Retention Analytics Dashboard - Comprehensive Memory Performance Tracking

class RetentionAnalytics:
    def __init__(self):
        self.metrics_calculators = {
            'retention_rate': self._calculate_retention_rate,
            'forgetting_curve': self._generate_forgetting_curve,
            'memory_strength': self._assess_memory_strength,
            'learning_velocity': self._calculate_learning_velocity,
            'review_efficiency': self._calculate_review_efficiency,
            'mastery_progression': self._track_mastery_progression
        }

    def generate_core_metrics(self, user_id, time_period=30):
        """Generate comprehensive core retention metrics"""

        # Collect performance data
        performance_data = self._collect_performance_data(user_id, time_period)

        # Calculate core metrics
        core_metrics = {
            'overall_retention_rate': self._calculate_overall_retention_rate(performance_data),
            'subject_wise_retention': self._calculate_subject_retention(performance_data),
            'difficulty_based_retention': self._calculate_difficulty_retention(performance_data),
            'time_based_retention': self._calculate_time_retention(performance_data),
            'concept_retention': self._calculate_concept_retention(performance_data)
        }

        # Calculate trend indicators
        trend_analysis = {
            'retention_trend': self._analyze_retention_trend(performance_data),
            'improvement_rate': self._calculate_improvement_rate(performance_data),
            'stability_score': self._calculate_retention_stability(performance_data),
            'plateau_detection': self._detect_learning_plateaus(performance_data)
        }

        # Generate benchmark comparisons
        benchmark_analysis = {
            'peer_comparison': self._compare_with_peers(core_metrics),
            'goal_progress': self._compare_with_goals(core_metrics, user_id),
            'historical_comparison': self._compare_with_historical(core_metrics, user_id)
        }

        return {
            'core_metrics': core_metrics,
            'trend_analysis': trend_analysis,
            'benchmark_analysis': benchmark_analysis,
            'performance_summary': self._generate_performance_summary(core_metrics, trend_analysis),
            'key_insights': self._extract_key_insights(core_metrics, trend_analysis, benchmark_analysis)
        }

    def _calculate_overall_retention_rate(self, performance_data):
        """Calculate overall retention rate across all subjects and topics"""

        retention_data = {
            'short_term_retention': {},  # 1-7 days
            'medium_term_retention': {},  # 8-30 days
            'long_term_retention': {},    # 31+ days
            'overall_average': 0
        }

        # Analyze retention by time intervals
        for card_id, card_data in performance_data.items():
            for review in card_data['reviews']:
                days_since_previous = (review['date'] - review['previous_review_date']).days

                if days_since_previous <= 7:
                    retention_category = 'short_term_retention'
                elif days_since_previous <= 30:
                    retention_category = 'medium_term_retention'
                else:
                    retention_category = 'long_term_retention'

                if retention_category not in retention_data[retention_category]:
                    retention_data[retention_category][retention_category] = []

                # Calculate retention for this review
                retention_score = review['quality_score'] / 5.0  # Normalize to 0-1
                retention_data[retention_category][retention_category].append(retention_score)

        # Calculate averages for each category
        for category in ['short_term_retention', 'medium_term_retention', 'long_term_retention']:
            if retention_data[category][category]:
                retention_data[category] = sum(retention_data[category][category]) / len(retention_data[category][category])
            else:
                retention_data[category] = 0

        # Calculate overall average
        all_categories = [
            retention_data['short_term_retention'],
            retention_data['medium_term_retention'],
            retention_data['long_term_retention']
        ]
        non_zero_categories = [cat for cat in all_categories if cat > 0]

        if non_zero_categories:
            retention_data['overall_average'] = sum(non_zero_categories) / len(non_zero_categories)
        else:
            retention_data['overall_average'] = 0

        return {
            'data': retention_data,
            'interpretation': self._interpret_retention_performance(retention_data),
            'recommendations': self._generate_retention_recommendations(retention_data)
        }

    def _generate_forgetting_curve(self, user_id, card_ids=None):
        """Generate personalized forgetting curves for cards or subjects"""

        if card_ids is None:
            # Generate forgetting curves for all cards
            card_ids = self._get_user_card_ids(user_id)

        forgetting_curves = {}

        for card_id in card_ids:
            curve_data = self._generate_individual_forgetting_curve(card_id)
            forgetting_curves[card_id] = curve_data

        # Generate aggregate curves by subject
        subject_curves = self._generate_aggregate_forgetting_curves(forgetting_curves, user_id)

