Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

TL;DR

This paper applies classification algorithms to educational data to predict engineering students' exam performance, aiming to identify and assist weak students for performance improvement.

Contribution

It demonstrates the effectiveness of decision tree algorithms in predicting student outcomes and provides a framework for targeted academic interventions.

Findings

Decision trees accurately predict student pass/fail outcomes.

Predictions helped improve performance of weak students.

Analysis showed better results after applying the model.

Abstract

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, Bayesian network etc can be applied on the educational data for predicting the student's performance in examination. This prediction will help to identify the weak students and help them to score better marks. The C4.5, ID3 and CART decision tree algorithms are applied on engineering student's data to predict their performance in the final exam. The outcome of the decision tree predicted the number of students who are likely to pass, fail or promoted to next year. The results provide steps to improve the performance of the students who were predicted to fail or promoted. After the declaration of the results in the final examination the marks obtained by the students are fed into the system and the results were analyzed for the next session. The comparative analysis of the results states that the prediction has helped the weaker students to improve and brought out betterment in the result.