E-Learning has been upgrading its own methods to overcome the learning base. This document represents the architecture of the adaptive learning system. Adaptive Learning is a method for utilizing data-driven instruction to adapt and personalize learning experiences according to the unique requirements of individual students. Reinforcement Learning is a type of adaptive learning used to specify type of learning for each student. Q-Learning is an algorithm under the reinforcement learning used in this project to analyse, and specify to the student which method of learning they best coop with.

• Personalized Learning Paths: The system should analyze students’ past performance and learning patterns to create personalized learning paths tailored to their individual needs and learning pace.

• Continuous Adaptation: Utilize Q-learning algorithm to dynamically adjust learning content and difficulty levels based on students’ progress, ensuring they are consistently challenged at an appropriate level.

• Real-time Feedback: Provide real-time feedback to students on their performance, strengths, and areas needing improvement, helping them to identify their progress and areas of focus.

• Optimized Resource Allocation: Efficiently allocate learning resources by recommending

specific learning materials, activities, and assessments to students based on their proficiency and learning goals.

• Engagement Enhancement: Enhance student engagement by incorporating interactive elements and multimedia content features to make the learning experience more enjoyable and motivating.

• Performance Tracking: Monitor and track students’ performance metrics, such as grades, completion rates, and time spent on tasks, to evaluate the effectiveness of the adaptive learning system and identify areas for improvement.

• Accessibility and Usability: Ensure the system is user-friendly and accessible to students with diverse learning needs, including support for multiple devices and assistive technologies.

• Privacy and Security: Implement robust privacy and security measures to protect students’ personal data and ensure compliance with data protection regulations.

• Integration with Moodle: Seamlessly integrate with the Moodle learning management system to leverage existing course materials, user accounts, and administrative functionalities.

• Continuous Improvement: Regularly evaluate and refine the adaptive learning system through data analysis, user feedback, and pedagogical research to enhance its effectiveness and usability over time

• Simple system for students and teachers to use.

• The system shall adapt on each student’s way of learning.

• The system shall have a simple UI.

• The system shall be compatible with different hand-held devices.

• The system shall have CRUD option for both Instructor and Manager

• The system shall be secured for each end of users.

• The system shall have an API used for teachers to add content through.

• The system should take user feedback and work on it.