A Python-based analytics platform for deep student performance analysis, benchmark identification, weakness detection, question-level diagnostics, time-efficiency analysis, trend tracking, and academic performance reporting.
- Top-20 student identification and benchmark analysis
- Test-wise and overall performance trends
- Student-wise deep performance profiling
- Chapter-wise strengths and weaknesses
- Question-level correct, wrong and not-attempted analysis
- Ideal-time benchmarking using correct responses from top-performing students
- Time-efficiency classification
- Charts, CSV outputs and analytical reports
For each question, the platform calculates an empirical benchmark from the average correct-response time of the Top-20 performers. A student's response can then be compared with that benchmark as Efficient, Needs Review, or Time Intensive. This is an analytical benchmark, not a fixed academic rule.
Student-Performance-Intelligence-Analytics-Platform/
├── data/sample_student_performance.csv
├── src/
│ ├── analysis.py
│ ├── benchmarks.py
│ ├── report.py
│ ├── visualizations.py
│ └── __init__.py
├── visuals/
├── reports/
├── tests/test_analysis.py
├── app.py
├── main.py
├── requirements.txt
├── .gitignore
├── LICENSE
└── README.md
pip install -r requirements.txt
python main.py
streamlit run app.pyThe platform is designed to move beyond marks and identify why performance changes, helping academic teams prioritize weak chapters, repeated question-level errors, unattempted questions, test-wise trends and time-efficiency gaps.
Use only synthetic or anonymized data in the public repository. Never upload real student names, phone numbers, internal identifiers, credentials or confidential institutional data.
MIT License.