Data Mining Applications: A comparative Study for Predicting Student's performance

TL;DR

This paper explores the application of decision tree algorithms in educational data mining to predict student performance, aiming to improve decision-making and student support strategies.

Contribution

It demonstrates the use of decision trees on student data to predict performance, highlighting its potential in educational decision support systems.

Findings

Decision trees effectively predict student performance.

The model helps identify students needing special attention.

Application can reduce dropout rates.

Abstract

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.