Recommender system for big data in education

With the advent of web based e-learning systems, a huge amount of educational data is getting generated. These massive data gave rise to Big data in educational sectors. Currently, big data analytics techniques are being used to analyze these educational data and generate different predictions and recommendations for students, teachers and schools. Recommendation systems are already very helpful in e-commerce, service industry and social networking sites. Recently recommendation systems are proved to be efficient for education sector as well. In this work we are using recommendation system for Big data in education. This work uses collaborative filtering based recommendation techniques to recommend elective courses to students, depending upon their grade points obtained in other subjects. We are using item based recommendation of Mahout machine learning library on top of Hadoop to generate set of recommendations. Similarity Log-likelihood is used to discover patterns among grades and subjects. Root Mean Square Error between actual grade and recommended grade is used to test the recommendation system. The output of this study can be used by schools, colleges or universities to suggest alternative elective courses to students.