Implement comprehensive study system with analysis, core functionality, tests, and CLI

[Copilot]

This PR implements a complete study system for the Python algorithm tutor as specified in the problem statement referencing @study_system_analysis.md.

What's Added

Core Study System (study_system.py)

• Session Management: Track individual study sessions with start/end times, topics covered, and performance metrics
• Progress Tracking: Persistent storage of student progress, problems solved, and topic mastery
• Adaptive Learning: Intelligent topic recommendations based on prerequisites and current mastery level
• Performance Analytics: Detailed statistics including success rates, study time, and learning patterns

Study System Analysis (study_system_analysis.md)

Comprehensive analysis document covering:

• Learning path management and adaptive progression
• Assessment framework with formative and summative evaluation
• Technical requirements for data storage and UI integration
• Implementation phases from core tracking to advanced ML-powered insights

Command Line Interface (tutor_cli.py)

User-friendly CLI providing:

# Start a study session
python tutor_cli.py -s alice start

# End session with progress data  
python tutor_cli.py -s alice end --session SESSION_ID --topics arrays --attempted 8 --completed 6

# View comprehensive statistics
python tutor_cli.py -s alice stats

# Get personalized topic recommendations
python tutor_cli.py -s alice recommend

Comprehensive Test Suite (test_study_system.py)

Complete test coverage validating:

• Basic functionality (session management, progress tracking)
• Data persistence across system restarts
• Topic progression with prerequisite handling
• All tests pass successfully ✅

Key Features

Structured Learning Path: The system includes a predefined curriculum covering:

• Beginner: Arrays, Linked Lists, Stacks & Queues
• Intermediate: Binary Trees, Graphs
• Advanced: Dynamic Programming

Prerequisite System: Topics are unlocked based on mastery of prerequisites, ensuring students follow a logical learning progression.

Performance Tracking: Tracks success rates with an 80% threshold for topic mastery, enabling adaptive difficulty adjustment.

Data Privacy: Student progress data is stored locally in JSON format with the study_data/ directory added to .gitignore to protect user privacy.

Usage Example

from study_system import StudySystemManager

# Initialize the study system
study_system = StudySystemManager()

# Start a session
session_id = study_system.start_study_session("student_name")

# End session with progress
study_system.end_study_session(
    "student_name", 
    session_id,
    ["arrays", "sorting"], 
    problems_attempted=5, 
    problems_completed=4
)

# Get personalized recommendations
recommendations = study_system.get_recommended_topics("student_name")

The implementation provides a solid foundation for an intelligent tutoring system that can adapt to individual learning patterns and guide students through algorithmic concepts effectively.

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[codara-ai-code-review]

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[llamapreview]

Auto Pull Request Review from LlamaPReview

1. Overview

1.1 Core Changes

• Primary purpose and scope: Implementation of a comprehensive study system for Python algorithm tutoring with session management, progress tracking, and adaptive learning capabilities
• Key components modified: Added study_system.py, tutor_cli.py, test_study_system.py, study_system_analysis.md
• Cross-component impacts: New data persistence layer, CLI interface integration, curriculum structure definition
• Business value alignment: Enables personalized learning paths, progress tracking, and adaptive recommendations for algorithm education

1.2 Technical Architecture

• System design modifications: Introduces layered architecture with data persistence, business logic, and CLI interface
• Component interaction changes: New StudySystemManager coordinates between data storage, session management, and recommendation engine
• Integration points impact: CLI provides user interface, JSON files serve as data storage backend
• Dependency changes and implications: Added json, datetime, pathlib dependencies; no external dependencies added

2. Critical Findings

2.1 Must Fix (P0🔴)

Issue: Division by zero risk and misleading performance calculation

• Analysis Confidence: High
• Impact: Incorrect performance metrics when no problems attempted; creates false mastery assessments
• Resolution: Use proper conditional logic instead of max(1) workaround

Issue: Incomplete topic mastery logic

• Analysis Confidence: High
• Impact: Topics marked as mastered based on single session performance rather than cumulative performance
• Resolution: Implement cumulative topic performance tracking across all sessions

2.2 Should Fix (P1🟡)

Issue: Lack of input validation

• Analysis Confidence: Medium
• Impact: Potential data integrity issues with invalid student IDs or topic names
• Suggested Solution: Add validation methods for student IDs and topic existence checks

Issue: Inefficient session lookup

• Analysis Confidence: Medium
• Impact: Performance degradation with large numbers of sessions due to linear search
• Suggested Solution: Use dictionary-based session lookup for O(1) access

Issue: Missing error handling in file operations

• Analysis Confidence: High
• Impact: Potential data loss or corruption from file I/O failures
• Suggested Solution: Add try-catch blocks around file operations with proper error logging

2.3 Consider (P2🟢)

Area: Data persistence optimization

• Analysis Confidence: Medium
• Improvement Opportunity: Better scalability through lazy loading or database backend

