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Code Learn
Subject area: Science,Engineering and Technology · Area of research: Computer Science
DOI: https://doi.org/10.64388/IREV9I7-1713466
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.
How to cite this paper
@article{1713466,
author = {Nandhini G S, Abitha M, Kavya G, Haarhish V S, Jaya Surya S; Lumin Yagal S},
title = {Code Learn},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {7},
pages = {652-655},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713466.pdf},
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.},
month = {January},
doi = {https://doi.org/10.64388/IREV9I7-1713466}
}