Adaptive Learning Platform (DSA Project)

A modular, test-driven Adaptive Learning Platform built using classical Data Structures & Algorithms (DSA).
The system simulates how a real-world intelligent learning platform works—using data structures like Trie, Graph, Priority Queue, Array, and HashMap to power search, sequencing, history, and recommendations.

This project demonstrates clean architecture, modern Python practices, and full automated testing suitable for academic submission and professional portfolios.

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📚 Features at a Glance

🔍 Trie-Based Content Search

Fast prefix-based search for:

• Course titles
• Sequence titles

Used for autocomplete and content discovery.

🔗 Graph-Based Course Prerequisites

Courses and their prerequisites are stored as a Directed Acyclic Graph (DAG):

• Add courses
• Add prerequisites
• Retrieve direct and indirect prerequisites
• Perform topological sorting

🧵 Priority Sequence Scheduler

Sequences are scheduled using a min-heap priority queue:

• Lower priority = executed earlier
• Stable ordering (FIFO for equal priority)
• Students progress step-by-step

📜 Student History (Array-Backed)

Logs activities using:

• Array indexes
• Activity objects

Tracks:

• Completed sequences
• Quiz scores
• Timestamps

🎯 Deterministic Recommendation Engine

Ranks the next best courses using:

• Difficulty alignment
• Progress gap
• Recency of learning

Provides explanation strings for transparency.

💻 CLI Demo

A full menu-driven CLI to interact with the system:

• Register students
• Search content
• Enroll in a course
• Complete sequences
• View history
• Generate recommendations

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🧠 DSA Concepts Used

Feature	Data Structure	File
Content Search	Trie	core/search/trie.py
Course Prerequisites	Directed Graph	core/graph/course_graph.py
Sequence Scheduling	Min-Heap	core/scheduling/sequence_scheduler.py
Student Registry	HashMap (Dict)	core/models/student.py
Activity History	Array	core/history/history.py
Recommendations	Weighted Scoring + Sorting	core/recommendations/recommendation_engine.py

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🗂️ Project Structure

adaptive-learning-platform/
├─ core/
│  ├─ models/
│  │  ├─ course.py
│  │  ├─ sequence.py
│  │  ├─ student.py
│  │  ├─ activity.py
│  │  └─ recommendation.py
│  ├─ search/
│  │  └─ trie.py
│  ├─ graph/
│  │  └─ course_graph.py
│  ├─ scheduling/
│  │  └─ sequence_scheduler.py
│  ├─ history/
│  │  └─ history.py
│  ├─ students/
│  │  └─ student_service.py 
│  ├─ recommendations/
│  │  └─ recommendation_engine.py
│  ├─ persistence/
│  │  └─ storage.py
│  ├─ config.py           
│  └─ logging_config.py      
├─ cli/
│  └─ cli.py
├─ tests/
│  ├─ test_trie.py
│  ├─ test_course_graph.py
│  ├─ test_sequence_scheduler.py
│  ├─ test_student_history.py
│  ├─ test_students.py
│  ├─ test_recommendations.py
│  ├─ test_storage.py
│  └─ test_integration_flow.py
├─ docs/
│  ├─ architecture.md     
│  └─ api_examples.md         
├─ .github/
│  └─ workflows/
│     └─ ci.yml
├─ README.md
├─ CONTRIBUTING.md
├─ LICENSE
├─ requirements.txt
├─ pytest.ini
└─ .gitignore