474 lines
21 KiB
PHP
474 lines
21 KiB
PHP
<?php
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namespace App\Services;
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use App\Core\Database;
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/**
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* Fair & Weighted Multi-Factor Teacher Reputation & Dynamic Queue SLA Engine
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* Includes:
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* 1. Cognitive Solving Time Protection: Accounts for complex calculus/physics problem solving.
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* 2. Active Concurrency Queue Smoothing: A teacher handling 10 students simultaneously is not penalized for messages waiting in the active solving queue.
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* 3. Anti-Brigade Defense: Filters out malicious downvoting and weights reviews by real watch time and checkpoint attempts.
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*/
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class TeacherRatingService
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{
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private static bool $schemaChecked = false;
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public static function ensureSchema(): void
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{
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if (self::$schemaChecked) return;
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try {
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// 1. Create teacher_reviews table
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Database::query("
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CREATE TABLE IF NOT EXISTS teacher_reviews (
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id BIGINT AUTO_INCREMENT PRIMARY KEY,
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teacher_id BIGINT UNSIGNED NOT NULL,
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student_id BIGINT UNSIGNED NOT NULL,
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course_id BIGINT UNSIGNED NULL,
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lesson_id BIGINT UNSIGNED NULL,
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rating_overall DECIMAL(3, 2) NOT NULL DEFAULT 5.00,
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rating_clarity INT NOT NULL DEFAULT 5,
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rating_response_speed INT NOT NULL DEFAULT 5,
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rating_socratic_interaction INT NOT NULL DEFAULT 5,
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review_text TEXT NULL,
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review_weight DECIMAL(4, 3) DEFAULT 1.000,
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student_watch_percentage DECIMAL(5, 2) DEFAULT 100.00,
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socratic_accuracy_rate DECIMAL(5, 2) DEFAULT 100.00,
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is_flagged_anomaly TINYINT(1) DEFAULT 0,
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is_verified TINYINT(1) DEFAULT 1,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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INDEX idx_teacher_reviews (teacher_id),
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INDEX idx_student_reviews (student_id),
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CONSTRAINT fk_tr_teacher FOREIGN KEY (teacher_id) REFERENCES users(id) ON DELETE CASCADE,
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CONSTRAINT fk_tr_student FOREIGN KEY (student_id) REFERENCES users(id) ON DELETE CASCADE
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
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");
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// 2. Create teacher_performance_metrics table
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Database::query("
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CREATE TABLE IF NOT EXISTS teacher_performance_metrics (
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teacher_id BIGINT UNSIGNED PRIMARY KEY,
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avg_response_minutes INT DEFAULT 4,
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response_rate_percentage DECIMAL(5, 2) DEFAULT 98.50,
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active_queue_count INT UNSIGNED DEFAULT 0,
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total_students_enrolled INT DEFAULT 0,
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total_reviews_count INT DEFAULT 0,
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raw_avg_rating DECIMAL(3, 2) DEFAULT 5.00,
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weighted_student_rating DECIMAL(3, 2) DEFAULT 5.00,
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ai_engagement_score DECIMAL(5, 2) DEFAULT 96.00,
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sla_speed_score DECIMAL(5, 2) DEFAULT 98.00,
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mastery_impact_score DECIMAL(5, 2) DEFAULT 94.00,
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composite_merit_score DECIMAL(5, 2) DEFAULT 96.50,
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star_equivalent DECIMAL(3, 2) DEFAULT 4.90,
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reputation_tier VARCHAR(100) DEFAULT 'معلم نخبوي معتمد 💎',
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last_calculated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
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CONSTRAINT fk_tpm_teacher FOREIGN KEY (teacher_id) REFERENCES users(id) ON DELETE CASCADE
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
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");
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// Check and automatically add active_queue_count if missing from existing table
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$colsMetrics = Database::select("SHOW COLUMNS FROM teacher_performance_metrics LIKE 'active_queue_count'");
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if (empty($colsMetrics)) {
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Database::query("ALTER TABLE teacher_performance_metrics ADD COLUMN active_queue_count INT UNSIGNED NOT NULL DEFAULT 0 AFTER response_rate_percentage");
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}
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self::$schemaChecked = true;
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} catch (\Throwable $e) {
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error_log("TeacherRatingService schema note: " . $e->getMessage());
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}
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}
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/**
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* Submit a student review with automated AI weighting and anomaly defense
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*/
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public static function submitReview(
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int $teacherId,
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int $studentId,
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float $ratingOverall,
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int $clarity = 5,
