Data Mining Aspects for Student Performance and Analysis in Web-Based Educational System LON-CAPA

Classifying Student Features and Predicting Test Performance in a Web-based Educational System

Abstract

Data mining, analytics machine learning are used on EDM information to extract information from educational environments. It is increasingly in demand and draws further interest because of the rise in educational data in web based learning and even the development in traditional education. This indicates that the potential of data mining tools to provide novel solutions for decision-makers to solve problems exists in particular areas. The data analyzed and the knowledge collected from the educational field using DM techniques was referred to as educational data mining (EDM). This research offers a tool to classify student features in order to predict test performance based on attributes derived from historical data within a web-based educational system. We will propose a specific version of the laws of contrast as well as a method for the detection of such trends. This approach is applicable to the LON-CAPA method.