refactor: redesign admin permalinks to RESTful paths
- Replace fragile /admin/ai-playground and /admin/ai-generation routes
with item-scoped /admin/questions/{id}/generate endpoints
- Add /admin/tryouts/{id}/questions route using Tryout primary key
instead of composite query params (?tryout_id=X&website_id=Y)
- Fix variant detail and review-bulk endpoints to use scoped paths
- Update all internal links (dashboard, hierarchy, exams) to new routes
- Remove obsolete ai_playground_view/submit/save functions
This commit is contained in:
Dwindi Ramadhana
612
PROJECT_UNDERSTANDING.md
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612
PROJECT_UNDERSTANDING.md
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# Project Understanding: IRT-Powered Adaptive Question Bank System
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> **Project Name:** IRT Bank Soal
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> **Version:** 1.0.0
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> **Last Updated:** 2026-06-15
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> **Repository:** https://git.backoffice.biz.id/dwindown/yellow-bank-soal
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---
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## Table of Contents
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1. [Executive Summary](#executive-summary)
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2. [Project Purpose](#project-purpose)
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3. [Tech Stack](#tech-stack)
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4. [Project Structure](#project-structure)
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5. [Core Concepts](#core-concepts)
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6. [Data Models](#data-models)
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7. [API Endpoints](#api-endpoints)
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8. [Key Services](#key-services)
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9. [Scoring Formulas](#scoring-formulas)
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10. [Configuration](#configuration)
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11. [Workflows](#workflows)
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12. [Deployment](#deployment)
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---
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## Executive Summary
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This is a **FastAPI-based backend system** for managing adaptive assessment/tryout exams with sophisticated scoring capabilities. The system supports both **Classical Test Theory (CTT)** and **Item Response Theory (IRT)** scoring methods, with multi-website support for WordPress integration.
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### Key Features
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| Feature | Description |
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|---------|-------------|
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| **CTT Scoring** | Classical Test Theory with exact Excel formula compatibility |
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| **IRT Support** | Item Response Theory (1PL Rasch model) for adaptive testing |
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| **Multi-Site** | Single backend serving multiple WordPress sites |
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| **AI Generation** | Automatic question variant generation via OpenRouter |
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| **Excel Import/Export** | Bulk import/export questions from Excel files |
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| **Adaptive Testing** | Computer Adaptive Testing (CAT) with theta estimation |
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| **Normalization** | Static, dynamic, or hybrid score normalization |
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---
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## Project Purpose
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The system replaces traditional fixed-difficulty exams with an **adaptive question bank** that:
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1. **Measures student ability accurately** using IRT theta estimation
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2. **Provides comparable scores** across different exam sessions via normalization
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3. **Generates new questions** using AI when needed
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4. **Integrates with WordPress** LMS platforms for student access
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5. **Reduces exam fraud** by delivering different question variants to each student
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|
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---
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## Tech Stack
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### Core Technologies
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```
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Framework: FastAPI >= 0.104.1
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Server: Uvicorn >= 0.24.0
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Database: PostgreSQL + SQLAlchemy 2.0 (async)
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ORM: SQLAlchemy >= 2.0.23
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Driver: asyncpg >= 0.29.0
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Migrations: Alembic >= 1.13.0
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Validation: Pydantic >= 2.5.0
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```
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### Data Processing
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```
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Excel: openpyxl >= 3.1.2, pandas >= 2.1.4
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Math/Science: numpy >= 1.26.2, scipy >= 1.11.4
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```
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### External Integrations
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```
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AI: OpenAI >= 1.6.1 (OpenRouter API)
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Task Queue: Celery >= 5.3.6, Redis >= 5.0.1
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Admin Panel: FastAPI-Admin >= 1.0.0
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```
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|
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---
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## Project Structure
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```
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yellow-bank-soal/
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├── app/
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│ ├── __init__.py
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│ ├── main.py # FastAPI app entry point
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│ ├── admin.py # FastAPI Admin configuration
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│ ├── admin_web.py # Admin web interface
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│ ├── database.py # Database configuration & session
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│ │
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│ ├── api/
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│ │ └── v1/
