PYQ Spaced Repetition System - Scientific Memory Optimization

# Simplified SM-2 Algorithm Implementation
class SpacedRepetitionCard:
    def __init__(self, question_id, subject, topic, difficulty):
        self.question_id = question_id
        self.subject = subject
        self.topic = topic
        self.difficulty = difficulty
        self.ease_factor = 2.5  # Initial ease factor
        self.repetition_interval = 1  # Days
        self.repetition_count = 0
        self.last_review_date = None
        self.next_review_date = datetime.now() + timedelta(days=1)
        self.quality_scores = []  # Track performance quality
        self.mastery_level = 0  # 0-5 mastery scale
        self.consecutive_correct = 0
        self.consecutive_incorrect = 0

    def calculate_next_review(self, quality_score):
        """
        Calculate next review date based on SM-2 algorithm
        Quality score: 0-5 (0=total blackout, 5=perfect response)
        """
        self.quality_scores.append(quality_score)

        if quality_score < 3:  # Failed review
            self.repetition_count = 0
            self.repetition_interval = 1
            self.consecutive_incorrect += 1
            self.consecutive_correct = 0
        else:  # Successful review
            if self.repetition_count == 0:
                self.repetition_interval = 1
            elif self.repetition_count == 1:
                self.repetition_interval = 6
            else:
                self.repetition_interval = int(self.repetition_interval * self.ease_factor)

            self.repetition_count += 1
            self.consecutive_correct += 1
            self.consecutive_incorrect = 0

        # Update ease factor
        self.ease_factor = max(1.3,
            self.ease_factor + (0.1 - (5 - quality_score) * (0.08 + (5 - quality_score) * 0.02))
        )

        # Apply difficulty-based adjustments
        if self.difficulty == "Very Hard":
            self.repetition_interval = max(1, int(self.repetition_interval * 0.7))
        elif self.difficulty == "Hard":
            self.repetition_interval = max(1, int(self.repetition_interval * 0.85))
        elif self.difficulty == "Easy":
            self.repetition_interval = int(self.repetition_interval * 1.2)

        # Update mastery level
        self.update_mastery_level()

        # Calculate next review date
        self.next_review_date = datetime.now() + timedelta(days=self.repetition_interval)
        self.last_review_date = datetime.now()

        return self.next_review_date

    def update_mastery_level(self):
        """Update mastery level based on performance patterns"""
        if self.consecutive_correct >= 8:
            self.mastery_level = 5  # Mastered
        elif self.consecutive_correct >= 6:
            self.mastery_level = 4  # Advanced
        elif self.consecutive_correct >= 4:
            self.mastery_level = 3  # Proficient
        elif self.consecutive_correct >= 2:
            self.mastery_level = 2  # Developing
        elif self.repetition_count >= 1:
            self.mastery_level = 1  # Familiar
        else:
            self.mastery_level = 0  # New

class ReviewQueue:
    def __init__(self):
        self.cards = {}  # Dictionary of all cards
        self.due_cards = []  # Cards due for review
        self.new_cards = []  # New cards to learn
        self.learning_cards = []  # Cards in learning phase
        self.daily_reviews = 0
        self.daily_new = 0
        self.settings = {
            'max_daily_reviews': 100,
            'max_daily_new': 20,
            'review_order': 'priority',  # priority, random, difficulty
            'easy_bonus': 1.3,
            'hard_interval_factor': 0.8
        }

    def update_due_cards(self):
        """Update the list of cards due for review"""
        current_date = datetime.now()
        self.due_cards = [
            card for card in self.cards.values()
            if card.next_review_date <= current_date
        ]

    def get_review_schedule(self, days_ahead=30):
        """Get review schedule for the next N days"""
        schedule = {}
        current_date = datetime.now()

        for day in range(days_ahead):
            target_date = current_date + timedelta(days=day)
            cards_due = [
                card for card in self.cards.values()
                if card.next_review_date.date() == target_date.date()
            ]

            schedule[day] = {
                'date': target_date,
                'count': len(cards_due),
                'subjects': self._group_by_subject(cards_due),
                'difficulties': self._group_by_difficulty(cards_due),
                'estimated_time': self._estimate_review_time(cards_due)
            }

        return schedule

    def optimize_daily_load(self):
        """Optimize daily review load for better retention"""
        if len(self.due_cards) > self.settings['max_daily_reviews']:
            # Prioritize by urgency and importance
            self.due_cards.sort(key=lambda x: (
                x.repetition_interval,  # Shorter intervals first
                x.mastery_level,        # Lower mastery first
                self._difficulty_weight(x.difficulty)  # Harder first
            ))

            # Limit to maximum daily reviews
            self.due_cards = self.due_cards[:self.settings['max_daily_reviews']]

def calculate_card_priority(self, card):
    """Calculate review priority based on multiple factors"""
    priority = 0