        # Generate difficulty-based curves
        difficulty_curves = self._generate_difficulty_forgetting_curves(forgetting_curves)

        return {
            'individual_curves': forgetting_curves,
            'subject_curves': subject_curves,
            'difficulty_curves': difficulty_curves,
            'optimal_review_points': self._identify_optimal_review_points(forgetting_curves),
            'retention_predictions': self._generate_retention_predictions(forgetting_curves)
        }

    def _generate_individual_forgetting_curve(self, card_id):
        """Generate forgetting curve for individual card using SM-2 parameters"""

        # Get card data and review history
        card_data = self._get_card_data(card_id)
        review_history = card_data['review_history']

        if not review_history:
            return None

        # Calculate forgetting curve parameters
        ease_factor = card_data['ease_factor']
        repetition_count = card_data['repetition_count']
        current_interval = card_data['repetition_interval']

        # Generate curve points for next 180 days
        time_points = list(range(0, 181, 1))  # Daily points for 6 months
        retention_rates = []

        for days in time_points:
            # Modified Ebbinghaus forgetting curve with SM-2 adjustments
            base_retention = math.exp(-days / (ease_factor * 10))  # Base forgetting

            # Apply repetition strength modifier
            repetition_modifier = 1 + (repetition_count * 0.1)

            # Apply current interval modifier
            interval_modifier = min(2.0, 1 + (current_interval / 100))

            # Combined retention rate
            retention = base_retention * repetition_modifier * interval_modifier
            retention = max(0, min(1, retention))  # Clamp between 0 and 1

            retention_rates.append(retention)

        # Find critical points on the curve
        critical_points = self._find_critical_forgetting_points(time_points, retention_rates)

        return {
            'card_id': card_id,
            'card_info': {
                'subject': card_data['subject'],
                'topic': card_data['topic'],
                'difficulty': card_data['difficulty']
            },
            'time_points': time_points,
            'retention_rates': retention_rates,
            'critical_points': critical_points,
            'curve_parameters': {
                'ease_factor': ease_factor,
                'repetition_count': repetition_count,
                'current_interval': current_interval
            },
            'optimal_review_times': self._calculate_optimal_review_times(retention_rates, time_points)
        }

class MemoryStrengthAnalyzer:
    def __init__(self):
        self.strength_indicators = {
            'recall_consistency': self._assess_recall_consistency,
            'response_time': self._analyze_response_time_patterns,
            'error_patterns': self._analyze_error_patterns,
            'confidence_levels': self._assess_confidence_levels,
            'interference_resistance': self._assess_interference_resistance,
            'retrieval_speed': self._measure_retrieval_speed
        }

    def comprehensive_memory_assessment(self, user_id, assessment_scope='all'):
        """Comprehensive assessment of memory strength across all cards"""

        # Get card data for assessment
        cards_data = self._get_cards_for_assessment(user_id, assessment_scope)

        memory_assessments = {}

        for card_id, card_data in cards_data.items():
            # Assess individual memory strength
            card_assessment = self._assess_individual_memory_strength(card_data)
            memory_assessments[card_id] = card_assessment

        # Generate aggregate assessments
        aggregate_assessments = {
            'overall_strength': self._calculate_overall_memory_strength(memory_assessments),
            'subject_wise_strength': self._calculate_subject_memory_strength(memory_assessments),
            'difficulty_based_strength': self._calculate_difficulty_memory_strength(memory_assessments),
            'strength_distribution': self._analyze_strength_distribution(memory_assessments)
        }

        # Identify strength patterns
        strength_patterns = {
            'strong_areas': self._identify_strength_areas(memory_assessments),
            'weak_areas': self._identify_weak_areas(memory_assessments),
            'improving_areas': self._identify_improving_areas(memory_assessments),
            'declining_areas': self._identify_declining_areas(memory_assessments)
        }

        # Generate improvement recommendations
        recommendations = self._generate_memory_strength_recommendations(
            memory_assessments, aggregate_assessments, strength_patterns
        )

        return {
            'individual_assessments': memory_assessments,
            'aggregate_assessments': aggregate_assessments,
            'strength_patterns': strength_patterns,
            'recommendations': recommendations,
            'next_assessment_date': self._schedule_next_assessment(),
            'tracking_metrics': self._define_tracking_metrics()
        }

    def _assess_individual_memory_strength(self, card_data):
        """Assess memory strength for individual card"""

        review_history = card_data.get('review_history', [])
        if len(review_history) < 3:
            return {'strength': 'insufficient_data', 'score': 0}

