Prediction of Students’ Performance based on Academic, Behaviour, Extra and Co-Curricular Activities

Abstract

Since Academic institutions contain huge volume of data regarding students such as academic scores, scores in co and extracurricular activities, family annual income, family background and other supporting documents, predicting individual students performance in all aspects manually is a difficult task. The proposed work uses data mining techniques to identify students who are eligible for scholarships and other benefits. Students are classified into different categories by means of academic, behavior, extra and co-curricular activities. Machine Learning algorithms such as Naive Bayes, Decision Tree Classifier and Support Vector Machine are used for predicting the performance of the student. With the help of this proposed model parents and instructors can monitor student’s performance and they can also provide essential technical and moral support. Also this helps in providing academic scholarship and training to the students to support them financially and to enrich their knowledge. It suggests the Academic Institutions to organize induction or training programmes at the beginning of the semester. Technical training, motivational talks, Yoga, etc are organized by the institutions by keeping in mind of students physical and mental health. Considering the e-learning platforms huge volumes of data and plethora of information are generated. In this work, various learning models are constructed and their accuracies are compared to analyse which algorithm out-performs.