A Review on Mining Students’ Data for Performance Prediction

Abstract— A country’s growth is strongly measured by the quality of its education system. Education sector has witnessed sea change in its functioning. Today it is recognized as an industry and as an industry it is facing challenges. The challenges of higher education being decrease in students’ success rate and their leaving the course without completion. An early prediction of students’ failure may help the management provide timely counselling as well as coaching to increase the success rate and student retention. Data mining are widely used in educational field to find new hidden patterns from student’s data which are used to understand the problem. Classification is one of the prediction type classifiers that classifies data based on the training set and uses the pattern to classify a new data. Aim of the project is to develop an internetworking application that uses data mining technique to predict the students’ performance based on their behaviour. This paper explores the link between emotional skills of the students along with the socio economic and previous academic performance parameters using Naive Bayes Classifier technique. Higher Education of Students has a direct impact on the work force provided to the industry and hence it directly affects the economy of the country. In educational institutions quality of the education is judged by the success rate of the students and to what extent the institute is capable of retaining its students. Student’s academic performance is based upon the diverse factors like personal, social, psychological and other environmental factors. Predicting Students’ performance can help identify the students who are at risk of failure and thus management can provide timely help < final year projects >