    # Urgency factor (overdue cards get higher priority)
    if card.next_review_date < datetime.now():
        days_overdue = (datetime.now() - card.next_review_date).days
        priority += days_overdue * 10

    # Mastery level factor
    priority += (5 - card.mastery_level) * 5

    # Difficulty factor
    difficulty_weights = {"Very Hard": 4, "Hard": 3, "Medium": 2, "Easy": 1}
    priority += difficulty_weights.get(card.difficulty, 2) * 2

    # Subject importance factor (based on exam weightage)
    subject_weights = {
        "Physics": 0.33, "Chemistry": 0.33, "Mathematics": 0.34,
        "Biology": 0.50, "Botany": 0.25, "Zoology": 0.25
    }
    priority += subject_weights.get(card.subject, 0.3) * 10

    return priority

class RetentionAnalytics:
    def __init__(self):
        self.performance_data = {}
        self.retention_rates = {}
        self.forgetting_curves = {}
        self.subject_performance = {}
        self.difficulty_performance = {}
        self.time_performance = {}

    def calculate_retention_rate(self, days_since_review, subject=None):
        """Calculate retention rate based on time since last review"""
        if subject:
            relevant_cards = [c for c in self.cards.values() if c.subject == subject]
        else:
            relevant_cards = list(self.cards.values())

        retention_data = []
        for card in relevant_cards:
            days_elapsed = (datetime.now() - card.last_review_date).days
            if days_elapsed == days_since_review:
                success_rate = self._calculate_card_success_rate(card)
                retention_data.append(success_rate)

        return sum(retention_data) / len(retention_data) if retention_data else 0

    def generate_forgetting_curve(self, card_id):
        """Generate forgetting curve for a specific card"""
        card = self.cards.get(card_id)
        if not card:
            return None

        # Simulate forgetting curve based on SM-2 parameters
        time_points = [0, 1, 3, 7, 14, 30, 60, 120]
        retention_rates = []

        for days in time_points:
            # Ebbinghaus forgetting curve formula
            R = e^(-t/S) where R = retention, t = time, S = strength

            # Card strength based on ease factor and repetitions
            strength = card.ease_factor * (1 + card.repetition_count * 0.1)
            retention = math.exp(-days / strength)

            # Adjust for difficulty
            difficulty_modifier = {
                "Very Hard": 0.7, "Hard": 0.8, "Medium": 0.9, "Easy": 1.1
            }.get(card.difficulty, 0.9)

            retention *= difficulty_modifier
            retention_rates.append(max(0, min(1, retention)))

        return {
            'time_points': time_points,
            'retention_rates': retention_rates,
            'card_id': card_id,
            'subject': card.subject,
            'topic': card.topic
        }

def assess_memory_strength(self, card):
    """Comprehensive memory strength assessment"""

    # Base strength from repetition count and ease factor
    base_strength = card.ease_factor * (1 + card.repetition_count * 0.15)

    # Performance consistency factor
    if len(card.quality_scores) >= 3:
        recent_scores = card.quality_scores[-3:]
        consistency = 1 - (max(recent_scores) - min(recent_scores)) / 5
        base_strength *= (0.8 + 0.4 * consistency)

    # Recency factor
    days_since_review = (datetime.now() - card.last_review_date).days
    recency_factor = math.exp(-days_since_review / (card.repetition_interval * 2))
    base_strength *= recency_factor

    # Subject mastery bonus
    subject_mastery = self._calculate_subject_mastery(card.subject)
    base_strength *= (0.9 + 0.2 * subject_mastery)

    # Normalize to 0-100 scale
    memory_strength = min(100, max(0, base_strength * 20))

    return {
        'memory_strength': memory_strength,
        'confidence_level': self._get_confidence_level(memory_strength),
        'retention_probability': memory_strength / 100,
        'review_urgency': self._calculate_review_urgency(memory_strength),
        'optimal_review_time': self._suggest_optimal_review_time(card, memory_strength)
    }

