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Early Warning System

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Early Warning
System

Early Intervention Framework

Human-Centric Decision Support System for Vocational Education.

Human-Centric Decision Support System for Vocational Education.

Duration

8 - 12 Weeks
(Self-Initiated Project)

8 - 12 Weeks
(Self-Initiated Project)

My Role

Business Analyst · UX Researcher · Product Designer

Business Analyst · UX Researcher · Product Designer

Tools

Figma, Google Forms, Excel,
Notion

Figma, Google Forms, Excel, Notion

Figma, Google Forms, Excel,
Notion

Executive Summary

Vocational schools in Indonesia often face a "silent dropout" crisis where students disengage weeks before teachers notice. I engineered a rules-based engine to transform fragmented paper logs into a real-time intervention pipeline.

  • The Problem: 8-week delay in identifying at-risk students due to manual paper-based logs.

  • The Solution: An automated, rules-based Early Warning System (EWS)

  • The Impact: Detection time reduced from 8 weeks to <5 days.

Vocational schools in Indonesia often face a "silent dropout" crisis where students disengage weeks before teachers notice. I engineered a rules-based engine to transform fragmented paper logs into a real-time intervention pipeline.

  • The Problem: 8-week delay in identifying at-risk students due to manual paper-based logs.

  • The Solution: An automated, rules-based Early Warning System (EWS)

  • The Impact: Detection time reduced from 8 weeks to <5 days.

Business Context

  1. The Gap

Through interviews with 3 homeroom teachers and 3 subject teachers, I diagnosed the systemic failure:

"Subject teachers held the data, but Homeroom teachers held the responsibility."

The gap was the lack of unified logic to trigger action. Data existed, but it was scattered—no mechanism connected the dots or alerted teachers before it was too late.

  1. Current State vs Future State

  1. Stakeholder Pain Points

Primary stakeholders and their pain points:

Stakeholders

Stakeholder

Homeroom Teachers

Homeroom Teachers


Subject Teachers

Students

Parents

Pain Points

Responsible for students welfare but no real-time data to act on

Holds grade data but no mandate to act on

Students facing personal hardship without anyone noticing

Being informed too late

Impact

Students at risk are missed

Student competence are often based on perception

Students graduate without core skills or drop out entirely

No chance to intervene

Secondary stakeholders are:

  • School Principal : Final authority over promotion and disciplinary decisions based on incomplete information

  • Foundation : Institutional owner; often risk-averse and resistant to operational change.

  • Education Authority : Sets standards but receives inconsistent reporting

System Logic & Rationale

  1. Risk Scoring Algorithm

Core Risks

Core Risks

Core Risks

To move from subjective 'teacher intuition' to objective 'system signals,' I engineered a dual-path risk engine. This algorithm distinguishes between Presence Risk (Attendance) and Competency Risk (Academic) to ensure no student disengages silently.

  1. Attendance Pattern

Description

Input

Risk Logic

Rationale


Attendance Pattern

Manual paper attendance log

  • 3 consecutive absence days → Soft Warning

  • 7 consecutive absence days → Critical Alert

Attendance is a leading indicator of disengagement. By flagging consecutive absences, the system identifies the critical 72-hour window where a student is most likely to drift toward permanent dropout.

Attendance is a leading indicator of disengagement. By flagging consecutive absences, the system identifies the critical 72-hour window where a student is most likely to drift toward permanent dropout.

  1. Academic Performance

Description

Input


Risk Logic





Rationale


Academic Performance

  • Daily quizzes

  • Subject assignments

  • Final exam results

  • Consistent low scores (below minimum competency criterion/KKM) triggers a "Soft Warning"

  • A drop of >20% in main subject average scores over a 3-week window triggers a "Soft Warning"

  • Scores falling below the minimum competency criterion/KKM in more than two core vocational subjects trigger a "Critical Alert"

Competency risk isn't just about 'low grades'—it's about velocity. By tracking performance decay instead of just static averages, we catch students who are struggling in mind, allowing for early support.

Design Choice: Slow Decay Logic

To ensure a humane approach, risk scores recover slowly over 14 days when positive behavior returns. This prevents 'score volatility' and ensures students are rewarded for sustained improvement rather than just a single day of attendance.

Technical Specs

  1. System Context Diagram

System Context Diagram

Entity Relationship Diagram

Functional Blueprint

This diagram illustrates how the Early Warning System integrates with existing school records and serves as a centralized hub for teachers to monitor student health and initiate timely support.

Context Diagram

ERD

Blueprint

This diagram illustrates how the Early Warning System integrates with existing school records and serves as a centralized hub for teachers to monitor student health and initiate timely support.

System Context Diagram

ERD

Functional Blueprint

This diagram illustrates how the Early Warning System integrates with existing school records and serves as a centralized hub for teachers to monitor student health and initiate timely support.

The Ethical Foundation

  1. Design Principles

This system was explicitly designed to support—not override—human judgment. It transforms data from a tool of surveillance into a mechanism of care, specifically engineered for the unique vulnerabilities of students in low-income or rural contexts.
The "No-Direct-Bot" Rule: To maintain trust, the system never contacts parents directly. All communication is mediated by the Homeroom Teacher to ensure that automated alerts are delivered with human empathy and context.

  1. Four Pillars of Architectural Ethics

Principle

Intervention, Not Punishment

Evidence-Informed,
Not Algorithm-Driven

Evidence-Informed,
Not Algorithm-Driven

Governance-Aligned,
Not Authority-Replacing

Governance-Aligned,
Not Authority-Replacing

Care-Oriented Data Use

Technical Implementation

Technical Implement-ation

Technical Implementation

Alerts trigger support workflows, not disciplinary sanctions

Alerts trigger support workflows, not disciplinary sanctions

The system aggregates signals but never automates promotion decisions

Subject teachers retain academic authority; Principals retain final say

Data is treated as a protective tool for student rights

Human Outcome

Focus shifts from "strict discipline" to "earlier care"

Decisions remain grounded in structured evidence, not algorithms

Technology respects and reinforces existing institutional authority

Institutional judgment held without compromising student privacy

The Handoff

To move from Framework to Function, I am handing off these requirements to the design phase with three core objectives:

  • Objective 1: Reduce Cognitive Load – Transform complex risk scores into a "Calm UI" that highlights only what is urgent.

  • Objective 2: Frictionless Intervention – Integrate communication tools (WhatsApp) directly into the workflow to ensure alerts lead to action.

  • Objective 3: Adoption over Complexity – Ensure the interface is optimized for low-end mobile devices and varying levels of digital literacy.

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