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│ │ ├── __init__.py
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│ │ └── session.py # Adaptive session endpoints
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│ │
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│ ├── core/
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│ │ ├── __init__.py
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│ │ ├── auth.py # Authentication & authorization
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│ │ ├── config.py # Settings from environment
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│ │ └── rate_limit.py # Rate limiting
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│ │
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│ ├── models/
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│ │ ├── __init__.py
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│ │ ├── ai_generation_run.py
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│ │ ├── item.py # Question items
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│ │ ├── report_schedule.py
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│ │ ├── session.py # Student tryout sessions
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│ │ ├── tryout.py # Tryout configurations
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│ │ ├── tryout_import_snapshot.py
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│ │ ├── tryout_snapshot_question.py
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│ │ ├── tryout_stats.py # Normalization statistics
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│ │ ├── user.py
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│ │ ├── user_answer.py # Student responses
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│ │ └── website.py
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│ │
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│ ├── routers/
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│ │ ├── __init__.py
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│ │ ├── admin.py # Admin-only endpoints
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│ │ ├── ai.py # AI generation endpoints
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│ │ ├── import_export.py # Excel import/export
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│ │ ├── reports.py # Report generation
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│ │ ├── sessions.py # Session management
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│ │ ├── tryouts.py # Tryout configuration
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│ │ └── wordpress.py # WordPress integration
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│ │
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│ ├── schemas/ # Pydantic request/response models
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│ │ ├── __init__.py
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│ │ ├── ai.py
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│ │ ├── report.py
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│ │ ├── session.py
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│ │ ├── tryout.py
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│ │ └── wordpress.py
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│ │
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│ └── services/
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│ ├── __init__.py
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│ ├── ai_generation.py # OpenRouter integration
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│ ├── cat_selection.py # Computer Adaptive Testing
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│ ├── config_management.py
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│ ├── ctt_scoring.py # CTT scoring engine
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│ ├── excel_import.py # Excel parsing
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│ ├── irt_calibration.py # IRT calibration
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│ ├── normalization.py
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│ ├── reporting.py
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│ ├── tryout_json_import.py
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│ └── wordpress_auth.py
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│
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├── alembic/ # Database migrations
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│ ├── env.py
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│ ├── script.py.mako
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│ └── versions/
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│
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├── tests/ # Unit & integration tests
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│ ├── test_auth_scope.py
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│ ├── test_auth_tokens.py
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│ ├── test_model_mappings.py
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│ ├── test_normalization.py
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│ ├── test_operational_hardening.py
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│ ├── test_route_wiring.py
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│ ├── test_security_regressions.py
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│ └── test_tryout_json_import.py
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│
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├── requirements.txt
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├── alembic.ini
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├── irt_1pl_mle.py # Standalone IRT MLE script
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├── PRD.md # Product Requirements Document
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├── project-brief.md # Technical specification
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└── handoff.md # Project handoff context
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```
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---
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## Core Concepts
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### 1. Tryout (Exam)
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A **Tryout** represents a complete exam/test with configurable behavior:
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```python
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scoring_mode: "ctt" | "irt" | "hybrid"
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selection_mode: "fixed" | "adaptive" | "hybrid"
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normalization_mode: "static" | "dynamic" | "hybrid"
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```
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### 2. Item (Question)
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An **Item** represents a single question with:
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- **Content**: stem (question text), options (A/B/C/D), correct_answer
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- **CTT Parameters**: p-value (difficulty), bobot (weight)
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- **IRT Parameters**: b (difficulty), se (standard error)
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- **Metadata**: slot position, difficulty level, AI generation info
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### 3. Session (Student Attempt)