Area: Enhanced topic recommendations

• Analysis Confidence: Medium
• Improvement Opportunity: More intelligent recommendations using difficulty weighting and learning patterns

Area: Additional analytics metrics

• Analysis Confidence: Low
• Improvement Opportunity: Time tracking, learning velocity, and difficulty progression metrics

2.4 Summary of Action Items

• Immediate (P0): Fix performance calculation and topic mastery logic - critical for accurate assessments
• Short-term (P1): Add input validation, optimize session lookup, implement error handling - important for data integrity
• Future (P2): Consider scalability improvements and enhanced analytics - valuable but not critical

3. Technical Analysis

3.1 Code Logic Analysis

📁 study_system.py - end_study_session

• Submitted PR Code:

    session.performance_score = problems_completed / max(problems_attempted, 1)

• Analysis:
  • Current logic uses max(1) as workaround for division by zero but creates misleading metrics
  • Edge case: When problems_attempted=0, shows 0% completion which is incorrect
  • Cross-component impact: Affects topic mastery determination and recommendation accuracy
  • Business logic considerations: Accurate performance tracking is essential for adaptive learning
• LlamaPReview Suggested Improvements:

    if problems_attempted > 0:
        session.performance_score = problems_completed / problems_attempted
    else:
        session.performance_score = 0.0  # No problems attempted

• Improvement rationale:
  • Technical benefits: Clear, accurate performance calculation
  • Business value: Reliable progress tracking and mastery assessment
  • Risk assessment: Critical fix for core functionality

📁 study_system.py - end_study_session

• Submitted PR Code:

    for topic in topics_covered:
        if (topic not in progress.topics_mastered and 
            session.performance_score >= 0.8):  # 80% threshold
            progress.topics_mastered.append(topic)

• Analysis:
  • Current logic only considers performance in current session for mastery
  • Edge cases: Student could have poor performance in previous sessions but good in one session
  • Cross-component impact: Affects topic recommendations and learning path progression
  • Business logic considerations: Mastery should reflect consistent performance over time
• LlamaPReview Suggested Improvements:

    # Track cumulative performance per topic
    topic_performance = {}
    for past_session in progress.sessions:
        for topic in past_session.topics_covered:
            if topic not in topic_performance:
                topic_performance[topic] = {'attempted': 0, 'completed': 0}
            topic_performance[topic]['attempted'] += past_session.problems_attempted
            topic_performance[topic]['completed'] += past_session.problems_completed
    
    # Check mastery based on cumulative performance
    for topic in topics_covered:
        if topic not in progress.topics_mastered:
            perf = topic_performance.get(topic, {'attempted': 0, 'completed': 0})
            if perf['attempted'] > 0 and (perf['completed'] / perf['attempted']) >= 0.8:
                progress.topics_mastered.append(topic)

• Improvement rationale:
  • Technical benefits: More accurate mastery assessment
  • Business value: Better adaptive learning based on consistent performance
  • Risk assessment: Critical for educational effectiveness

📁 study_system.py - StudySystemManager.init

• Submitted PR Code:

    self.student_progress = self._load_progress()

• Analysis:
  • Current implementation loads all student data into memory on initialization
  • Performance impact: Becomes inefficient with large numbers of students
  • Scalability considerations: Limits system to small-scale deployment
• LlamaPReview Suggested Improvements:

    # Lazy load implementation
    self.student_progress = {}  # Empty dict, load on demand
    
    def _get_student_progress(self, student_id: str) -> StudentProgress:
        """Lazy load student progress when needed."""
        if student_id not in self.student_progress:
            # Load from file or initialize new student
            if self.progress_file.exists():
                with open(self.progress_file, 'r') as f:
                    data = json.load(f)
                    if student_id in data:
                        self.student_progress[student_id] = StudentProgress(**data[student_id])
                    else:
                        self.student_progress[student_id] = StudentProgress(student_id=student_id)
            else:
                self.student_progress[student_id] = StudentProgress(student_id=student_id)
        return self.student_progress[student_id]

• Improvement rationale:
  • Technical benefits: Better memory efficiency and scalability
  • Business value: Supports larger user bases
  • Risk assessment: Medium priority enhancement

3.2 Key Quality Aspects

• System scalability considerations: Current implementation may not scale well for large numbers of students
• Performance bottlenecks: Linear session lookup and full data loading could impact performance
• Testing strategy and coverage: Excellent test coverage with comprehensive test cases
• Documentation needs: Good overall documentation with usage examples

4. Overall Evaluation

• Technical assessment: Well-structured implementation with clean separation of concerns
• Business impact: Provides foundation for adaptive learning system with personalized recommendations
• Risk evaluation: Medium risk due to critical issues in performance calculation and mastery logic
• Notable positive aspects: Comprehensive test coverage, good documentation, privacy-conscious design
• Implementation quality: Generally high code quality with appropriate use of modern Python features
• Final recommendation: Request Changes - must address P0 issues before merging

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