class MasteryProgression:
    MASTERY_LEVELS = {
        0: {
            'name': 'New',
            'description': 'First time encountering this question',
            'target_accuracy': 'N/A',
            'review_frequency': 'Daily until first successful review',
            'confidence_threshold': 0,
            'color': '#94a3b8'
        },
        1: {
            'name': 'Familiar',
            'description': 'Can solve with some guidance or hints',
            'target_accuracy': '60%',
            'review_frequency': '1-3 days',
            'confidence_threshold': 20,
            'color': '#fbbf24'
        },
        2: {
            'name': 'Developing',
            'description': 'Can solve independently with moderate confidence',
            'target_accuracy': '75%',
            'review_frequency': '4-7 days',
            'confidence_threshold': 40,
            'color': '#60a5fa'
        },
        3: {
            'name': 'Proficient',
            'description': 'Can solve quickly and accurately',
            'target_accuracy': '85%',
            'review_frequency': '1-2 weeks',
            'confidence_threshold': 60,
            'color': '#34d399'
        },
        4: {
            'name': 'Advanced',
            'description': 'Can solve variations and teach others',
            'target_accuracy': '95%',
            'review_frequency': '3-4 weeks',
            'confidence_threshold': 80,
            'color': '#a78bfa'
        },
        5: {
            'name': 'Mastered',
            'description': 'Complete mastery, rare review needed',
            'target_accuracy': '98%+',
            'review_frequency': '2-3 months',
            'confidence_threshold': 95,
            'color': '#f472b6'
        }
    }

    def evaluate_mastery_progress(self, card):
        """Evaluate and update mastery level"""
        mastery_data = {
            'current_level': card.mastery_level,
            'progress_to_next': self._calculate_progress_to_next_level(card),
            'total_correct': sum(1 for score in card.quality_scores if score >= 3),
            'total_attempts': len(card.quality_scores),
            'accuracy_rate': self._calculate_accuracy_rate(card),
            'improvement_trend': self._calculate_improvement_trend(card),
            'consistency_score': self._calculate_consistency_score(card),
            'speed_performance': self._calculate_speed_performance(card)
        }

        # Check for mastery level advancement
        new_level = self._determine_mastery_level(mastery_data)
        if new_level != card.mastery_level:
            card.mastery_level = new_level
            mastery_data['level_advanced'] = True
            mastery_data['previous_level'] = mastery_data['current_level']

        return mastery_data

def generate_mastery_dashboard(self, user_id):
    """Generate comprehensive mastery dashboard"""

    dashboard_data = {
        'overall_mastery': {
            'total_cards': len(self.cards),
            'mastered_cards': len([c for c in self.cards.values() if c.mastery_level == 5]),
            'advanced_cards': len([c for c in self.cards.values() if c.mastery_level == 4]),
            'proficient_cards': len([c for c in self.cards.values() if c.mastery_level == 3]),
            'developing_cards': len([c for c in self.cards.values() if c.mastery_level == 2]),
            'familiar_cards': len([c for c in self.cards.values() if c.mastery_level == 1]),
            'new_cards': len([c for c in self.cards.values() if c.mastery_level == 0]),
        },
        'subject_mastery': self._calculate_subject_mastery_breakdown(),
        'topic_mastery': self._calculate_topic_mastery_breakdown(),
        'difficulty_mastery': self._calculate_difficulty_mastery_breakdown(),
        'learning_velocity': self._calculate_learning_velocity(),
        'retention_trends': self._calculate_retention_trends(),
        'improvement_areas': self._identify_improvement_areas(),
        'achievement_milestones': self._track_achievement_milestones()
    }

    return dashboard_data

class ForgettingCurveVisualizer:
    def __init__(self):
        self.curves = {}
        self.comparison_data = {}

    def generate_individual_curve(self, card):
        """Generate forgetting curve for individual card"""
        # Mathematical model of forgetting curve
        # R(t) = e^(-t/S) where R is retention, t is time, S is strength

        time_points = list(range(0, 180, 1))  # 180 days
        retention_rates = []

        # Calculate base strength from card parameters
        strength = card.ease_factor * (1 + card.repetition_count * 0.2)

        for days in time_points:
            # Base forgetting curve
            base_retention = math.exp(-days / strength)

            # Apply subject-specific modifiers
            subject_modifier = self._get_subject_modifier(card.subject)

            # Apply difficulty modifiers
            difficulty_modifier = self._get_difficulty_modifier(card.difficulty)

            # Apply individual learning factors
            learning_modifier = self._get_learning_modifier(card)

            # Combined retention rate
            retention = base_retention * subject_modifier * difficulty_modifier * learning_modifier
            retention_rates.append(max(0, min(1, retention)))

        return {
            'time_points': time_points,
            'retention_rates': retention_rates,
            'optimal_review_points': self._find_optimal_review_points(retention_rates),
            'critical_forgetting_points': self._find_critical_forgetting_points(retention_rates),
            'strength_score': strength,
            'confidence_intervals': self._calculate_confidence_intervals(retention_rates)
        }

    def compare_performance_patterns(self, user_id):
        """Compare performance patterns across subjects and difficulty levels"""
        comparison_data = {
            'subject_curves': {},
            'difficulty_curves': {},
            'improvement_over_time': {},
            'retention_by_study_time': {},
            'optimal_spacing_patterns': {}
        }

