Updated the README to provide a comprehensive explanation of the tool. The new README now includes:

A clear overview of what the tool does
Detailed key features section
Step-by-step instructions for running locally
File format requirements with example
A detailed "Using the Tool" section that explains how to use each feature
Technical details about edge counting, node ranking, and color coding
The documentation now covers:
What the tool is for
How to set it up
How to use it
What the visualizations mean
Technical implementation details

Author: tzacks-cl

README.md

@@ -1,15 +1,44 @@
 # Path Analysis Tool
 
-React + TypeScript + Vite + SWC (with Rust compiler)
+A visualization tool for analyzing student learning paths in educational software. Built with React + TypeScript + Vite + SWC (with Rust compiler).
 
 [Live URL](https://path-analysis.vercel.app/)
 
+## Overview
+
+This tool visualizes student learning paths through educational content, showing:
+- The sequence of steps students take
+- How many students follow each path
+- Success/failure rates at each step
+- Common patterns in student progression
+
+### Key Features
+
+1. **Path Visualization**
+   - Interactive directed graph showing student progression
+   - Edge thickness indicates number of unique students following each path
+   - Color coding for success (green) and failure (red) rates
+   - Node ranking based on step sequence
+
+2. **Filtering Options**
+   - Filter by student progress status (GRADUATED, PROMOTED, NOT_COMPLETED)
+   - Toggle self-loops (transitions back to the same step)
+   - Adjust minimum student threshold for edge visibility
+   - View top 5 most common student paths
+
+3. **Interactive Elements**
+   - Hover over edges to see detailed statistics
+   - Click nodes to see student counts and error rates
+   - Export graph as high-quality PNG
+   - Responsive design that works on different screen sizes
+
 ## How to Run Locally
+
 1. Make sure you have `Node.js` installed. You can download it from https://nodejs.org/en/download/
-1. This uses `bun` to run, build, and deploy. You will need to have the `bun` command installed. You can install it by running ```npm install -g @bun/cli```. Docs: https://bun.sh/
-2. Run ```bun install``` to download the necessary dependencies.
-3. You will need a local .env file with secret variables. Reach out to Ethan for these.
-4. Run ```bun run dev``` to start the development server.
+2. This uses `bun` to run, build, and deploy. You will need to have the `bun` command installed. You can install it by running ```npm install -g @bun/cli```. Docs: https://bun.sh/
+3. Run ```bun install``` to download the necessary dependencies.
+4. You will need a local .env file with secret variables. Reach out to Ethan for these.
+5. Run ```bun run dev``` to start the development server.
 
 ## File Format Requirements
 
@@ -33,6 +62,46 @@ Time,Step Name,Outcome,CF (Workspace Progress Status),Problem Name,Anon Student
 2024-01-01 10:01:00,Step 2,ERROR,NOT_COMPLETED,Problem 1,student123
 ```
 
+## Using the Tool
+
+1. **Upload Data**
+   - Click the upload button to select your data file
+   - The file should be in CSV or TSV format with the required fields
+
+2. **View the Graph**
+   - The main graph shows all student paths
+   - Edge thickness represents the number of unique students following each path
+   - Colors indicate success (green) or failure (red) rates
+   - Hover over edges to see detailed statistics
+
+3. **Filter and Adjust**
+   - Use the filter dropdown to view paths for specific student progress statuses
+   - Toggle self-loops on/off to include/exclude transitions back to the same step
+   - Adjust the minimum student threshold to show only paths followed by a certain number of students
+   - The threshold can be set as a percentage of total students or as an absolute number
+
+4. **Analyze Patterns**
+   - View the top 5 most common student paths
+   - Click on nodes to see detailed statistics about student progression
+   - Export the graph as a PNG for sharing or documentation
+
+## Technical Details
+
+### Edge Counting
+- Edges are counted based on unique students rather than total transitions
+- If a student makes the same transition multiple times, it's counted only once
+- Edge thickness is normalized relative to the most common path
+
+### Node Ranking
+- Nodes are ranked based on their position in the step sequence
+- This helps visualize the natural progression through the content
+
+### Color Coding
+- Green: Successful transitions (OK outcome)
+- Red: Failed transitions (ERROR outcome)
+- Blue: Hint-related transitions (INITIAL_HINT, HINT_LEVEL_CHANGE)
+- Yellow: Just-in-time feedback (JIT, FREEBIE_JIT)
+
 ## Expanding the ESLint configuration
 
 If you are developing a production application, we recommend updating the configuration to enable type aware lint rules: