Exam Reliability & Question Quality Analyzer
An industry-grade tool that extracts questions from exam documents, runs Classical Test Theory (CTT) analysis, and detects duplicate questions — powered by a FastAPI backend + React frontend.
| Feature | Detail |
|---|---|
| Document Ingestion | PDF (text + scanned via OCR), DOCX, TXT, Images (JPG/PNG/TIFF) |
| Normalization | Regex-based question parser → structured JSON |
| Difficulty Index | p-value per question (proportion correct) |
| Discrimination Index | D-value using top/bottom 27% split |
| Distractor Efficiency | Flags non-correct options chosen by < 5% |
| Cronbach's Alpha | Exam-level internal consistency |
| Similarity Detection | TF-IDF + Cosine Similarity, union-find clustering |
| React Dashboard | Upload → analysis → interactive charts + question table |
git clone https://github.com/Codeguruu03/ExamForge.git
cd ExamForge# Create and activate a virtual environment
python -m venv venv
venv\Scripts\activate # Windows
# source venv/bin/activate # macOS/Linux
# Install dependencies
pip install -r backend/requirements.txt
# Configure environment
cp .env.example .env
# Edit .env and add your OCR_SPACE_API_KEYpython -m uvicorn backend.main:app --reload --port 8000API docs available at: http://localhost:8000/docs
cd frontend
npm install
npm run devDashboard available at: http://localhost:5173
Upload an exam document. Returns structured NormalizationResult.
Body: multipart/form-data
Field: file — PDF, DOCX, TXT, JPG, PNG, TIFF, BMP, GIF
{
"exam": {
"exam_id": "uuid",
"total_questions": 25,
"questions": [ { "id": 1, "text": "...", "options": [...], "correct_option": "C" } ]
},
"warnings": [],
"raw_text_preview": "..."
}Run Classical Test Theory analysis on an exam with student responses.
{
"exam": { ... },
"student_responses": [
{ "student_id": "S01", "responses": { "1": "C", "2": "A" } }
],
"correct_answers": { "1": "C", "2": "B" }
}Returns ExamStats with per-question difficulty_index, discrimination_index, distractor breakdown, and exam-level cronbach_alpha.
Detect duplicate/near-duplicate questions using TF-IDF + Cosine Similarity.
{ "exam": { ... } }Returns SimilarityReport — pairs at ≥ 0.95 similarity (duplicates) and 0.60–0.94 (near-duplicates), grouped into clusters.
ExamForge/
├── backend/
│ ├── api/
│ │ ├── endpoints/
│ │ │ ├── upload.py # POST /api/upload/
│ │ │ ├── analyze.py # POST /api/analyze/
│ │ │ └── similarity.py # POST /api/similarity/
│ │ └── router.py
│ ├── core/
│ │ ├── models.py # Exam, Question, Option, NormalizationResult
│ │ ├── stat_models.py # ExamStats, QuestionStat, DistractorStat
│ │ └── similarity_models.py# SimilarityReport, SimilarPair, Cluster
│ ├── services/
│ │ ├── ingestion.py # PDF/DOCX/Image text extraction
│ │ ├── cleaner.py # Text noise removal
│ │ ├── normalizer.py # Regex question parser
│ │ ├── stats_engine.py # CTT metrics engine
│ │ └── similarity_engine.py# TF-IDF similarity engine
│ ├── main.py
│ └── requirements.txt
├── frontend/
│ └── src/
│ ├── pages/
│ │ ├── UploadPage.jsx
│ │ └── Dashboard.jsx
│ └── components/
│ ├── StatsOverview.jsx
│ ├── DifficultyChart.jsx
│ ├── SimilarityReport.jsx
│ └── QuestionTable.jsx
├── .env.example
└── README.md
Backend: FastAPI · Uvicorn · PyMuPDF · python-docx · NumPy · scikit-learn · python-dotenv · OCR.space API
Frontend: React 19 · Vite 7 · Tailwind CSS · Recharts · Axios
| Variable | Required | Description |
|---|---|---|
OCR_SPACE_API_KEY |
✅ | Free key from ocr.space |
UPLOAD_DIR |
Optional | Upload directory (default: uploads) |