Abstract
CodeLearn is an intelligent and adaptive e-learning platform designed to transform how students and professionals acquire knowledge through personalized, data-driven instruction. The platform provides a comprehensive library of courses, quizzes, and learning materials across domains such as programming, data science, and web development. By modeling each learner?s pace, performance, and interests, CodeLearn delivers a tailored learning experience that supports continuous engagement and improved outcomes. The system is built on a modern technological stack, featuring a React.js?based frontend and a Python backend implemented using Flask or Django. To optimize content delivery, CodeLearn employs a hybrid recommendation engine that integrates Collaborative Filtering, Content-Based Filtering, and Deep Q-Learning. These machine learning techniques enable the platform to recommend the most relevant lessons and activities for each user, adapting dynamically as their learning behavior evolves. Secure user authentication and role-based access control ensure that learners receive personalized dashboards, while administrators can efficiently manage course content, monitor learner progress, and oversee system operations. Adaptive quizzes and real-time analytics further enhance personalization by evaluating individual performance and adjusting difficulty levels to match the learner?s evolving proficiency
Keywords
Personalized Learning Platform, Adaptive E-Learning System, AI-Based Education, Hybrid Recommendation Engine, React.Js Frontend, Python Backend, Secure Authentication, Machine Learning Integration, Deep Q-Learning, Adaptive Assessment, Firebase Notifications, Progress Analytics.
Citations
IRE Journals:
Nandhini G S, Abitha M, Kavya G, Haarhish V S, Jaya Surya S; Lumin Yagal S "Code Learn" Iconic Research And Engineering Journals Volume 9 Issue 7 2026 Page 652-655 https://doi.org/10.64388/IREV9I7-1713466
IEEE:
Nandhini G S, Abitha M, Kavya G, Haarhish V S, Jaya Surya S; Lumin Yagal S
"Code Learn" Iconic Research And Engineering Journals, 9(7) https://doi.org/10.64388/IREV9I7-1713466
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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