  Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area
focusing upon methodologies for extracting useful knowledge from data and there
are several useful KDD tools to extracting the knowledge. This knowledge can be
used to increase the quality of education. But educational institution does not
use any knowledge discovery process approach on these data. Data 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 students' past performance data to generate the model and this model can be
used to predict the students' performance. It helps earlier in identifying the
dropouts and students who need special attention and allow the teacher to
provide appropriate advising/counseling.