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A **Session** tracks a student's attempt:
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- Links student (`wp_user_id`) to a Tryout
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- Records all answers via `UserAnswer` records
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- Stores computed scores: NM, NN, theta
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### 4. Website (Multi-Tenant)
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The system supports **multiple WordPress websites** from a single backend:
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- Each website has isolated data
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- Authenticated via `X-Website-ID` header
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- WordPress JWT tokens for authentication
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---
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## Data Models
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### Entity Relationship Diagram
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```mermaid
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erDiagram
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Website ||--o{ Tryout : "hosts"
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Website ||--o{ User : "contains"
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Website ||--o{ Session : "serves"
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Website ||--o{ Item : "contains"
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Tryout ||--o{ Item : "contains"
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Tryout ||--o{ Session : "has"
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Tryout ||--o{ TryoutStats : "tracks"
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Session ||--o{ UserAnswer : "contains"
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Session ||--o{ User : "belongs to"
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Item ||--o{ UserAnswer : "answered by"
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Item ||--o{ Item : "has variants"
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AIGenerationRun ||--o{ Item : "generates"
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```
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### Model Summary
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| Model | Purpose | Key Fields |
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|-------|---------|------------|
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| `Website` | Multi-tenant isolation | domain, wordpress_url |
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| `User` | WordPress user mapping | wp_user_id, website_id |
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| `Tryout` | Exam configuration | scoring_mode, selection_mode, normalization_mode |
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| `Item` | Question | stem, options, ctt_p, ctt_bobot, irt_b, irt_se |
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| `Session` | Student attempt | session_id, NM, NN, theta |
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| `UserAnswer` | Single response | response, is_correct, bobot_earned |
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| `TryoutStats` | Normalization data | participant_count, rataan, sb |
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| `AIGenerationRun` | AI generation batch | model, status, items_generated |
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---
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## API Endpoints
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### Public API (via `/api/v1`)
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| Method | Endpoint | Description |
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|--------|----------|-------------|
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| `GET` | `/tryout/{tryout_id}/config` | Get tryout configuration |
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| `PUT` | `/tryout/{tryout_id}/normalization` | Update normalization settings |
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| `GET` | `/tryout/` | List tryouts for website |
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| `GET` | `/tryout/{tryout_id}/calibration-status` | Get IRT calibration status |
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| `POST` | `/tryout/{tryout_id}/calibrate` | Trigger IRT calibration |
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| `POST` | `/session/` | Create new session |
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| `GET` | `/session/{session_id}` | Get session details |
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| `POST` | `/session/{session_id}/complete` | Submit answers, calculate scores |
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### Admin API (requires admin role)
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| Method | Endpoint | Description |
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|--------|----------|-------------|
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| `POST` | `/ai/generate` | Generate AI questions |
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| `POST` | `/import/excel` | Import questions from Excel |
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| `GET` | `/export/excel/{tryout_id}` | Export questions to Excel |
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| `GET` | `/reports/*` | Generate various reports |
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### Adaptive Session API (via `/api/v1/session`)
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| Method | Endpoint | Description |
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|--------|----------|-------------|
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| `POST` | `/adaptive/start` | Start adaptive session |
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| `POST` | `/adaptive/respond` | Submit answer, get next item |
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| `POST` | `/adaptive/complete` | Complete adaptive session |
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---
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## Key Services
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### 1. CTT Scoring Engine (`ctt_scoring.py`)
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Implements Classical Test Theory scoring with exact Excel formulas.
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**Key Functions:**
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- `calculate_ctt_p()` - Difficulty: p = Σ Benar / Total Peserta
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- `calculate_ctt_bobot()` - Weight: Bobot = 1 - p
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- `calculate_ctt_nm()` - Raw Score: NM = (Total_Bobot / Total_Bobot_Max) × 1000
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- `calculate_ctt_nn()` - Normalized: NN = 500 + 100 × ((NM - Rataan) / SB)
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- `categorize_difficulty()` - Categorize by p-value
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- `update_tryout_stats()` - Incrementally update normalization stats
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|
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### 2. IRT Calibration (`irt_calibration.py`)
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|
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Implements Item Response Theory (1PL Rasch model) for adaptive testing.