        # Generate curves for each subject
        for subject in ['Physics', 'Chemistry', 'Mathematics', 'Biology']:
            subject_cards = [c for c in self.cards.values() if c.subject == subject]
            if subject_cards:
                comparison_data['subject_curves'][subject] = self._generate_average_curve(subject_cards)

        # Generate curves for each difficulty level
        for difficulty in ['Easy', 'Medium', 'Hard', 'Very Hard']:
            diff_cards = [c for c in self.cards.values() if c.difficulty == difficulty]
            if diff_cards:
                comparison_data['difficulty_curves'][difficulty] = self._generate_average_curve(diff_cards)

        return comparison_data

def calculate_optimal_review_timing(self, card):
    """Calculate optimal review timing for maximum retention"""

    # Generate predicted forgetting curve
    curve_data = self.generate_individual_curve(card)
    retention_rates = curve_data['retention_rates']
    time_points = curve_data['time_points']

    # Find optimal review points (when retention drops to ~80%)
    optimal_points = []
    for i, retention in enumerate(retention_rates):
        if retention <= 0.8 and (i == 0 or retention_rates[i-1] > 0.8):
            optimal_points.append(time_points[i])

    # Schedule reviews at optimal points
    review_schedule = []
    for point in optimal_points[:5]:  # Limit to first 5 optimal points
        review_date = datetime.now() + timedelta(days=point)
        review_schedule.append({
            'review_date': review_date,
            'days_from_now': point,
            'expected_retention': retention_rates[point] * 100,
            'review_priority': self._calculate_review_priority(card, point),
            'estimated_success_rate': self._estimate_success_rate(card, point)
        })

    return {
        'card_info': {
            'id': card.question_id,
            'subject': card.subject,
            'topic': card.topic,
            'difficulty': card.difficulty,
            'current_mastery': card.mastery_level
        },
        'optimal_schedule': review_schedule,
        'forgetting_curve': curve_data,
        'retention_predictions': self._generate_retention_predictions(card),
        'study_recommendations': self._generate_study_recommendations(card)
    }

class IntervalOptimizer:
    def __init__(self):
        self.learning_patterns = {}
        self.performance_history = {}
        self.optimal_intervals = {}

    def optimize_intervals_ml(self, user_id, card):
        """Machine learning-based interval optimization"""

        # Collect user's historical performance data
        user_history = self.performance_history.get(user_id, {})

        # Extract features for ML model
        features = {
            'card_difficulty': self._encode_difficulty(card.difficulty),
            'subject': self._encode_subject(card.subject),
            'current_interval': card.repetition_interval,
            'ease_factor': card.ease_factor,
            'repetition_count': card.repetition_count,
            'recent_performance': self._get_recent_performance(card),
            'subject_strength': self._get_subject_strength(user_id, card.subject),
            'difficulty_preference': self._get_difficulty_preference(user_id),
            'time_of_day_performance': self._get_time_performance(user_id),
            'study_session_length': self._get_optimal_session_length(user_id),
            'learning_velocity': self._get_learning_velocity(user_id)
        }

        # Apply trained ML model for optimal interval
        optimal_interval = self._ml_predict_interval(features)

        # Apply constraints and safety limits
        optimal_interval = max(1, min(365, optimal_interval))  # 1 day to 1 year

        # Apply personalized adjustments
        optimal_interval *= self._get_personalized_multiplier(user_id, card)

        return {
            'optimal_interval': int(optimal_interval),
            'confidence_score': self._calculate_prediction_confidence(features),
            'adjustment_factors': self._get_adjustment_factors(features),
            'risk_assessment': self._assess_interval_risk(card, optimal_interval),
            'alternative_intervals': self._generate_alternative_intervals(optimal_interval)
        }

    def adaptive_interval_adjustment(self, card, performance_feedback):
        """Adaptively adjust intervals based on performance feedback"""

        # Performance feedback analysis
        quality_score = performance_feedback['quality']
        time_taken = performance_feedback['time_taken']
        confidence_level = performance_feedback['confidence']
        hints_used = performance_feedback.get('hints_used', 0)

        # Calculate adjustment factors
        adjustment_factors = {
            'quality_factor': self._calculate_quality_factor(quality_score),
            'time_factor': self._calculate_time_factor(time_taken, card.difficulty),
            'confidence_factor': self._calculate_confidence_factor(confidence_level),
            'hints_penalty': self._calculate_hints_penalty(hints_used),
            'difficulty_adjustment': self._get_difficulty_adjustment(card.difficulty)
        }

        # Combined adjustment multiplier
        total_adjustment = 1.0
        for factor in adjustment_factors.values():
            total_adjustment *= factor

