A light weight hybrid recommendation system for personalised-learning based on user learning behavior

Abstract

Millions of students use learning platforms every day. This shows how fast online education is growing. Choosing the course can be tough with so many options out there. The Lightweight Hybrid Recommendation System helps with this. This thing is made for learning. It uses two methods: Collaborative Filtering and Content-Based Filtering to help you learn. The education system examines the way students learn and understand things. For example, it checks how time they spend on lessons how they do on quizzes and if they finish courses. This information helps the system give better course recommendations. We tested it with 50 students and 80 learning resources covering ten topics. The results were good. It worked better than using one method. It is also fast and efficient. This makes it suitable for to medium-sized online learning platforms. The Lightweight Hybrid Recommendation System is a tool, for online learning. It assists students in finding courses and study materials that suit their needs. The system provides recommendations. Students can make the most of their learning experience by being participants. The Lightweight Hybrid Recommendation System plays a role.