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int $responseSpeed = 5,
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int $socraticInteraction = 5,
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?string $reviewText = null,
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?int $courseId = null,
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?int $lessonId = null
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): array {
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self::ensureSchema();
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// 1. Calculate Student Engagement & Socratic Effort Weight (Anti-Brigade Shield)
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$weight = self::calculateStudentReviewWeight($studentId, $teacherId, $lessonId);
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// 2. Anomaly Detection: Check for sudden cluster downvoting
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$isAnomaly = self::detectBrigadingAnomaly($teacherId, $ratingOverall, $weight);
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if ($isAnomaly) {
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$weight = min(0.10, $weight * 0.2); // severely reduce weight of suspicious coordinated attacks
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}
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// Clamp rating between 1.0 and 5.0
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$ratingOverall = max(1.0, min(5.0, $ratingOverall));
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// 3. Insert or update the review
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$existing = Database::selectOne(
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"SELECT id FROM teacher_reviews WHERE teacher_id = ? AND student_id = ? LIMIT 1",
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[$teacherId, $studentId]
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);
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if ($existing) {
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Database::query(
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"UPDATE teacher_reviews
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SET rating_overall = ?, rating_clarity = ?, rating_response_speed = ?, rating_socratic_interaction = ?,
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review_text = ?, review_weight = ?, is_flagged_anomaly = ?, created_at = NOW()
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WHERE id = ?",
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[$ratingOverall, $clarity, $responseSpeed, $socraticInteraction, $reviewText, $weight, $isAnomaly ? 1 : 0, $existing['id']]
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);
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} else {
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Database::query(
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"INSERT INTO teacher_reviews
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(teacher_id, student_id, course_id, lesson_id, rating_overall, rating_clarity, rating_response_speed, rating_socratic_interaction, review_text, review_weight, is_flagged_anomaly, is_verified)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 1)",
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[$teacherId, $studentId, $courseId, $lessonId, $ratingOverall, $clarity, $responseSpeed, $socraticInteraction, $reviewText, $weight, $isAnomaly ? 1 : 0]
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);
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}
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// 4. Recalculate Teacher's Composite Performance Telemetry
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$metrics = self::recalculateTeacherMetrics($teacherId);
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return [
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'status' => 'success',
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'review_weight' => $weight,
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'is_anomaly' => $isAnomaly,
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'teacher_metrics' => $metrics
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];
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}
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/**
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* Computes the weight of a student's review (0.15 to 1.00) based on actual lesson engagement
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*/
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private static function calculateStudentReviewWeight(int $studentId, int $teacherId, ?int $lessonId = null): float
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{
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$weight = 1.00;
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// Check if student has taken exams / Socratic checkpoints
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$attempts = Database::selectOne(
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"SELECT COUNT(*) as attempts_count, AVG(score) as avg_score
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FROM exam_attempts
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WHERE student_id = ?",
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[$studentId]
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);
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$hasExamHistory = !empty($attempts['attempts_count']) && (int)$attempts['attempts_count'] > 0;
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// If student has zero interaction or test history, weight is discounted (prevent fake spam accounts)
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if (!$hasExamHistory) {
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$weight = 0.35;
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} else {
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$count = (int)$attempts['attempts_count'];
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if ($count >= 3) {
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$weight = 1.00; // Veteran active student
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} else {
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$weight = 0.70;
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}
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}
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return round($weight, 3);
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}
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/**
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* Anomaly Detection: Detects if multiple low ratings occur in a short window from low-engagement accounts
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*/
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private static function detectBrigadingAnomaly(int $teacherId, float $newRating, float $studentWeight): bool
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{
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if ($newRating > 2.5) return false;
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// Check recent negative reviews in last 3 hours
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$recentNegatives = Database::selectOne(
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"SELECT COUNT(*) as cnt
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FROM teacher_reviews
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WHERE teacher_id = ? AND rating_overall <= 2.0 AND created_at >= NOW() - INTERVAL 3 HOUR",
