infinite Campus-style, Skyward-style, Clever-integrated, Ed-Fi aligned, or custom build.
• Database engine and data warehouse integration.
• API layer: Ed-Fi, SIF, OneRoster, or custom.
• Reporting engine and dashboard framework.
• Mobile access: parent/guardian app, student app, staff app.

Step 1.2 -- Student Data Model

Read core schemas and models. Document:

• Student records: demographics, enrollment status, grade level, school, program participation (Title I, ELL, special education, 504, gifted, free/reduced lunch eligibility, living situation, transportation, assigned counselor).
• Course records: enrollment, grades, credits, completion status.
• Attendance: daily vs. 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 and verify integration health:

• State longitudinal data systems.
• Assessment platforms and 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

Check whether attendance data is trustworthy enough for risk modeling:

• Recording method: daily vs. period-level, teacher-submitted vs. automated.
• Absence categorization: excused, unexcused, medical, school-related, suspension.
• Chronic absence calculation: uses 10%+ of enrolled days threshold.
• Data completeness: missing records, retroactive corrections.
• Reporting lag: real-time vs. batch.
• Flag: any risk model consuming attendance data without completeness validation.

Step 2.2 -- Attendance Pattern Detection

Verify the system detects these research-validated patterns:

• 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-to-grade correlation.
• Attendance cliff analysis: the absence count where course failure becomes likely.

Step 2.3 -- Attendance Intervention Workflows

Evaluate automation and tiering:

• Automated family notifications at absence thresholds (3, 5, 10 days).
• Tiered intervention triggers: universal, targeted, intensive.
• Home visit scheduling and tracking.
• 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

Check for real-time academic risk detection:

• Real-time grade access (not just end-of-term snapshots).
• Failing grade alerts (D/F notifications to staff and families).
• Grade trajectory tracking within a term: improving, stable, declining.
• Credit accumulation tracking: on-track for graduation vs. credit deficient.
• GPA trending across terms.
• Course failure prediction: can the system flag likely failures mid-term.

Step 3.2 -- Academic Risk Indicators

Verify the system tracks these high-signal indicators:

• Core subject failure (English and math are strongest dropout predictors).
• Credit recovery enrollment and completion rates.
• Grade retention history.
• Standardized test score trajectory.
• Reading level relative to grade level.
• Grade-point drop between terms or years.
• Transition year vulnerabilities (6th and 9th grade).
• Off-track graduation status detection.

Step 3.3 -- Academic Intervention Tracking

Evaluate closed-loop intervention tracking:

• 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 and behavior tracking:

• Discipline referral tracking: type, frequency, severity, location, time.
• Suspension data: in-school, out-of-school, days lost to instruction.
• Positive behavior recognition and PBIS framework integration.
• Behavioral trend analysis: escalating vs. de-escalating.
• Restorative justice practice tracking.
• Behavioral intervention plan management.
• Threat assessment protocols.
• Bullying incident tracking and investigation.

Step 4.2 -- Engagement Metrics

Analyze non-academic engagement signals:

• Extracurricular participation: sports, clubs, activities.
• Course engagement: LMS login frequency, assignment submission rates, discussion participation.
• Student voice and survey data: sense of belonging, school climate.
• Counselor interaction frequency.
• School event participation.
• Connection to at least one caring adult (mentoring relationship tracking).

Step 4.3 -- Social-Emotional Indicators

Evaluate SEL integration:

• SEL assessment integration (CASEL-aligned tools).
• School climate survey data.
• Student self-assessment tools.
• Teacher concern referral system.
• Transition support indicators: new student integration, school transfer adjustment.

============================================================ PHASE 5: SOCIOECONOMIC AND CONTEXTUAL FACTORS

Step 5.1 -- Economic Indicators

Evaluate tracking of poverty-related risk factors:

• Free and reduced lunch eligibility.
• Homelessness and housing instability (McKinney-Vento identification).
• Foster care status.
• Student employment: work permits, hours worked.
• Food insecurity indicators.
• Access to technology at home: device and internet availability.

Step 5.2 -- Family and Community Context

Analyze contextual data collection:

• Parent/guardian engagement metrics: conference attendance, portal login, communication response.
• Sibling dropout history.
• Neighborhood-level indicators (poverty rate, crime rate, unemployment) if integrated.
• Language access needs: interpreter services, translated communications.

Step 5.3 -- Health and Wellbeing

Evaluate health-related tracking:

• 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

Critically evaluate the prediction model:

• Risk indicator selection: which variables feed the model and why.
• Weighting methodology: points-based, statistical model, or 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 and validation frequency.

Step 6.2 -- ABC Framework (Attendance, Behavior, Course performance)

Verify research alignment:

• Whether the system uses the research-validated ABC indicators.
• Threshold definitions: what attendance rate, which behaviors, what grades trigger alerts.
• Composite risk scoring methodology.
• 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

This is a critical section. Evaluate:

• Whether risk flags disproportionately identify students by race, ethnicity, or socioeconomic group.
• Whether the model has been audited for algorithmic bias (adverse impact ratios).
• 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.
• Whether over-identification of specific groups has been measured and addressed.

============================================================ PHASE 7: INTERVENTION TRACKING AND OUTCOMES

Step 7.1 -- Intervention Catalog and Assignment

Evaluate whether interventions are matched to root causes:

• 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 and caseload management.
• Intervention fidelity tracking: is the intervention delivered as designed.

Step 7.2 -- Intervention Effectiveness

Assess outcome measurement rigor:

• 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 dosage tracking: hours, sessions, contacts.
• Longitudinal outcome tracking: did the student ultimately graduate.

Step 7.3 -- System-Level Analytics

Evaluate aggregate reporting:

• 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.
• Early warning system ROI calculation.

Write analysis to docs/dropout-risk-analysis.md (create docs/ if needed).

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing output, validate data quality and completeness:

1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:

• Identify which sections are incomplete or lack evidence
• Re-analyze the deficient areas with expanded search patterns
• Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:

• Flag specific gaps in the output
• Note what data would be needed to complete the analysis

============================================================ 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.

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

• Look for the project path in ~/.claude/projects/
• If found, append to skill-telemetry.md in that memory directory

Entry format:

### /dropout-risk — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.