|
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|
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**Key Functions:**
|
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- `estimate_theta_mle()` - MLE theta estimation for students
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- `estimate_b()` - IRT difficulty calibration for items
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- `calibrate_item()` - Calibrate single item from response data
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- `calibrate_all()` - Batch calibrate all items in tryout
|
||||
- `calculate_fisher_information()` - Fisher information for item selection
|
||||
|
||||
**Parameters:**
|
||||
- θ (theta): Student ability [-3, +3]
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||||
- b: Item difficulty [-3, +3]
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||||
- Probability: P(θ) = 1 / (1 + exp(-(θ - b)))
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||||
|
||||
### 3. AI Generation (`ai_generation.py`)
|
||||
|
||||
Generates question variants using OpenRouter API.
|
||||
|
||||
**Key Functions:**
|
||||
- `generate_question()` - Generate single question via OpenRouter
|
||||
- `generate_questions_batch()` - Generate multiple questions
|
||||
- `save_ai_question()` - Save generated question to database
|
||||
- `check_cache_reuse()` - Check for reusable similar questions
|
||||
|
||||
**Models Supported:**
|
||||
- Qwen 2.5 32B (balanced)
|
||||
- Mistral Small (low cost)
|
||||
- Llama 3.3 70B (premium)
|
||||
|
||||
### 4. Excel Import/Export (`excel_import.py`)
|
||||
|
||||
Bulk import/export questions from Excel files.
|
||||
|
||||
**Key Functions:**
|
||||
- `parse_excel_import()` - Parse Excel file to items
|
||||
- `bulk_insert_items()` - Insert parsed items to database
|
||||
- `export_questions_to_excel()` - Export tryout to Excel
|
||||
|
||||
### 5. CAT Selection (`cat_selection.py`)
|
||||
|
||||
Computer Adaptive Testing item selection algorithm.
|
||||
|
||||
**Key Functions:**
|
||||
- `select_next_item()` - Select next item based on theta estimate
|
||||
- `calculate_theta_update()` - Update theta after response
|
||||
- `check_termination()` - Check if test should end
|
||||
|
||||
---
|
||||
|
||||
## Scoring Formulas
|
||||
|
||||
### CTT (Classical Test Theory)
|
||||
|
||||
Based on exact client Excel formulas:
|
||||
|
||||
```python
|
||||
# STEP 1: Tingkat Kesukaran (p-value)
|
||||
p = Σ Benar / Total Peserta
|
||||
|
||||
# STEP 2: Bobot (Weight)
|
||||
Bobot = 1 - p
|
||||
|
||||
# STEP 3: Total Benar per Siswa
|
||||
Total_Benar = count of correct answers
|
||||
|
||||
# STEP 4: Total Bobot Earned per Siswa
|
||||
Total_Bobot_Siswa = Σ Bobot for each correct answer
|
||||
|
||||
# STEP 5: Nilai Mentah (Raw Score)
|
||||
NM = (Total_Bobot_Siswa / Total_Bobot_Max) × 1000
|
||||
|
||||
# STEP 6: Nilai Nasional (Normalized Score)
|
||||
NN = 500 + 100 × ((NM - Rataan) / SB)
|
||||
```
|
||||
|
||||
### IRT (Item Response Theory)
|
||||
|
||||
1PL Rasch Model:
|
||||
|
||||
```python
|
||||
# Probability of correct response
|
||||
P(θ, b) = 1 / (1 + exp(-(θ - b)))
|
||||
|
||||
# Log-likelihood for MLE
|
||||
LL = Σ [u_i × log(P) + (1-u_i) × log(1-P)]
|
||||
|
||||
# Theta estimation via MLE
|
||||