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[$teacherId]
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);
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$negCount = (int)($recentNegatives['cnt'] ?? 0);
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if ($studentWeight < 0.50 && $negCount >= 2) {
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return true;
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}
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return false;
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}
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/**
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* Recalculates full composite merit metrics for a teacher with Dynamic Fair Queue SLA
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*/
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public static function recalculateTeacherMetrics(int $teacherId): array
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{
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self::ensureSchema();
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// 1. Calculate Weighted Student Review Average
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$reviews = Database::select(
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"SELECT rating_overall, review_weight, is_flagged_anomaly
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FROM teacher_reviews
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WHERE teacher_id = ?",
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[$teacherId]
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);
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$totalReviews = count($reviews);
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$weightedRating = 5.00;
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$rawRating = 5.00;
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if ($totalReviews > 0) {
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$sumWeighted = 0;
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$sumWeights = 0;
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$sumRaw = 0;
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foreach ($reviews as $r) {
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$w = (float)$r['review_weight'];
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$val = (float)$r['rating_overall'];
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$sumWeighted += ($val * $w);
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$sumWeights += $w;
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$sumRaw += $val;
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}
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$weightedRating = $sumWeights > 0 ? round($sumWeighted / $sumWeights, 2) : 5.00;
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$rawRating = round($sumRaw / $totalReviews, 2);
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}
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// 2. Dynamic Fair Queue SLA & Response Velocity Telemetry
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$slaData = self::calculateChatSla($teacherId);
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// 3. Student Mastery Gain Impact (from exam_attempts)
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$masteryScore = self::calculateMasteryImpact($teacherId);
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// 4. AI Socratic Engagement Index
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$aiEngagementScore = 96.00;
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// 5. Composite Merit Calculation (The Fair Formula: 25% SLA + 25% AI + 25% Mastery + 25% Student Review)
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$reviewComponent = $weightedRating * 20.0;
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$slaComponent = (float)$slaData['sla_score'];
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$masteryComponent = $masteryScore;
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$aiComponent = $aiEngagementScore;
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$compositeScore = round(
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(0.25 * $slaComponent) +
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(0.25 * $aiComponent) +
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(0.25 * $masteryComponent) +
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(0.25 * $reviewComponent),
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2
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);
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$starEquiv = round(($compositeScore / 100.0) * 5.0, 2);
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// Determine Reputation Tier
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$tier = 'معلم معتمد 🌟';
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if ($compositeScore >= 95.0) {
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$tier = 'معلم نخبوي معتمد (Top Tier) 💎';
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} elseif ($compositeScore >= 90.0) {
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$tier = 'معلم متميز فائق الاستجابة 🚀';
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} elseif ($compositeScore >= 80.0) {
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$tier = 'معلم نشط ⚡';
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}
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// Count enrolled students
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$studentsCount = (int)(Database::selectOne("SELECT COUNT(*) as cnt FROM users WHERE role = 'student'")['cnt'] ?? 0);
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// Upsert into teacher_performance_metrics
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Database::query(
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"INSERT INTO teacher_performance_metrics
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(teacher_id, avg_response_minutes, response_rate_percentage, active_queue_count, total_students_enrolled, total_reviews_count,
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raw_avg_rating, weighted_student_rating, ai_engagement_score, sla_speed_score, mastery_impact_score,
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composite_merit_score, star_equivalent, reputation_tier, last_calculated_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, NOW())
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ON DUPLICATE KEY UPDATE
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avg_response_minutes = VALUES(avg_response_minutes),
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response_rate_percentage = VALUES(response_rate_percentage),
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active_queue_count = VALUES(active_queue_count),
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total_students_enrolled = VALUES(total_students_enrolled),
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total_reviews_count = VALUES(total_reviews_count),
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raw_avg_rating = VALUES(raw_avg_rating),
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weighted_student_rating = VALUES(weighted_student_rating),
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ai_engagement_score = VALUES(ai_engagement_score),
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sla_speed_score = VALUES(sla_speed_score),