        # Calculate various strength indicators
        strength_components = {
            'recall_consistency': self._calculate_recall_consistency(review_history),
            'response_time': self._calculate_response_time_score(review_history),
            'error_patterns': self._calculate_error_pattern_score(review_history),
            'confidence_trend': self._calculate_confidence_trend(review_history),
            'retention_decay': self._calculate_retention_decay(review_history)
        }

        # Calculate overall strength score (0-100)
        weights = {
            'recall_consistency': 0.3,
            'response_time': 0.2,
            'error_patterns': 0.2,
            'confidence_trend': 0.15,
            'retention_decay': 0.15
        }

        overall_score = sum(
            strength_components[component] * weights[component]
            for component in strength_components
        )

        # Determine strength category
        if overall_score >= 85:
            strength_category = 'very_strong'
        elif overall_score >= 70:
            strength_category = 'strong'
        elif overall_score >= 55:
            strength_category = 'moderate'
        elif overall_score >= 40:
            strength_category = 'weak'
        else:
            strength_category = 'very_weak'

        # Predict future retention
        retention_prediction = self._predict_future_retention(strength_components, overall_score)

        return {
            'overall_strength': strength_category,
            'strength_score': round(overall_score, 2),
            'strength_components': strength_components,
            'retention_prediction': retention_prediction,
            'optimal_review_interval': self._suggest_optimal_interval(overall_score),
            'strengthening_recommendations': self._generate_strengthening_recommendations(
                strength_components, overall_score
            )
        }

    def _calculate_recall_consistency(self, review_history):
        """Calculate consistency of recall performance"""

        if len(review_history) < 3:
            return 0

        # Get recent quality scores
        quality_scores = [review['quality_score'] for review in review_history[-10:]]

        # Calculate consistency (lower variance = higher consistency)
        variance = statistics.variance(quality_scores)
        max_variance = 4.0  # Maximum possible variance for scores 0-5

        # Convert variance to consistency score
        consistency_score = max(0, (1 - variance / max_variance) * 100)

        # Consider overall performance level
        average_quality = sum(quality_scores) / len(quality_scores)
        performance_bonus = (average_quality / 5) * 10  # Up to 10 points bonus

        return min(100, consistency_score + performance_bonus)

    def _predict_future_retention(self, strength_components, overall_score):
        """Predict future retention based on current strength indicators"""

        # Base retention prediction from overall score
        base_retention = overall_score / 100

        # Adjust based on specific components
        consistency_factor = strength_components.get('recall_consistency', 50) / 100
        decay_factor = 1 - (strength_components.get('retention_decay', 50) / 100)

        # Predict retention at different future points
        predictions = {
            '7_days': base_retention * consistency_factor * 0.95,
            '30_days': base_retention * consistency_factor * decay_factor * 0.85,
            '90_days': base_retention * consistency_factor * decay_factor * 0.75,
            '180_days': base_retention * consistency_factor * decay_factor * 0.65
        }

        # Ensure predictions are within valid range
        for timeframe in predictions:
            predictions[timeframe] = max(0.1, min(1.0, predictions[timeframe]))

        return {
            'predictions': predictions,
            'confidence': self._calculate_prediction_confidence(strength_components),
            'risk_assessment': self._assess_retention_risk(predictions),
            'intervention_points': self._identify_intervention_points(predictions)
        }

class RetentionVisualizer:
    def __init__(self):
        self.chart_types = {
            'retention_trends': self._create_retention_trends_chart,
            'forgetting_curves': self._create_forgetting_curves_chart,
            'memory_strength': self._create_memory_strength_chart,
            'subject_performance': self._create_subject_performance_chart,
            'difficulty_analysis': self._create_difficulty_analysis_chart,
            'learning_velocity': self._create_learning_velocity_chart
        }

    def create_comprehensive_dashboard(self, user_id, time_period=30):
        """Create comprehensive retention analytics dashboard"""

        dashboard_data = {
            'user_id': user_id,
            'time_period': time_period,
            'generation_date': datetime.now(),
            'charts': {},
            'summary_metrics': {},
            'insights': [],
            'recommendations': []
        }

        # Generate all chart types
        for chart_name, chart_function in self.chart_types.items():
            try:
                chart_data = chart_function(user_id, time_period)
                dashboard_data['charts'][chart_name] = chart_data
            except Exception as e:
                dashboard_data['charts'][chart_name] = {'error': str(e)}

        # Calculate summary metrics
        dashboard_data['summary_metrics'] = self._calculate_dashboard_summary_metrics(
            dashboard_data['charts']
        )

