Intelligent Recommender System using Machine Learning to Reinforce Probation Students in OMAN Technical Education

Abstract

Machine learning is fascinating the entire industrial world to get new insights from different verticals. In extent to that recommender system is quiet popular to provide customized individual recommendation whenever there is information overflow, this prediction capabilities boosting the business in order to increase the revenue specially in the ecommerce platform. Past few years researcher’s has adopted machine learning and recommender system proficiencies to accelerate educational data mining and learning analytics. In this article, researchers are contributing to find new and innovative insights using the said techniques. Currently OMAN is a fast growing country and rapidly trying to transform its existing education system into a modern and excellent education system.  In this scenario, the proposed insights will help the academicians and scholars to improve the students’ performance. Based on the analysis and observation percentage of scholars who is under probation has been rapidly increasing, the proposed model will use the basic concept of collaborative filtering recommender system using machine learning to predict the course wise grade before opting the individual course. The proposed model will predict how much grade he could get into the individual course and advisors can compare with grade, which is required to come out from the probation. In addition to that, to know the reason of the tutees probation, correlation analysis based on machine learning model has been implemented to find the most result effecting factors. The said insights will help the advisors and faculty members to recommend the students what actions required to clear his/her probation.