θ_mle = argmax_θ LL(θ)
|
||||
```
|
||||
|
||||
### Difficulty Categories (CTT Standard)
|
||||
|
||||
| p-value | Category | Description |
|
||||
|---------|----------|-------------|
|
||||
| p < 0.30 | Sulit | Difficult |
|
||||
| 0.30 ≤ p ≤ 0.70 | Sedang | Medium |
|
||||
| p > 0.70 | Mudah | Easy |
|
||||
|
||||
---
|
||||
|
||||
## Configuration
|
||||
|
||||
### Environment Variables
|
||||
|
||||
```bash
|
||||
# Database
|
||||
DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/irt_bank_soal
|
||||
|
||||
# FastAPI
|
||||
SECRET_KEY=your-secret-key-here
|
||||
ENVIRONMENT=development # development, staging, production
|
||||
ENABLE_ADMIN=true
|
||||
ADMIN_USERNAME=admin
|
||||
ADMIN_PASSWORD=your-password
|
||||
|
||||
# OpenRouter (AI)
|
||||
OPENROUTER_API_KEY=sk-or-v1-xxx
|
||||
OPENROUTER_MODEL_QWEN=qwen/qwen2.5-32b-instruct
|
||||
OPENROUTER_MODEL_CHEAP=mistralai/mistral-small-2603
|
||||
OPENROUTER_MODEL_LLAMA=meta-llama/llama-3.3-70b-instruct
|
||||
|
||||
# Redis/Celery
|
||||
REDIS_URL=redis://localhost:6379/0
|
||||
CELERY_BROKER_URL=redis://localhost:6379/0
|
||||
|
||||
# CORS
|
||||
ALLOWED_ORIGINS=http://localhost:3000,https://yourdomain.com
|
||||
```
|
||||
|
||||
### Tryout Configuration Options
|
||||
|
||||
```python
|
||||
# Scoring Mode
|
||||
scoring_mode = "ctt" # Classical Test Theory
|
||||
scoring_mode = "irt" # Item Response Theory
|
||||
scoring_mode = "hybrid" # Both (IRT for calibration, CTT for scoring)
|
||||
|
||||
# Selection Mode
|
||||
selection_mode = "fixed" # Fixed order questions
|
||||
selection_mode = "adaptive" # Computer Adaptive Testing
|
||||
selection_mode = "hybrid" # Start fixed, switch to adaptive
|
||||
|
||||
# Normalization Mode
|
||||
normalization_mode = "static" # Use hardcoded rataan/sb
|
||||
normalization_mode = "dynamic" # Calculate from participant data
|
||||
normalization_mode = "hybrid" # Dynamic when sufficient data
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Workflows
|
||||
|
||||
### 1. Student Taking a Tryout
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant S as Student
|
||||
participant API as FastAPI
|
||||
participant WP as WordPress
|
||||
|
||||
S->>API: POST /session/ (start session)
|
||||
API-->>S: session_id
|
||||
|
||||
loop For each question
|
||||
S->>API: GET /session/{id}/next-item
|
||||
API-->>S: Question data
|
||||
|
||||
S->>API: POST /session/{id}/answer
|
||||
API-->>S: Next question or completion
|
||||
end
|
||||
|
||||
S->>API: POST /session/{id}/complete
|
||||
API-->>S: NM, NN scores
|
||||
```
|
||||
|
||||
### 2. Admin Importing Questions
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
A[Upload Excel File] --> B[Parse Excel]
|
||||
B --> C{Validate Structure}
|
||||
C -->|Invalid| D[Return Error]
|
||||
C -->|Valid| E[Calculate CTT p & bobot]
|
||||
E --> F[Bulk Insert Items]
|
||||
F --> G[Commit to Database]
|
||||
G --> H[Return Import Summary]
|
||||
```
|
||||
|
||||
### 3. AI Question Generation
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
A[Request Generation] --> B{Check Cache}
|
||||
B -->|Found similar| C[Return Cached]
|
||||
B -->|Not found| D[Call OpenRouter API]
|
||||
D --> E{Parse Response}
|
||||
E -->|Parse Error| F[Return Error]
|
||||
E -->|Success| G[Save to Database]
|
||||
G --> H[Return Generated Item]