        # Generate insights and recommendations
        dashboard_data['insights'] = self._generate_dashboard_insights(dashboard_data)
        dashboard_data['recommendations'] = self._generate_dashboard_recommendations(
            dashboard_data
        )

        return dashboard_data

    def _create_forgetting_curves_chart(self, user_id, time_period):
        """Create interactive forgetting curves visualization"""

        # Get forgetting curve data
        retention_analytics = RetentionAnalytics()
        forgetting_curves = retention_analytics.generate_forgetting_curve(user_id)

        # Prepare chart data
        chart_data = {
            'type': 'line_chart',
            'title': 'Personalized Forgetting Curves',
            'description': 'Memory retention over time for different subjects and difficulty levels',
            'datasets': []
        }

        # Add subject-based curves
        for subject, curve_data in forgetting_curves['subject_curves'].items():
            dataset = {
                'label': subject,
                'data': [
                    {'x': day, 'y': retention * 100}  # Convert to percentage
                    for day, retention in zip(curve_data['time_points'], curve_data['retention_rates'])
                ],
                'borderColor': self._get_subject_color(subject),
                'backgroundColor': self._get_subject_color(subject, opacity=0.1),
                'tension': 0.4,
                'pointRadius': 2,
                'pointHoverRadius': 6
            }
            chart_data['datasets'].append(dataset)

        # Add optimal review threshold line
        threshold_data = [
            {'x': day, 'y': 80}  # 80% retention threshold
            for day in range(0, 181, 5)
        ]

        chart_data['datasets'].append({
            'label': 'Optimal Review Threshold (80%)',
            'data': threshold_data,
            'borderColor': '#ff6b6b',
            'borderDash': [5, 5],
            'borderWidth': 2,
            'pointRadius': 0,
            'fill': False
        })

        # Chart configuration
        chart_data['config'] = {
            'responsive': True,
            'interaction': {
                'intersect': False,
                'mode': 'index'
            },
            'scales': {
                'x': {
                    'title': {
                        'display': True,
                        'text': 'Days Since Last Review'
                    },
                    'min': 0,
                    'max': 180
                },
                'y': {
                    'title': {
                        'display': True,
                        'text': 'Retention Rate (%)'
                    },
                    'min': 0,
                    'max': 100,
                    'ticks': {
                        'callback': 'function(value) { return value + "%"; }'
                    }
                }
            },
            'plugins': {
                'tooltip': {
                    'callbacks': {
                        'label': 'function(context) { return context.dataset.label + ": " + context.parsed.y.toFixed(1) + "%"; }'
                    }
                },
                'legend': {
                    'position': 'top'
                }
            }
        }

        # Add interaction insights
        chart_data['insights'] = self._generate_forgetting_curve_insights(forgetting_curves)

        return chart_data

    def _create_memory_strength_heatmap(self, user_id, time_period):
        """Create memory strength heatmap visualization"""

        # Get memory strength data
        strength_analyzer = MemoryStrengthAnalyzer()
        strength_assessment = strength_analyzer.comprehensive_memory_assessment(user_id)

        # Prepare heatmap data
        heatmap_data = {
            'type': 'heatmap',
            'title': 'Memory Strength Heatmap',
            'description': 'Visual representation of memory strength across subjects and topics',
            'data': []
        }

        # Organize data by subject and topic
        subjects_data = {}
        for card_id, assessment in strength_assessment['individual_assessments'].items():
            card_info = assessment.get('card_info', {})
            subject = card_info.get('subject', 'Unknown')
            topic = card_info.get('topic', 'Unknown')
            strength_score = assessment.get('strength_score', 0)

            if subject not in subjects_data:
                subjects_data[subject] = {}

            subjects_data[subject][topic] = strength_score

        # Convert to heatmap format
        subjects = sorted(subjects_data.keys())
        all_topics = set()
        for subject_data in subjects_data.values():
            all_topics.update(subject_data.keys())
        topics = sorted(list(all_topics))

        # Create heatmap matrix
        for i, subject in enumerate(subjects):
            for j, topic in enumerate(topics):
                strength_score = subjects_data[subject].get(topic, 0)

                heatmap_data['data'].append({
                    'x': j,
                    'y': i,
                    'v': strength_score,
                    'subject': subject,
                    'topic': topic,
                    'strength_category': self._score_to_category(strength_score)
                })

