Recommendation system for E-learning platforms

Abstract

E-Learning platform provides learners all over the world with information and resources to enhance the education management and delivery. In the current scenario there is a lack of recommendation systems for various online courses on the web. Anyone eager to learn from this huge pool of courses online, they need to get a legitimate course which is best suited for them. The recommendation system which is currently available is not suited for learners. With the rapid increase of E-learning, a new demand of recommendation system has been created which can help a learner in such a way so that they can choose a suitable course for themselves in an easy and accessible manner. In this paper an E-learning recommendation system is constructed to offer better courses to the users. The proposed recommendation system extracts user reviews from various E-learning websites such as Edx, Coursera, Udemy, etc. and suggests the users whether the course is suitable or not. Reviews are collected by using web scraping for an e-learning website. Then sentiment analysis of the collected reviews is performed. This tells the system about the sentiment of a particular review. Afterwards techniques like hybrid SVM and maximum entropy are used to provide a recommendation to the user. Then the users can easily decide which course is better suited for them. So, the tedious task of going through all the courses on the web can be avoided. With the help of this the users can easily map out the best course of their choosing.