Dropout Learning

A sophisticated learning platform that applies advanced educational principles to create personalized, mastery-based learning experiences from uploaded PDF study materials, with a focus on data science and related technical fields.

Features

• Knowledge Graph Architecture: Maps relationships between topics, showing prerequisites and connections that support structured learning
• Adaptive Diagnostic Assessment: Identifies each learner's knowledge frontier and gaps to create personalized learning paths
• Mastery-Based Progression: Students aren't pushed forward until they've demonstrated mastery of prerequisite skills
• Granular Knowledge Points: Lessons are scaffolded into small, digestible segments with worked examples and practice problems
• Immediate Feedback System: Provides explanatory feedback within minutes of starting a new lesson
• Daily XP Goal System: Gamifies the learning experience with progress tracking and streak mechanisms
• Module-Based Content Organization: Each uploaded PDF creates its own SQLite database to store module-specific content
• Interactive Code Editor: Built-in Python and R code editor with execution capabilities for data science exercises
• Data Visualization Tools: Create and interact with data visualizations based on your analyses
• Accessibility Features: High-contrast mode, dyslexia-friendly fonts, and adjustable text sizes
• Spaced Repetition: Automatic scheduling of review materials at optimal intervals for long-term retention

Tech Stack

• Backend: Node.js with Express
• Database: SQLite (one database per PDF module)
• PDF Processing: pdf-lib for extracting content from uploaded PDFs
• Frontend: Pure JavaScript, HTML, and CSS with Bootstrap for styling

Getting Started

Prerequisites

• Node.js (v14 or higher)
• npm

Installation

1. Clone the repository:

   git clone https://github.com/arvindcr4/dropout-learning.git
   cd dropout-learning
   

2. Install dependencies:

   npm install
   

3. Start the server:

   node server.js
   

4. Access the application at http://localhost:5000

Usage

1. Upload Study Material: Upload a PDF document containing study material
2. Take the Adaptive Diagnostic: Complete the diagnostic assessment to identify your knowledge frontier
3. Follow Your Custom Course: Based on the diagnostic results, a personalized learning path is created
4. Master Knowledge Points: Work through each knowledge point with worked examples and practice problems
5. Complete Coding Exercises: Practice data science skills with interactive coding challenges
6. Explore the Knowledge Graph: Visualize your progress and plan your learning journey
7. Track Progress: Monitor your daily XP goals and learning streaks

Project Structure

• server.js: Main Express application and API endpoints
• db.js: Database utilities for managing SQLite databases
• public/: Frontend assets and HTML pages
  • index.html: Main application interface
  • diagnostic.html: Adaptive diagnostic experience
  • knowledge-point.html: Knowledge point lesson interface
  • code-editor.html: Interactive coding environment for data science exercises
  • knowledge-graph.html: Interactive visualization of learning pathways
• databases/: Generated SQLite databases (one per PDF)
• uploads/: Temporary storage for uploaded PDFs

Implementation Details

Knowledge Points Structure

Each lesson is broken down into knowledge points following this sequence:

1. Worked example with subgoal labeling
2. Up to 5 practice problems similar to the worked example
3. Mastery assessment before unlocking the next knowledge point

Interactive Code Environment

• Supports Python and R for data science exercises
• Real-time code execution and feedback
• Test case validation for automatic assessment
• Visualization capabilities for data outputs
• Scaffolded hints to guide learning

Knowledge Graph Visualization

• D3.js-powered interactive graph
• Visual representation of topic relationships
• Color-coded by subject category and mastery level
• Zoom and pan capabilities for exploration
• Recommended personalized learning paths

XP (eXperience Points) System

• XP rewards correct answers and good learning habits
• Each XP is roughly equivalent to 1 minute of focused effort
• Daily XP goals are adjustable to suit individual learning pace
• Streak tracking for consistent engagement

Mastery Requirements

• Mastery is achieved by correctly solving a minimum threshold of practice problems
• Prerequisites must be mastered before advancing to more complex topics
• The system creates personalized learning paths based on demonstrated mastery
• Adaptive difficulty based on performance

Spaced Repetition

• Automatic scheduling of review materials
• Optimal intervals for long-term retention
• Interleaving of topics to enhance learning

License

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