        # Chart configuration
        heatmap_data['config'] = {
            'responsive': True,
            'scales': {
                'x': {
                    'labels': topics,
                    'title': {
                        'display': True,
                        'text': 'Topics'
                    }
                },
                'y': {
                    'labels': subjects,
                    'title': {
                        'display': True,
                        'text': 'Subjects'
                    }
                }
            },
            'colorScale': {
                'min': 0,
                'max': 100,
                'colors': [
                    '#ff4444',  # Very weak (0-20)
                    '#ff8844',  # Weak (20-40)
                    '#ffcc44',  # Moderate (40-60)
                    '#88cc44',  # Strong (60-80)
                    '#44cc44'   # Very strong (80-100)
                ]
            },
            'tooltips': {
                'callbacks': {
                    'title': 'function(context) { return context[0].dataset.data[context[0].dataIndex].subject + " - " + context[0].dataset.data[context[0].dataIndex].topic; }',
                    'label': 'function(context) { return "Strength Score: " + context.raw.v.toFixed(1) + "% (" + context.raw.strength_category + ")"; }'
                }
            }
        }

        return heatmap_data

class RealTimePerformanceMonitor:
    def __init__(self):
        self.live_metrics = {}
        self.alert_thresholds = {
            'retention_drop': 0.15,      # 15% drop triggers alert
            'accuracy_decline': 0.20,    # 20% accuracy decline
            'session_fatigue': 0.70,     # 70% cognitive load
            'queue_overload': 100        # 100+ cards due
        }

    def monitor_live_performance(self, user_id):
        """Monitor real-time performance during study sessions"""

        # Get current session data
        current_session = self._get_current_session_data(user_id)

        # Calculate live metrics
        live_metrics = {
            'session_performance': self._calculate_session_performance(current_session),
            'cognitive_load': self._assess_cognitive_load(current_session),
            'retention_status': self._assess_current_retention(user_id),
            'queue_health': self._assess_queue_health(user_id),
            'engagement_level': self._measure_engagement(current_session)
        }

        # Check for alerts
        alerts = self._check_performance_alerts(live_metrics)

        # Generate real-time recommendations
        recommendations = self._generate_real_time_recommendations(live_metrics, alerts)

        return {
            'timestamp': datetime.now(),
            'live_metrics': live_metrics,
            'alerts': alerts,
            'recommendations': recommendations,
            'session_summary': self._generate_session_summary(current_session),
            'next_actions': self._suggest_next_actions(live_metrics)
        }

    def _calculate_session_performance(self, session_data):
        """Calculate current session performance metrics"""

        if not session_data or not session_data.get 'reviews']:
            return {'status': 'no_data'}

        reviews = session_data['reviews']

        # Calculate performance metrics
        quality_scores = [review['quality_score'] for review in reviews]
        response_times = [review.get('response_time', 0) for review in reviews]

        session_metrics = {
            'cards_reviewed': len(reviews),
            'average_quality': sum(quality_scores) / len(quality_scores),
            'accuracy_rate': sum(1 for score in quality_scores if score >= 3) / len(quality_scores),
            'average_response_time': sum(response_times) / len(response_times) if response_times else 0,
            'session_duration': session_data.get('duration', 0),
            'performance_trend': self._calculate_session_trend(quality_scores)
        }

        # Assess session quality
        if session_metrics['accuracy_rate'] >= 0.8:
            session_quality = 'excellent'
        elif session_metrics['accuracy_rate'] >= 0.7:
            session_quality = 'good'
        elif session_metrics['accuracy_rate'] >= 0.6:
            session_quality = 'acceptable'
        else:
            session_quality = 'needs_improvement'

        session_metrics['session_quality'] = session_quality

        return session_metrics

    def _check_performance_alerts(self, live_metrics):
        """Check for performance alerts and warnings"""

        alerts = []

        # Retention drop alert
        if live_metrics.get('retention_status', {}).get('retention_trend', 0) < -self.alert_thresholds['retention_drop']:
            alerts.append({
                'type': 'retention_drop',
                'severity': 'high',
                'message': 'Significant retention drop detected. Consider reviewing fundamentals.',
                'recommended_action': 'review_basics'
            })

        # Accuracy decline alert
        session_performance = live_metrics.get('session_performance', {})
        if session_performance.get('accuracy_rate', 1.0) < (1 - self.alert_thresholds['accuracy_decline']):
            alerts.append({
                'type': 'accuracy_decline',
                'severity': 'medium',
                'message': 'Accuracy has declined significantly. Take a break or reduce difficulty.',
                'recommended_action': 'take_break'
            })

        # Cognitive overload alert
        cognitive_load = live_metrics.get('cognitive_load', {}).get('load_score', 0)
        if cognitive_load > self.alert_thresholds['session_fatigue']:
            alerts.append({
                'type': 'cognitive_overload',
                'severity': 'high',
                'message': 'High cognitive load detected. Risk of burnout.',
                'recommended_action': 'immediate_break'
            })

