Learning Planner

Personal learning management system that helps set learning goals, create plans, track progress, and provides spaced repetition review features.

Features

1. Learning Goal Management

• Skill tree definition and breakdown
• Knowledge point hierarchy management
• Goal priority settings
• Goal completion time planning

2. Learning Plan Generation

• Daily learning task generation
• Weekly learning plans
• Automatic plan adjustment
• Learning reminder settings

3. Progress Tracking and Visualization

• Real-time learning progress tracking
• Progress visualization charts
• Learning time statistics
• Completion rate analysis

4. Spaced Repetition Review System

• SM-2 algorithm implementation
• Flashcard-based review
• Automatic scheduling
• Forgetting curve optimization

5. Learning Resource Management

• Resource link bookmarking
• Resource categorization
• Resource to knowledge point mapping
• Resource usage statistics

6. Learning Outcome Evaluation

• Self-assessment records
• Test score management
• Learning effectiveness analysis
• Ability growth curves

Installation

cd ~/.openclaw/workspace/skills/learning-planner
pip install -e .

# Add to PATH (optional)
ln -s ~/.openclaw/workspace/skills/learning-planner/src/learning_cli.py ~/.local/bin/learning

Usage

Learning Goals

# Create learning goal
learning goal create "Python Programming" --description "Master Python programming language" --deadline 2024-12-31

# Create sub-goals (knowledge point breakdown)
learning goal create "Python Basic Syntax" --parent 1 --priority high
learning goal create "Python OOP" --parent 1 --priority high
learning goal create "Python Advanced Features" --parent 1 --priority medium

# List goals
learning goal list

# View goal details
learning goal show 1

# Update goal progress
learning goal progress 1 --percent 75

# Complete goal
learning goal complete 1

Learning Plans

# Generate today's learning plan
learning plan today

# Generate this week's learning plan
learning plan week

# View plans
learning plan list

# Mark task complete
learning plan complete 1

# Postpone task
learning plan postpone 1 --days 1

Spaced Repetition Review

# Create review card
learning card create "Python list comprehension syntax" --answer "[x for x in iterable if condition]" --tags python,basics

# Today's review
learning review today

# View review statistics
learning review stats

# Manually adjust card difficulty
learning card difficulty 1 --level hard

Learning Resources

# Add resource
learning resource add "Python Official Documentation" --url https://docs.python.org --type documentation --tags python

# Link resource to goal
learning resource link 1 --goal 1

# List resources
learning resource list

# Search resources
learning resource search python

Progress and Reports

# Learning statistics
learning stats

# Generate learning report
learning report --days 30

# View skill tree progress
learning tree

# Learning time statistics
learning time --days 7

Data Storage

Database location: ~/.config/learning-planner/learning.db

# View database path
learning data path

Tech Stack

• Python 3.8+
• SQLite data storage
• Click (CLI framework)
• Rich (terminal styling)
• SM-2 spaced repetition algorithm

Data Models

Learning Goals Table (goals)

{
    id: int
    title: str              # Goal name
    description: str        # Description
    parent_id: int          # Parent goal ID
    priority: str           # Priority: low, medium, high
    status: str             # Status: active, completed, paused
    progress: float         # Progress 0-100
    deadline: str           # Deadline
    estimated_hours: int    # Estimated study hours
    completed_hours: int    # Completed hours
    created_at: str
    updated_at: str
}

Learning Plans Table (plans)

{
    id: int
    goal_id: int            # Related goal
    title: str              # Task title
    description: str        # Description
    scheduled_date: str     # Planned date
    estimated_minutes: int  # Estimated duration (minutes)
    status: str             # Status: pending, completed, postponed
    completed_at: str       # Completion time
    created_at: str
}

Review Cards Table (cards)

{
    id: int
    goal_id: int            # Related goal
    front: str              # Card front (question)
    back: str                # Card back (answer)
    tags: str               # Tags
    ease_factor: float      # Difficulty factor
    interval: int           # Interval days
    repetitions: int        # Repetition count
    next_review: str        # Next review time
    last_review: str        # Last review time
    created_at: str
}

Review Records Table (reviews)

{
    id: int
    card_id: int            # Card ID
    quality: int            # Rating 0-5
    reviewed_at: str        # Review time
    time_spent: int         # Time spent (seconds)
}

Learning Resources Table (resources)

{
    id: int
    title: str              # Resource name
    url: str                # Link
    resource_type: str      # Type: video, article, book, documentation
    tags: str               # Tags
    goal_id: int            # Related goal
    notes: str              # Notes
    created_at: str
}

Learning Sessions Table (sessions)

{
    id: int
    goal_id: int            # Related goal
    start_time: str         # Start time
    end_time: str           # End time
    duration: int           # Duration (minutes)
    notes: str              # Notes
}

SM-2 Algorithm Explanation

Spaced repetition algorithm based on SuperMemo-2:

1. Quality Rating: 0-5 points

   • 5: Perfect response
   • 4: Correct response, hesitated
   • 3: Correct response, difficult
   • 2: Incorrect, close to correct
   • 1: Incorrect, remembered some
   • 0: Completely forgot

2. Ease Factor (EF): Initial 2.5, range 1.3-2.5

   • EF' = EF + (0.1 - (5-q) * (0.08 + (5-q) * 0.02))

3. Interval Days:

   • First: 1 day
   • Second: 6 days
   • Nth: Previous interval * EF