|
||||
```
|
||||
|
||||
### 4. IRT Calibration
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
A[Collect Responses] --> B{Enough Data?}
|
||||
B -->|No| C[Wait for more]
|
||||
B -->|Yes| D[For each Item]
|
||||
D --> E[Get Response Matrix]
|
||||
E --> F[Estimate b via MLE]
|
||||
F --> G[Calculate Standard Error]
|
||||
G --> H[Update Item]
|
||||
H --> D
|
||||
D --> I[Mark Items Calibrated]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Deployment
|
||||
|
||||
### Requirements
|
||||
|
||||
- Python 3.10+
|
||||
- PostgreSQL 14+
|
||||
- Redis 6+ (for Celery)
|
||||
- Nginx (reverse proxy)
|
||||
- aaPanel with Python Manager (recommended)
|
||||
|
||||
### Running the Application
|
||||
|
||||
```bash
|
||||
# Install dependencies
|
||||
pip install -r requirements.txt
|
||||
|
||||
# Run migrations
|
||||
alembic upgrade head
|
||||
|
||||
# Start server
|
||||
uvicorn app.main:app --host 0.0.0.0 --port 8000
|
||||
|
||||
# Or with reload (development)
|
||||
uvicorn app.main:app --reload
|
||||
```
|
||||
|
||||
### Running Tests
|
||||
|
||||
```bash
|
||||
pytest tests/ -v
|
||||
```
|
||||
|
||||
### API Documentation
|
||||
|
||||
- Swagger UI: `http://localhost:8000/docs`
|
||||
- ReDoc: `http://localhost:8000/redoc`
|
||||
- OpenAPI JSON: `http://localhost:8000/openapi.json`
|
||||
|
||||
---
|
||||
|
||||
## Security Considerations
|
||||
|
||||
### Authentication
|
||||
|
||||
- WordPress JWT tokens for user authentication
|
||||
- `X-Website-ID` header for multi-tenant isolation
|
||||
- Admin routes protected by admin role check
|
||||
|
||||
### Production Hardening
|
||||
|
||||
1. **SECRET_KEY** must be set to a strong, unique value
|
||||
2. **ADMIN_PASSWORD** must not be the default
|
||||
3. **CORS** origins should be explicitly configured
|
||||
4. **Database** connections should use SSL in production
|
||||
5. **Rate limiting** enabled for AI generation endpoints
|
||||
|
||||
---
|
||||
|
||||
## Glossary
|
||||
|
||||
| Term | Definition |
|
||||
|------|------------|
|
||||
| **Tryout** | An exam/test assessment |
|
||||
| **Item** | A single question in a tryout |
|
||||
| **Session** | A student's attempt at a tryout |
|
||||
| **CTT** | Classical Test Theory - traditional scoring |
|
||||
| **IRT** | Item Response Theory - modern adaptive scoring |
|
||||
| **NM** | Nilai Mentah - raw score [0-1000] |
|
||||
| **NN** | Nilai Nasional - normalized score [0-1000] |
|
||||
| **θ (theta)** | IRT ability estimate [-3 to +3] |
|
||||
| **b** | IRT item difficulty [-3 to +3] |
|
||||
| **p-value** | CTT proportion correct [0 to 1] |
|
||||
| **Bobot** | CTT weight (1 - p) |
|
||||
| **Rataan** | Mean (Indonesian) |
|
||||
| **SB** | Simpangan Baku - Standard Deviation |
|
||||
| **CAT** | Computer Adaptive Testing |
|
||||
| **MLE** | Maximum Likelihood Estimation |
|
||||
|
||||
---
|
||||
|
||||
## References
|
||||
|
||||
- [PRD.md](./PRD.md) - Complete Product Requirements Document
|
||||
- [project-brief.md](./project-brief.md) - Original technical specification
|
||||
- [FastAPI Documentation](https://fastapi.tiangolo.com/)
|
||||
- [SQLAlchemy 2.0](https://docs.sqlalchemy.org/en/20/)
|
||||
- [Item Response Theory](https://en.wikipedia.org/wiki/Item_response_theory)
|
||||
Reference in New Issue
Block a user