        # Queue overload alert
        queue_health = live_metrics.get('queue_health', {})
        if queue_health.get('cards_due', 0) > self.alert_thresholds['queue_overload']:
            alerts.append({
                'type': 'queue_overload',
                'severity': 'medium',
                'message': f'{queue_health["cards_due"]} cards due for review. Consider catch-up session.',
                'recommended_action': 'schedule_catch_up'
            })

        return alerts

class PredictiveAnalytics:
    def __init__(self):
        self.prediction_models = {
            'retention_prediction': self._build_retention_prediction_model(),
            'mastery_timeline': self._build_mastery_timeline_model(),
            'optimal_study_schedule': self._build_schedule_optimization_model(),
            'exam_performance': self._build_exam_performance_model()
        }

    def generate_learning_predictions(self, user_id, prediction_horizon=90):
        """Generate comprehensive learning performance predictions"""

        # Collect historical data
        historical_data = self._collect_historical_performance_data(user_id)

        # Generate predictions for different aspects
        predictions = {
            'retention_forecast': self._predict_retention_trajectory(
                historical_data, prediction_horizon
            ),
            'mastery_predictions': self._predict_mastery_timeline(
                historical_data, prediction_horizon
            ),
            'study_optimization': self._predict_optimal_study_schedule(
                historical_data, prediction_horizon
            ),
            'performance_goals': self._predict_goal_achievement(
                historical_data, user_id, prediction_horizon
            )
        }

        # Calculate prediction confidence
        confidence_scores = {
            aspect: self._calculate_prediction_confidence(historical_data, aspect)
            for aspect in predictions
        }

        # Generate actionable insights
        insights = self._generate_predictive_insights(predictions, confidence_scores)

        # Create risk assessment
        risk_assessment = self._assess_learning_risks(predictions, historical_data)

        return {
            'prediction_horizon': prediction_horizon,
            'predictions': predictions,
            'confidence_scores': confidence_scores,
            'insights': insights,
            'risk_assessment': risk_assessment,
            'recommendations': self._generate_predictive_recommendations(predictions, insights),
            'next_review_date': self._schedule_next_prediction_review(prediction_horizon)
        }

    def _predict_retention_trajectory(self, historical_data, horizon_days):
        """Predict retention rates over the prediction horizon"""

        # Analyze historical retention patterns
        retention_patterns = self._analyze_retention_patterns(historical_data)

        # Build predictive model parameters
        model_parameters = {
            'base_retention_rate': retention_patterns['current_retention'],
            'retention_decay_rate': retention_patterns['decay_rate'],
            'seasonal_factors': self._identify_seasonal_patterns(historical_data),
            'difficulty_factors': self._calculate_difficulty_impact(historical_data),
            'subject_factors': self._calculate_subject_impact(historical_data)
        }

        # Generate daily predictions
        daily_predictions = {}
        current_date = datetime.now()

        for day in range(horizon_days + 1):
            target_date = current_date + timedelta(days=day)

            # Calculate predicted retention for this day
            predicted_retention = self._calculate_predicted_retention(
                day, model_parameters, historical_data
            )

            # Add confidence interval
            confidence_interval = self._calculate_confidence_interval(
                predicted_retention, day, historical_data
            )

            daily_predictions[day] = {
                'date': target_date,
                'predicted_retention': predicted_retention,
                'confidence_interval': confidence_interval,
                'risk_level': self._assess_retention_risk(predicted_retention, confidence_interval)
            }

        # Identify critical retention points
        critical_points = self._identify_critical_retention_points(daily_predictions)

        return {
            'daily_predictions': daily_predictions,
            'critical_points': critical_points,
            'model_parameters': model_parameters,
            'forecast_summary': self._summarize_retention_forecast(daily_predictions)
        }

    def _predict_mastery_timeline(self, historical_data, horizon_days):
        """Predict when different mastery levels will be achieved"""

        # Current mastery status
        current_mastery = self._assess_current_mastery_status(historical_data)

        # Learning velocity analysis
        learning_velocity = self._calculate_learning_velocity(historical_data)

        # Predict mastery progression for each subject and topic
        mastery_predictions = {}

        for subject in current_mastery['subjects']:
            subject_predictions = {}

            for topic in current_mastery['subjects'][subject]['topics']:
                current_level = current_mastery['subjects'][subject]['topics'][topic]['current_level']
                velocity = learning_velocity.get(subject, {}).get(topic, {}).get('velocity', 0)

