ABSTRACT
Educational data mining is focused on developing techniques and studying educational data for the better understanding of students’ academic performance. It plays a vital role to enhance teaching and learning methodology. Authors from Kampus Gong Badak have designed a framework for predicting the academic performance of computer science bachelor students. This chapter outlines to encourage teachers to include data mining tools as a part of higher education knowledge management systems. Five types clustering data mining techniques such as partitioning clustering, grid-based clustering, hierarchical clustering, density-based clustering, and model-based clustering are reviewed. Classification technique classifies data based on the learning set and uses the pattern to classify a test set. Clustering performs grouping of students based on similar values. Authors Sadiq Hussain and Hazarika have proposed a method using Rattle for the selection of educational data mining model.
