International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2025
p-ISSN: 2395-0072
www.irjet.net
Development of personalized Recommendation System for E-Learning Platform Gajanan Wankhade 1, Mohak Taywade, Shreyash Meshram 1Gajanan Wankhade Prof, & Guide JD college 2 Mohak Taywade, Student, Police line takli 3 Shreyash Meshram, student , Fetri
Asssistant Professor, Students, Department of Computer and Science and Engineering JD College of Engineering and Management Nagpur ---------------------------------------------------------------------***--------------------------------------------------------------------1.1 Related Work Abstract - In later a long time, e-learning stages such as Coursera, Udemy, edX, and Khan Institute have revolutionized the way individuals procure information and abilities over the globe. With the computerized change of instruction, learners nowadays have get to to an overpowering number of courses, instructional exercises, and learning assets. Be that as it may, exploring this tremendous ocean of substance to discover personalized, important, and skill-appropriate materials remains a major challenge for clients. This inquire about proposes a energetic substance suggestion framework custom fitted particularly for elearning situations, planned to convey personalized learning encounters based on client interface, learning designs, ability levels, and interaction history. The center objective of this framework is to direct learners along effective learning ways by prescribing substance such as courses, instructional exercises, video addresses, and perusing materials. These proposals are not as it were based on what clients have locked in with already, but moreover consider how well they have performed, their favored modes of learning (video, content, intelligently works out), and their career or learning objectives. By analyzing behavioral information and execution measurements, the framework points to improve learning productivity and inspiration through focused on suggestions.
Recommendation frameworks have been a conspicuous zone of investigate in both scholastic and commercial spaces, playing a basic part in personalization for stages such as Netflix, Amazon, and Spotify. Within the setting of instruction, a few e-learning stages have coordinated recommender frameworks to upgrade client engagement and learning results. This segment presents a survey of existing strategies, advances, and restrictions recommendation systems.
1.2 Personalized Learning Path Generation Rather than giving isolated recommendations, the system builds a progressive learning path. This is achieved by: • Mapping prerequisites and dependencies between courses • Identifying learner’s skill gaps using test results and skipped topics • Sequencing content from beginner to advanced levels within a topic
2. DEPLOYMENT PLAN & FLOW DIAGRAM The arrangement of the proposed content recommendation system follows a modular microservice architecture to ensure scalability, performance, and effectiveness. The framework is composed of key components including a frontend interface, backend APIs, the recommendation engine, databases, and machine learning model management. The frontend is built using React.js or Vue.js and deployed as a static web application, typically served through NGINX or platforms like AWS S3 with CloudFront for efficient content delivery and fast loading times. This interface interacts directly with the backend to fetch course recommendations, display user dashboards, and manage feedback submissions.
Key Words: Machine Learning, Mobile Application Development, Data Collection, Model Optimization, Agricultural Technology, AI Integration, Future Challenges.
1.INTRODUCTION The fast development of online instruction has definitely changed how people learn, upskill, and move over careers. Elearning platforms such as Coursera, Udemy, edX, and the Khan Institute have become central to this advancement, offering a wide range of courses across disciplines, open and often at a low cost. Be that as it may, with the gigantic deluge of instructional substance, learners are regularly overpowered with choices, making it troublesome to recognize the foremost significant, skill-appropriate, and personalized learning materials..
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3. CONCLUSIONS The proposed content recommendation system presents a dynamic and adaptive solution to the growing demand for personalized learning experiences in e-learning environments. By integrating content-based filtering, collaborative filtering, clustering techniques, and Natural
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