Personalized Learning Recommendation System in E-learning Platforms Using Collaborative Filtering and Machine Learning

Personalised learning recommendation systems are an essential component in the process of enhancing the effectiveness of websites that provide online education. Educational information that is tailored to the specific requirements of each student is the responsibility of these systems, which are accountable for delivering it. This research study aims to present a novel approach to personalized learning recommendation systems that makes use of collaborative filtering and machine learning techniques. The purpose of this research study is to present this method. In our method, we make use of collaborative filtering techniques to explore the interactions that take place between users and products, as well as to identify patterns of similarity among pupils. The use of machine learning models, such as decision trees and neural networks, enables us to present learners with tailored recommendations based on their preferences, historical behaviors, and performance indicators. This is accomplished through the utilization of these models. The solution that is being provided offers recommendations that are not only dynamic but also adaptable; these recommendations alter over time regardless of how learners interact with the platform.