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RateMyGaucho (Chrome Extension)

RateMyGaucho enhances UCSB GOLD by showing professor ratings and course data directly on course result pages. It works offline and preserves your privacy by using a local dataset.

Demo Video: https://www.youtube.com/watch?v=fl7-icSAves

Novel Features

  • Offline & Private: Works without an internet connection and never sends your data to external servers. All data lives in a single packaged CSV (courses_final_enrollment.csv).
  • Gaucho-Themed Ratings: Displays professor ratings with custom, partially-filled Gaucho star icons for at-a-glance assessments.
  • In-Depth Course Data: Goes beyond professor ratings to show course-specific details like grading trends, enrollment history, and recent student reviews.
  • Visual Summaries: Inline bar charts translate grade distributions and historic enrollment snapshots into easy-to-interpret visuals.
  • Smart Review Filtering: Intelligently filters reviews to show only those relevant to the specific instructor, providing more accurate insights.
  • UCSB Plat Integration: Provides direct links to professor profiles and curriculum pages on UCSB Plat.

Installation

  1. Download the latest release from the Releases page.
  2. Extract the ZIP file.
  3. Open Chrome, go to chrome://extensions, and enable "Developer mode".
  4. Click "Load unpacked" and select the extracted folder.

Building

To create a distributable ZIP file, run the appropriate script for your OS:

  • Windows: ./scripts/package.ps1
  • macOS/Linux: bash ./scripts/package.sh

The build script bundles the unified dataset and content script into dist/RateMyGaucho.zip, which is ready to upload to the Chrome Web Store.

Data Source

The extension ships with courses_final_enrollment.csv as its only data source. Every rating, review, and course record comes from this file, ensuring consistent results across the UI. When updating data, replace this CSV and rebuild the package.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

MIT © 2025