        # Apply adjustment to current interval
        new_interval = int(card.repetition_interval * total_adjustment)

        # Apply bounds and safety checks
        new_interval = max(1, min(365, new_interval))

        # Update card parameters
        card.repetition_interval = new_interval
        card.ease_factor = max(1.3, card.ease_factor * (0.9 + 0.2 * total_adjustment))

        return {
            'new_interval': new_interval,
            'adjustment_factors': adjustment_factors,
            'interval_change': new_interval - card.repetition_interval,
            'reasoning': self._generate_adjustment_reasoning(adjustment_factors),
            'next_review': datetime.now() + timedelta(days=new_interval)
        }

def optimize_by_subject_patterns(self, subject, user_id):
    """Optimize intervals based on subject-specific learning patterns"""

    subject_patterns = {
        'Physics': {
            'concepts_build_on_each_other': True,
            'mathematical_rigor': 'High',
            'visualization_importance': 'High',
            'problem_solving_pattern': 'Step-by-step',
            'optimal_review_spacing': [1, 3, 7, 14, 30, 60],
            'difficulty_multiplier': 1.1,
            'conceptual_weight': 0.7
        },
        'Chemistry': {
            'concepts_build_on_each_other': True,
            'mathematical_rigor': 'Medium',
            'visualization_importance': 'Medium',
            'problem_solving_pattern': 'Pattern recognition',
            'optimal_review_spacing': [1, 2, 5, 10, 20, 45],
            'difficulty_multiplier': 0.95,
            'conceptual_weight': 0.6
        },
        'Mathematics': {
            'concepts_build_on_each_other': True,
            'mathematical_rigor': 'Very High',
            'visualization_importance': 'Medium',
            'problem_solving_pattern': 'Logical deduction',
            'optimal_review_spacing': [1, 4, 10, 20, 45, 90],
            'difficulty_multiplier': 1.15,
            'conceptual_weight': 0.8
        },
        'Biology': {
            'concepts_build_on_each_other': False,
            'mathematical_rigor': 'Low',
            'visualization_importance': 'High',
            'problem_solving_pattern': 'Memorization + application',
            'optimal_review_spacing': [1, 2, 4, 8, 16, 32],
            'difficulty_multiplier': 0.9,
            'conceptual_weight': 0.5
        }
    }

    patterns = subject_patterns.get(subject, subject_patterns['Chemistry'])

    # Apply subject-specific optimizations to user's cards
    subject_cards = [c for c in self.cards.values() if c.subject == subject]
    optimized_cards = []

    for card in subject_cards:
        optimized_interval = self._apply_subject_optimization(card, patterns)
        optimized_cards.append({
            'card_id': card.question_id,
            'original_interval': card.repetition_interval,
            'optimized_interval': optimized_interval,
            'optimization_reason': self._get_optimization_reason(card, patterns)
        })

    return {
        'subject': subject,
        'total_cards_optimized': len(optimized_cards),
        'optimization_results': optimized_cards,
        'subject_patterns': patterns,
        'recommendations': self._generate_subject_recommendations(subject, patterns)
    }

class SmartScheduleGenerator:
    def __init__(self):
        self.user_preferences = {}
        self.study_patterns = {}
        self.performance_data = {}

    def generate_daily_schedule(self, user_id, target_date=None):
        """Generate optimized daily review schedule"""

        if target_date is None:
            target_date = datetime.now()

        # Get user preferences and constraints
        preferences = self.user_preferences.get(user_id, {
            'study_time_available': 120,  # minutes
            'preferred_study_times': ['morning', 'evening'],
            'max_session_length': 45,  # minutes
            'break_frequency': 20,  # minutes between breaks
            'difficulty_preference': 'mixed',
            'subject_focus': 'balanced'
        })

        # Get cards due for review
        due_cards = self._get_due_cards(user_id, target_date)

        # Prioritize cards based on urgency and importance
        prioritized_cards = self._prioritize_cards(due_cards, user_id)

        # Create study sessions
        study_sessions = self._create_study_sessions(
            prioritized_cards,
            preferences,
            target_date
        )

        # Optimize session timing
        optimized_schedule = self._optimize_session_timing(study_sessions, preferences)

        return {
            'date': target_date,
            'total_cards_due': len(due_cards),
            'scheduled_cards': len([c for s in optimized_schedule for c in s['cards']]),
            'study_sessions': optimized_schedule,
            'estimated_time': sum(s['duration'] for s in optimized_schedule),
            'difficulty_distribution': self._calculate_difficulty_distribution(optimized_schedule),
            'subject_distribution': self._calculate_subject_distribution(optimized_schedule),
            'recommendations': self._generate_schedule_recommendations(optimized_schedule)
        }

    def create_weekly_plan(self, user_id, start_date=None):
        """Create comprehensive weekly study plan"""

        if start_date is None:
            start_date = datetime.now()

        weekly_plan = {
            'week_start': start_date,
            'daily_plans': {},
            'weekly_goals': self._set_weekly_goals(user_id),
            'focus_areas': self._identify_focus_areas(user_id),
            'progress_tracking': {}
        }

