The Case Study on Data Mining Based Students’ Performance Prediction for Academic Success in E-learning

This study investigates student performance with the aim to forecast students' academic outcomes using data analytics and machine learning techniques. There is a big drop out per cent especially in the technological study programs at the universities, however, focusing on the successful study process there is important to analyze and to predict some risks on students’ to assure low dropout percent. While Student Performance Prediction (SPP) has potential benefits, such as personalized learning and early interventions, several challenges make the task complex in relation to (1) data quality and availability and (2) incomplete and inconsistent data. Predicting performance requires a range of different data types: academic records, attendance, and engagement in class, socio-economic background, and even behavioral factors. Moreover, according to the fourth Sustainable Development Goal (SDG) we focus on the quality in education.This paper presents a case study of data mining method for SPP by using classification, regression, and clustering are generally used in education to predict student’s performance which is important to control drop out of studies percent. The case presented is to predict risks and to assure academic success and quality based on the CRISP-DM data mining.