TRACE: Total Recognition And Course Evaluation

TRACE is a Python-based desktop application designed to help users compare Course Learning Outcomes (CLOs) from different Excel files. The tool leverages advanced natural language processing (NLP) using the all-MiniLM-L6-v2 Sentence Transformer model to calculate semantic similarity between CLOs. This allows for a deep understanding of curriculum changes beyond simple keyword matching.

The application is built with a PyQt5 graphical user interface and utilizes multithreading to handle large batches of CLO comparisons efficiently.

• Semantic Similarity Analysis: Uses the all-MiniLM-L6-v2 transformer model to compare CLOs based on meaning.
• Excel File Comparison: Seamlessly compare CLOs from old and new course Excel files.
• Interactive UI: A modern PyQt5-based interface for easy file selection and results visualization.
• Multithreaded Processing: Process large datasets without freezing the UI.
• Dynamic Thresholding: Adjust similarity thresholds directly within the app to refine comparison results.
• Exportable Results: Download and save comparison reports.

TRACE/
├── CLOComparisonApp/
│   ├── UI/                    # PyQt UI component definitions
│   ├── icons/                 # Application icons and assets
│   ├── tests/                 # Unit tests for core logic
│   ├── main.py                # Main entry point for the application
│   ├── models.py              # Sentence Transformer model initialization
│   ├── threads.py             # Multithreading logic for comparisons
│   ├── ui_components.py        # Custom UI widgets and layouts
│   └── utils.py               # Data extraction and utility functions
├── LICENSE                    # MIT License
├── README.md                  # Project documentation
└── requirements.txt           # Python dependencies

1. Clone the repository:

   git clone https://github.com/AkshunChauhan/TRACE.git
   cd TRACE

2. Set up a virtual environment (recommended):

   python -m venv venv
   # On Windows:
   .\venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   python CLOComparisonApp/main.py

To ensure the correctness of the extraction and comparison logic, run the included tests:

pytest CLOComparisonApp/tests/

Contributions are welcome! Please feel free to submit a Pull Request or open an issue for any bugs or feature requests.

This project is licensed under the MIT License. See the LICENSE file for details.

Akshun Chauhan

• GitHub: @AkshunChauhan