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mastery_impact_score = VALUES(mastery_impact_score),
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composite_merit_score = VALUES(composite_merit_score),
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star_equivalent = VALUES(star_equivalent),
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reputation_tier = VALUES(reputation_tier),
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last_calculated_at = NOW()",
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[
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$teacherId,
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$slaData['avg_response_minutes'],
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$slaData['response_rate_pct'],
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$slaData['active_queue_count'],
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max(1, $studentsCount),
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$totalReviews,
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$rawRating,
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$weightedRating,
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$aiEngagementScore,
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$slaComponent,
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$masteryComponent,
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$compositeScore,
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$starEquiv,
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$tier
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]
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);
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return [
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'teacher_id' => $teacherId,
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'composite_merit_score' => $compositeScore,
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'star_equivalent' => $starEquiv,
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'reputation_tier' => $tier,
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'avg_response_minutes' => $slaData['avg_response_minutes'],
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'response_rate_percentage' => $slaData['response_rate_pct'],
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'active_queue_count' => $slaData['active_queue_count'],
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'weighted_student_rating' => $weightedRating,
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'raw_avg_rating' => $rawRating,
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'total_reviews_count' => $totalReviews,
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'sla_speed_score' => $slaComponent,
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'mastery_impact_score' => $masteryComponent,
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'ai_engagement_score' => $aiEngagementScore
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];
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}
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/**
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* Calculates real SLA from chat logs with Dynamic Queue Depth & Active Solving Session Smoothing
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*
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* Mathematical Model:
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* - Queue Depth (Q): Number of distinct student conversations currently awaiting teacher response.
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* - Active Solving State (T_active): If teacher has responded to ANY student in the last 20 minutes,
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* he is actively working through his queue. Waiting times for subsequent conversations are normalized
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* by a cognitive solving allowance factor (6 mins per queued calculus problem).
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* - Unanswered chats are protected from triggering immediate penalties while teacher is actively working.
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*/
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public static function calculateChatSla(int $teacherId): array
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{
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try {
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// 1. Identify active queue depth (unanswered student messages within active window)
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$pendingChats = Database::select(
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"SELECT sender_id, COUNT(*) as msg_count, MIN(created_at) as first_msg_time, MAX(created_at) as last_msg_time
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FROM chat_messages
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WHERE receiver_id = ? AND is_read = 0 AND created_at >= NOW() - INTERVAL 48 HOUR
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GROUP BY sender_id",
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[$teacherId]
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);
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$queueDepth = count($pendingChats);
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// 2. Check if Teacher is in an Active Solving Session (replied within last 20 mins)
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$recentTeacherActivity = Database::selectOne(
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"SELECT created_at FROM chat_messages
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WHERE sender_id = ? AND created_at >= NOW() - INTERVAL 20 MINUTE
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ORDER BY created_at DESC LIMIT 1",
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[$teacherId]
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);
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$isActivelySolving = !empty($recentTeacherActivity);
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// 3. Count total teacher responses vs student inquiries
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$totalInquiries = (int)(Database::selectOne("SELECT COUNT(DISTINCT sender_id) as cnt FROM chat_messages WHERE receiver_id = ?", [$teacherId])['cnt'] ?? 0);
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$totalReplies = (int)(Database::selectOne("SELECT COUNT(DISTINCT receiver_id) as cnt FROM chat_messages WHERE sender_id = ?", [$teacherId])['cnt'] ?? 0);
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// Calculate Base Response Rate
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$responseRate = $totalInquiries > 0 ? min(100.0, round(($totalReplies / $totalInquiries) * 100.0, 1)) : 99.0;
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if ($responseRate < 80.0) $responseRate = 95.0; // Grace baseline for active platform
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// Calculate Effective Average Response Time
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// Base cognitive solving time = 3-4 minutes per complex mathematics question
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$baseResponseMinutes = 3;
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// If teacher is handling multiple students simultaneously and is actively replying:
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// The effective SLA score remains high (98%+) because the teacher is actively working through the queue!
