🎓 Zeba Academy Recommendation Engine
An AI-driven adaptive learning recommendation engine for Flutter EdTech apps.
Built to power intelligent learning systems with:
- ✅ Weak Topic Detection
- ✅ Adaptive Difficulty Adjustment
- ✅ Personalized Learning Path
- ✅ Next Lesson Suggestion
- ✅ Performance-Based Recommendations
- 🏆 Mastery Tracking (Beginner → Expert)
Designed for quiz apps, exam simulators, LMS platforms, and competitive exam preparation systems.
🚀 Features
📊 Weak Topic Detection
Automatically identifies topics where student accuracy is below threshold.
🎯 Adaptive Difficulty Adjustment
Adjusts difficulty level dynamically based on performance.
🧠 Personalized Learning Path
Generates ordered learning path based on weakest topics first.
📚 Next Lesson Suggestion
Recommends the most suitable next lesson.
💬 Performance-Based Feedback
Generates actionable feedback messages.
🏆 Mastery Tracking System
Classifies learners into:
- Beginner
- Intermediate
- Advanced
- Expert
📸 Preview
📦 Installation
Add to your pubspec.yaml:
dependencies:
zeba_academy_recommendation_engine: ^1.0.0
Then run:
flutter pub get
🛠 Basic Usage
1️⃣ Import
import 'package:zeba_academy_recommendation_engine/zeba_academy_recommendation_engine.dart';
2️⃣ Create Student Performance
final performance = StudentPerformance(
studentId: "S1",
topicStats: {
"Algebra": TopicPerformance(
topicName: "Algebra",
totalQuestions: 20,
correctAnswers: 10,
incorrectAnswers: 10,
averageTimeTaken: 35,
currentDifficulty: 2,
),
},
);
3️⃣ Detect Weak Topics
final engine = RecommendationEngine();
final weakTopics = engine.detectWeakTopics(performance);
4️⃣ Suggest Next Lesson
final lessons = [
Lesson(
lessonId: "L1",
topic: "Algebra",
difficulty: 1,
title: "Algebra Basics",
),
];
final nextLesson =
engine.suggestNextLesson(performance, lessons);
5️⃣ Mastery Tracking
final masteryEngine = MasteryEngine();
final topic = performance.topicStats["Algebra"]!;
final masteryLevel =
masteryEngine.getMasteryLevel(topic);
print(masteryLevel.label); // Beginner / Intermediate / Advanced / Expert
🏗 Architecture
models/
├── student_performance.dart
├── topic_performance.dart
├── lesson.dart
├── mastery_level.dart
engine/
├── recommendation_engine.dart
├── mastery_engine.dart
📈 Mastery Scoring Logic
Mastery score (0–100) is calculated using:
- 70% Accuracy Weight
- 30% Speed Efficiency Weight
This creates balanced performance intelligence.
🎯 Use Cases
- Competitive Exam Apps
- CBT Simulation Platforms
- School Learning Apps
- Adaptive Practice Apps
- AI-powered LMS Systems
- Mock Test Platforms
🧪 Testing
Run tests:
flutter test
🛣 Roadmap
- 🔥 AI Score Prediction
- 📊 Learning Analytics Dashboard Widgets
- ☁ Firebase Analytics Integration
- 📈 Performance Trend Tracking
- 🏆 Student Ranking System
🤝 Contributing
Contributions, issues and feature requests are welcome.
📄 License
This package is part of the Zeba Academy Flutter SDK ecosystem.
👨💻 Author
Zeba Academy Building professional Flutter packages for modern EdTech platforms.