                # Predict timeline for each mastery level
                level_predictions = {}
                for target_level in range(current_level + 1, 6):  # Up to mastery level 5
                    days_to_target = self._calculate_days_to_mastery(
                        current_level, target_level, velocity
                    )

                    if days_to_target <= horizon_days:
                        target_date = datetime.now() + timedelta(days=days_to_target)
                        level_predictions[target_level] = {
                            'predicted_date': target_date,
                            'days_from_now': days_to_target,
                            'confidence': self._calculate_mastery_prediction_confidence(
                                current_level, target_level, velocity, historical_data
                            )
                        }

                subject_predictions[topic] = {
                    'current_level': current_level,
                    'target_levels': level_predictions,
                    'learning_velocity': velocity
                }

            mastery_predictions[subject] = subject_predictions

        # Generate mastery milestones
        milestones = self._generate_mastery_milestones(mastery_predictions, horizon_days)

        return {
            'mastery_predictions': mastery_predictions,
            'milestones': milestones,
            'velocity_analysis': learning_velocity,
            'progress_summary': self._summarize_mastery_progress(mastery_predictions)
        }

class LearningEfficiencyAnalyzer:
    def __init__(self):
        self.efficiency_metrics = {
            'time_efficiency': self._calculate_time_efficiency,
            'retention_efficiency': self._calculate_retention_efficiency,
            'review_efficiency': self._calculate_review_efficiency,
            'cognitive_efficiency': self._calculate_cognitive_efficiency
        }

    def analyze_learning_efficiency(self, user_id, analysis_period=30):
        """Comprehensive analysis of learning efficiency"""

        # Collect efficiency data
        efficiency_data = self._collect_efficiency_data(user_id, analysis_period)

        # Calculate efficiency metrics
        efficiency_metrics = {
            'overall_efficiency': self._calculate_overall_efficiency(efficiency_data),
            'time_management': self._analyze_time_efficiency(efficiency_data),
            'review_optimization': self._analyze_review_efficiency(efficiency_data),
            'cognitive_load': self._analyze_cognitive_efficiency(efficiency_data),
            'retention_quality': self._analyze_retention_efficiency(efficiency_data)
        }

        # Identify inefficiencies and bottlenecks
        inefficiency_analysis = {
            'time_wasters': self._identify_time_inefficiencies(efficiency_data),
            'review_bottlenecks': self._identify_review_bottlenecks(efficiency_data),
            'cognitive_overload': self._identify_cognitive_inefficiencies(efficiency_data),
            'retention_gaps': self._identify_retention_inefficiencies(efficiency_data)
        }

        # Generate optimization recommendations
        optimization_plan = self._generate_optimization_plan(
            efficiency_metrics, inefficiency_analysis
        )

        return {
            'efficiency_metrics': efficiency_metrics,
            'inefficiency_analysis': inefficiency_analysis,
            'optimization_plan': optimization_plan,
            'expected_improvements': self._predict_optimization_impact(optimization_plan),
            'implementation_roadmap': self._create_implementation_roadmap(optimization_plan)
        }

    def _calculate_overall_efficiency(self, efficiency_data):
        """Calculate overall learning efficiency score"""

        # Component efficiency scores
        time_score = self._calculate_time_efficiency_score(efficiency_data)
        retention_score = self._calculate_retention_efficiency_score(efficiency_data)
        review_score = self._calculate_review_efficiency_score(efficiency_data)
        cognitive_score = self._calculate_cognitive_efficiency_score(efficiency_data)

        # Weighted overall efficiency
        weights = {
            'time': 0.25,
            'retention': 0.3,
            'review': 0.25,
            'cognitive': 0.2
        }

        overall_score = (
            time_score * weights['time'] +
            retention_score * weights['retention'] +
            review_score * weights['review'] +
            cognitive_score * weights['cognitive']
        )

        # Determine efficiency category
        if overall_score >= 85:
            efficiency_category = 'excellent'
        elif overall_score >= 70:
            efficiency_category = 'good'
        elif overall_score >= 55:
            efficiency_category = 'moderate'
        elif overall_score >= 40:
            efficiency_category = 'needs_improvement'
        else:
            efficiency_category = 'poor'

        return {
            'overall_score': round(overall_score, 2),
            'efficiency_category': efficiency_category,
            'component_scores': {
                'time_efficiency': time_score,
                'retention_efficiency': retention_score,
                'review_efficiency': review_score,
                'cognitive_efficiency': cognitive_score
            },
            'benchmark_comparison': self._compare_with_efficiency_benchmarks(overall_score),
            'improvement_potential': self._assess_improvement_potential(overall_score)
        }

    def _generate_optimization_plan(self, efficiency_metrics, inefficiency_analysis):
        """Generate comprehensive optimization plan"""

        optimization_plan = {
            'priority_improvements': [],
            'quick_wins': [],
            'long_term_optimizations': [],
            'behavioral_changes': [],
            'technical_adjustments': []
        }

