Performance Prediction of Engineering Students using Decision Trees

Abstract

mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on engineering students' past performance data to generate the model and this model can be used to predict the students' performance. It will enable to identify the students in advance who are likely to fail and allow the teacher to provide appropriate inputs.