Prediction of Student’s Educational Performance Using Machine Learning Techniques

Abstract

Educational data mining indicates an area of research in which the data mining, machine learning, and statistics are applied to predict information from academic environment. Educating is an act of imparting or acquiring knowledge to/from a person formally engaged in learning and developing their innate quality. Over the years, the data mining techniques are being applied to academics to find out the hidden knowledge from educational datasets and other external factors. Previous research has been done to identify the elements that change the performance, and these elements can be termed as emotional and external factors. One’s performance can be affected by factors such as not attending classes, diversion, remembrance, physical or mental exhaustion due to exertion, sentiments, surroundings, pecuniary, and pressure from family members. This research effort is on external factors and organizational elements. For teachers to foretell the future of a student is very useful and it identifies a student with his performance. In this research paper, external factors are studied and investigated and implemented using XGBoost classifier for predicting the student’s performance.