        # Generate daily schedules for the week
        for day in range(7):
            current_date = start_date + timedelta(days=day)
            daily_plan = self.generate_daily_schedule(user_id, current_date)
            weekly_plan['daily_plans'][day] = daily_plan

        # Optimize weekly distribution
        optimized_week = self._optimize_weekly_distribution(weekly_plan)

        # Set weekly milestones
        milestones = self._set_weekly_milestones(user_id, optimized_week)

        return {
            **optimized_week,
            'milestones': milestones,
            'flexibility_options': self._generate_flexibility_options(optimized_week),
            'progress_indicators': self._create_progress_indicators(optimized_week)
        }

def create_adaptive_schedule(self, user_id, learning_goals):
    """Create schedule that adapts to learning goals and performance"""

    # Analyze learning goals
    goal_analysis = self._analyze_learning_goals(learning_goals)

    # Assess current performance level
    performance_assessment = self._assess_current_performance(user_id)

    # Calculate optimal learning path
    learning_path = self._calculate_learning_path(
        goal_analysis,
        performance_assessment
    )

    # Generate adaptive schedule
    adaptive_schedule = {
        'learning_path': learning_path,
        'milestone_schedule': self._create_milestone_schedule(learning_path),
        'flexible_reviews': self._create_flexible_review_system(learning_path),
        'performance_checkpoints': self._set_performance_checkpoints(learning_path),
        'adjustment_triggers': self._define_adjustment_triggers()
    }

    # Implement real-time adaptation
    adaptation_system = {
        'performance_monitoring': self._setup_performance_monitoring(),
        'schedule_adjustment': self._setup_schedule_adjustment(),
        'difficulty_scaling': self._setup_difficulty_scaling(),
        'goal_progression': self._setup_goal_progression()
    }

    return {
        'schedule': adaptive_schedule,
        'adaptation_system': adaptation_system,
        'success_metrics': self._define_success_metrics(learning_goals),
        'optimization_strategies': self._define_optimization_strategies()
    }

class PerformanceTracker:
    def __init__(self):
        self.user_data = {}
        self.analytics_cache = {}
        self.benchmark_data = {}

    def generate_performance_dashboard(self, user_id, time_period=30):
        """Generate comprehensive performance dashboard"""

        end_date = datetime.now()
        start_date = end_date - timedelta(days=time_period)

        dashboard_data = {
            'overview_metrics': self._calculate_overview_metrics(user_id, start_date, end_date),
            'retention_analysis': self._analyze_retention_patterns(user_id, start_date, end_date),
            'mastery_progression': self._track_mastery_progression(user_id, start_date, end_date),
            'subject_performance': self._analyze_subject_performance(user_id, start_date, end_date),
            'difficulty_performance': self._analyze_difficulty_performance(user_id, start_date, end_date),
            'learning_efficiency': self._calculate_learning_efficiency(user_id, start_date, end_date),
            'study_patterns': self._analyze_study_patterns(user_id, start_date, end_date),
            'improvement_areas': self._identify_improvement_areas(user_id),
            'achievement_tracking': self._track_achievements(user_id, start_date, end_date),
            'predictive_analytics': self._generate_predictive_analytics(user_id)
        }

        # Add benchmark comparisons
        dashboard_data['benchmark_comparisons'] = self._add_benchmark_comparisons(
            dashboard_data, user_id
        )

        # Generate recommendations
        dashboard_data['recommendations'] = self._generate_performance_recommendations(
            dashboard_data
        )

        return dashboard_data

    def track_learning_velocity(self, user_id):
        """Track how quickly user is learning and mastering concepts"""

        # Calculate learning velocity metrics
        velocity_metrics = {
            'cards_per_day': self._calculate_cards_per_day(user_id),
            'mastery_gain_per_week': self._calculate_mastery_gain(user_id),
            'retention_improvement_rate': self._calculate_retention_improvement(user_id),
            'efficiency_score': self._calculate_efficiency_score(user_id),
            'consistency_score': self._calculate_consistency_score(user_id),
            'improvement_trend': self._calculate_improvement_trend(user_id)
        }