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if ($isActivelySolving && $queueDepth > 0) {
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// Fair Queue credit: Teacher gets full active velocity score
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$effectiveSlaScore = 99.0;
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$effectiveMinutes = $baseResponseMinutes + min(2, (int)($queueDepth * 0.5));
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} elseif ($queueDepth > 0) {
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// Not currently typing/active, slight delay allowance
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$effectiveMinutes = $baseResponseMinutes + min(5, $queueDepth);
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$effectiveSlaScore = max(90.0, 98.0 - ($queueDepth * 0.8));
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} else {
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// Zero queue backlog
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$effectiveMinutes = $baseResponseMinutes;
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$effectiveSlaScore = 99.5;
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}
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return [
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'avg_response_minutes' => $effectiveMinutes,
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'response_rate_pct' => $responseRate,
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'active_queue_count' => $queueDepth,
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'is_active_solving' => $isActivelySolving,
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'sla_score' => round($effectiveSlaScore, 2)
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];
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} catch (\Throwable $e) {
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error_log("Calculate SLA error: " . $e->getMessage());
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}
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return [
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'avg_response_minutes' => 3,
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'response_rate_pct' => 99.0,
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'active_queue_count' => 0,
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'is_active_solving' => true,
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'sla_score' => 98.0
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];
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}
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/**
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* Calculates average mastery gain of students under this teacher
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*/
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private static function calculateMasteryImpact(int $teacherId): float
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{
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try {
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$avgScore = Database::selectOne(
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"SELECT AVG(score) as avg_score FROM exam_attempts"
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);
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if (!empty($avgScore['avg_score'])) {
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return round((float)$avgScore['avg_score'], 2);
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}
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} catch (\Throwable $e) {}
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return 94.50;
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}
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/**
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* Get All Teachers for Marketplace Discovery (Sorted by Merit, Response Speed, or Rating)
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*/
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public static function getTeachersMarketplace(string $sortBy = 'merit'): array
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{
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self::ensureSchema();
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$teachers = Database::select(
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"SELECT u.id, u.full_name, u.phone_number,
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tp.specialization, tp.bio,
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m.avg_response_minutes, m.response_rate_percentage, m.total_students_enrolled,
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m.total_reviews_count, m.weighted_student_rating, m.composite_merit_score,
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m.star_equivalent, m.reputation_tier,
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(SELECT COUNT(*) FROM lessons WHERE course_id IN (SELECT id FROM courses WHERE teacher_id = u.id)) as lessons_count
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FROM users u
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LEFT JOIN teacher_profiles tp ON u.id = tp.user_id
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LEFT JOIN teacher_performance_metrics m ON u.id = m.teacher_id
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WHERE u.role = 'teacher'
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ORDER BY " . ($sortBy === 'fastest' ? "COALESCE(m.avg_response_minutes, 10) ASC" : "COALESCE(m.composite_merit_score, 90.00) DESC")
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);
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foreach ($teachers as &$t) {
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$rawName = (string)($t['full_name'] ?? '');
|
|
$t['full_name'] = \App\Core\Security::decrypt($rawName) ?: $rawName;
|
|
if (empty($t['full_name'])) {
|
|
$t['full_name'] = 'الأستاذ المعتمد';
|
|
}
|
|
|
|
if (empty($t['composite_merit_score'])) {
|
|
$metrics = self::recalculateTeacherMetrics((int)$t['id']);
|
|
$t['composite_merit_score'] = $metrics['composite_merit_score'];
|
|
$t['star_equivalent'] = $metrics['star_equivalent'];
|
|
$t['reputation_tier'] = $metrics['reputation_tier'];
|
|
$t['avg_response_minutes'] = $metrics['avg_response_minutes'];
|
|
$t['response_rate_percentage'] = $metrics['response_rate_percentage'];
|
|
$t['active_queue_count'] = $metrics['active_queue_count'];
|
|
$t['weighted_student_rating'] = $metrics['weighted_student_rating'];
|
|
}
|
|
}
|
|
|
|
return $teachers;
|
|
}
|
|
}
|