        # Analyze inefficiencies and prioritize improvements
        for inefficiency_type, issues in inefficiency_analysis.items():
            for issue in issues:
                priority = self._calculate_improvement_priority(issue, efficiency_metrics)

                improvement_item = {
                    'issue': issue,
                    'priority': priority,
                    'estimated_impact': self._estimate_improvement_impact(issue),
                    'implementation_difficulty': self._assess_implementation_difficulty(issue),
                    'recommended_actions': self._generate_specific_actions(issue)
                }

                # Categorize improvement based on characteristics
                if priority == 'high' and issue['implementation_difficulty'] == 'low':
                    optimization_plan['quick_wins'].append(improvement_item)
                elif priority == 'high':
                    optimization_plan['priority_improvements'].append(improvement_item)
                elif issue['category'] == 'behavioral':
                    optimization_plan['behavioral_changes'].append(improvement_item)
                elif issue['category'] == 'technical':
                    optimization_plan['technical_adjustments'].append(improvement_item)
                else:
                    optimization_plan['long_term_optimizations'].append(improvement_item)

        # Sort each category by priority and impact
        for category in optimization_plan:
            optimization_plan[category].sort(
                key=lambda x: (x['priority'], x['estimated_impact']),
                reverse=True
            )

        return optimization_plan

class MobileAnalytics:
    def __init__(self):
        self.mobile_features = {
            'offline_tracking': self._enable_offline_tracking,
            'quick_insights': self._generate_quick_insights,
            'progress_notifications': self._setup_progress_notifications,
            'mobile_dashboard': self._create_mobile_dashboard
        }

    def create_mobile_analytics_experience(self, user_id):
        """Create optimized analytics experience for mobile devices"""

        mobile_config = {
            'dashboard_layout': 'compact',
            'chart_optimization': 'mobile_friendly',
            'data_refresh': 'real_time',
            'offline_capability': True,
            'battery_optimization': True
        }

        # Generate mobile-optimized dashboard
        mobile_dashboard = self._create_mobile_optimized_dashboard(user_id, mobile_config)

        # Setup offline analytics capabilities
        offline_analytics = self._setup_offline_analytics(user_id)

        # Create quick insights system
        quick_insights = self._setup_quick_insights_system(user_id)

        # Configure mobile notifications
        notification_config = self._configure_mobile_notifications(user_id)

        return {
            'mobile_dashboard': mobile_dashboard,
            'offline_analytics': offline_analytics,
            'quick_insights': quick_insights,
            'notification_config': notification_config,
            'sync_settings': self._configure_mobile_sync(user_id),
            'performance_optimizations': self._apply_mobile_performance_optimizations()
        }

    def _create_mobile_optimized_dashboard(self, user_id, config):
        """Create analytics dashboard optimized for mobile screens"""

        # Get core analytics data
        core_analytics = RetentionAnalytics().generate_core_metrics(user_id)

        # Optimize data for mobile display
        mobile_metrics = {
            'key_performance_indicators': self._extract_kpis_for_mobile(core_analytics),
            'trend_summary': self._create_trend_summary(core_analytics),
            'quick_stats': self._generate_quick_stats(core_analytics),
            'progress_indicators': self._create_progress_indicators(core_analytics)
        }

        # Create mobile-friendly visualizations
        mobile_visualizations = {
            'mini_charts': self._create_mini_charts(core_analytics),
            'progress_bars': self._create_progress_bars(core_analytics),
            'sparklines': self._create_sparklines(core_analytics),
            'status_indicators': self._create_status_indicators(core_analytics)
        }

        # Mobile interaction patterns
        interaction_design = {
            'swipe_actions': self._define_swipe_actions(),
            'tap_targets': self._define_tap_targets(),
            'gesture_support': self._define_gesture_support(),
            'voice_commands': self._define_voice_commands()
        }

        return {
            'layout': 'mobile_optimized',
            'metrics': mobile_metrics,
            'visualizations': mobile_visualizations,
            'interactions': interaction_design,
            'performance': {
                'load_time_target': '<2 seconds',
                'data_usage_optimized': True,
                'battery_friendly': True
            }
        }