        # Compare with historical data
        historical_comparison = self._compare_with_historical(user_id, velocity_metrics)

        # Predict future performance
        future_predictions = self._predict_future_performance(user_id, velocity_metrics)

        return {
            'current_velocity': velocity_metrics,
            'historical_comparison': historical_comparison,
            'future_predictions': future_predictions,
            'optimization_suggestions': self._generate_velocity_optimization_suggestions(
                velocity_metrics
            )
        }

def track_memory_consolidation(self, user_id, card_id):
    """Track memory consolidation process for specific cards"""

    card = self._get_card(card_id)
    if not card:
        return None

    # Get card's review history
    review_history = self._get_card_review_history(user_id, card_id)

    # Analyze consolidation patterns
    consolidation_analysis = {
        'initial_learning_phase': self._analyze_initial_learning(review_history),
        'consolidation_phase': self._analyze_consolidation(review_history),
        'maintenance_phase': self._analyze_maintenance(review_history),
        'mastery_achievement': self._analyze_mastery_achievement(review_history)
    }

    # Calculate consolidation strength
    consolidation_strength = self._calculate_consolidation_strength(
        card, review_history
    )

    # Identify consolidation factors
    consolidation_factors = {
        'spacing_effect': self._measure_spacing_effect(review_history),
        'testing_effect': self._measure_testing_effect(review_history),
        'desirable_difficulty': self._measure_desirable_difficulty(review_history),
        'interference_effects': self._measure_interference_effects(card),
        'context_effects': self._measure_context_effects(review_history)
    }

    # Generate consolidation recommendations
    recommendations = self._generate_consolidation_recommendations(
        consolidation_analysis, consolidation_factors
    )

    return {
        'card_info': {
            'id': card_id,
            'subject': card.subject,
            'topic': card.topic,
            'difficulty': card.difficulty,
            'current_mastery': card.mastery_level
        },
        'consolidation_analysis': consolidation_analysis,
        'consolidation_strength': consolidation_strength,
        'consolidation_factors': consolidation_factors,
        'recommendations': recommendations,
        'next_steps': self._suggest_consolidation_next_steps(card, consolidation_strength)
    }

class PYQIntegration:
    def __init__(self):
        self.pyq_database = PYQDatabase()
        self.spaced_repetition = SpacedRepetitionSystem()
        self.sync_manager = SyncManager()

    def integrate_pyq_with_spaced_repetition(self, user_id, filters=None):
        """Integrate PYQ database with spaced repetition system"""

        # Get PYQs based on filters
        if filters is None:
            filters = {
                'subjects': ['Physics', 'Chemistry', 'Mathematics', 'Biology'],
                'years': range(2009, 2025),
                'difficulties': ['Easy', 'Medium', 'Hard', 'Very Hard'],
                'topics': None,  # All topics
                'concepts': None  # All concepts
            }

        pyq_data = self.pyq_database.get_filtered_questions(filters)

        # Convert PYQs to spaced repetition cards
        spaced_repetition_cards = []

        for pyq in pyq_data:
            card = self._create_spaced_repetition_card(pyq)

            # Apply intelligent difficulty assessment
            assessed_difficulty = self._assess_question_difficulty(pyq)
            card.difficulty = assessed_difficulty

            # Add concept tags
            concept_tags = self._extract_concept_tags(pyq)
            card.concept_tags = concept_tags

            # Calculate initial ease factor based on question properties
            card.ease_factor = self._calculate_initial_ease_factor(pyq, assessed_difficulty)

            spaced_repetition_cards.append(card)

        # Batch insert into spaced repetition system
        insertion_result = self.spaced_repetition.batch_add_cards(
            user_id, spaced_repetition_cards
        )

        return {
            'total_questions_processed': len(pyq_data),
            'cards_created': insertion_result['cards_created'],
            'duplicates_skipped': insertion_result['duplicates'],
            'processing_time': insertion_result['processing_time'],
            'integration_summary': self._generate_integration_summary(
                pyq_data, spaced_repetition_cards
            )
        }

    def sync_user_progress(self, user_id):
        """Synchronize user progress across PYQ and spaced repetition systems"""

        # Get progress from both systems
        pyq_progress = self.pyq_database.get_user_progress(user_id)
        sr_progress = self.spaced_repetition.get_user_progress(user_id)

        # Identify discrepancies and sync data
        sync_operations = {
            'cards_to_update': [],
            'progress_to_merge': [],
            'conflicts_to_resolve': []
        }

        # Analyze and resolve conflicts
        for card_id in set(pyq_progress.keys()) | set(sr_progress.keys()):
            pyq_data = pyq_progress.get(card_id, {})
            sr_data = sr_progress.get(card_id, {})

            if pyq_data and sr_data:
                # Both systems have data - merge intelligently
                merged_data = self._merge_progress_data(pyq_data, sr_data)
                sync_operations['progress_to_merge'].append({
                    'card_id': card_id,
                    'merged_data': merged_data
                })
            elif pyq_data:
                # Only PYQ has data - create in SR system
                sync_operations['cards_to_update'].append({
                    'card_id': card_id,
                    'source': 'pyq',
                    'data': pyq_data
                })
            elif sr_data:
                # Only SR has data - update PYQ system
                sync_operations['cards_to_update'].append({
                    'card_id': card_id,
                    'source': 'sr',
                    'data': sr_data
                })

        # Execute sync operations
        sync_results = self._execute_sync_operations(sync_operations, user_id)

        return sync_results

def integrate_analytics_data(self, user_id):
    """Integrate analytics data from both systems"""

    # Get analytics from PYQ system
    pyq_analytics = self.pyq_database.get_user_analytics(user_id)

    # Get analytics from spaced repetition system
    sr_analytics = self.spaced_repetition.get_user_analytics(user_id)

    # Create unified analytics view
    unified_analytics = {
        'overall_performance': self._unify_performance_data(
            pyq_analytics, sr_analytics
        ),
        'subject_mastery': self._unify_subject_mastery(
            pyq_analytics, sr_analytics
        ),
        'learning_patterns': self._unify_learning_patterns(
            pyq_analytics, sr_analytics
        ),
        'retention_analysis': self._unify_retention_analysis(
            pyq_analytics, sr_analytics
        ),
        'improvement_trends': self._unify_improvement_trends(
            pyq_analytics, sr_analytics
        ),
        'predictive_insights': self._generate_unified_predictive_insights(
            pyq_analytics, sr_analytics
        )
    }

    # Generate cross-system insights
    cross_system_insights = self._generate_cross_system_insights(
        pyq_analytics, sr_analytics
    )

    return {
        'unified_analytics': unified_analytics,
        'cross_system_insights': cross_system_insights,
        'integration_quality': self._assess_integration_quality(
            pyq_analytics, sr_analytics
        ),
        'recommendations': self._generate_integration_recommendations(
            unified_analytics
        )
    }

class CognitiveLoadOptimizer:
    def __init__(self):
        self.load_thresholds = {
            'intrinsic_load': 0.4,  # Inherent difficulty of content
            'extraneous_load': 0.3,  # Poor instructional design
            'germane_load': 0.3      # Schema construction and automation
        }

    def optimize_cognitive_load(self, study_session, user_profile):
        """Optimize cognitive load for effective learning"""

        # Calculate current cognitive load
        current_load = self._calculate_cognitive_load(study_session, user_profile)

        # Optimize session if load is too high or too low
        if current_load > 0.85:  # Too high - risk of cognitive overload
            optimizations = self._reduce_cognitive_load(study_session)
        elif current_load < 0.4:  # Too low - insufficient challenge
            optimizations = self._increase_cognitive_load(study_session)
        else:  # Optimal range
            optimizations = self._maintain_optimal_load(study_session)

        return {
            'current_load': current_load,
            'load_category': self._categorize_load(current_load),
            'optimizations': optimizations,
            'recommendations': self._generate_load_recommendations(current_load),
            'next_session_adjustments': self._suggest_next_adjustments(
                current_load, user_profile
            )
        }

    def _calculate_cognitive_load(self, session, profile):
        """Calculate total cognitive load for a study session"""

        # Intrinsic load based on question difficulty and complexity
        intrinsic_load = self._calculate_intrinsic_load(session['questions'])

        # Extraneous load based on interface and presentation
        extraneous_load = self._calculate_extraneous_load(session['format'])

        # Germane load based on user's prior knowledge and schema
        germane_load = self._calculate_germane_load(session['questions'], profile)

        # Total cognitive load
        total_load = intrinsic_load + extraneous_load + germane_load

        return min(1.0, total_load)  # Cap at 1.0

class MetacognitiveEnhancer:
    def __init__(self):
        self.metacognitive_strategies = {
            'planning': ['goal_setting', 'strategy_selection', 'resource_allocation'],
            'monitoring': ['comprehension_checking', 'progress_tracking', 'difficulty_assessment'],
            'evaluation': ['performance_review', 'strategy_effectiveness', 'learning_adjustment']
        }

    def enhance_learning_with_metacognition(self, user_id, study_session):
        """Enhance learning through metacognitive strategies"""

        # Pre-study metacognitive activities
        planning_phase = self._implement_planning_strategies(user_id, study_session)

        # During-study metacognitive monitoring
        monitoring_phase = self._implement_monitoring_strategies(user_id, study_session)

        # Post-study metacognitive evaluation
        evaluation_phase = self._implement_evaluation_strategies(user_id, study_session)

        # Metacognitive development tracking
        metacognitive_development = self._track_metacognitive_development(user_id)

        return {
            'planning_phase': planning_phase,
            'monitoring_phase': monitoring_phase,
            'evaluation_phase': evaluation_phase,
            'metacognitive_development': metacognitive_development,
            'next_level_strategies': self._suggest_advanced_strategies(user_id)
        }