Accuracy analysis of Educational Data Mining using Feature Selection Algorithm

TL;DR

This paper evaluates how feature selection in Educational Data Mining improves data accuracy for predicting student progress by removing irrelevant attributes and analyzing results with machine learning metrics.

Contribution

It demonstrates the effectiveness of feature selection in EDM for enhancing data accuracy and reliability in student performance prediction.

Findings

Improved accuracy metrics with feature selection

Enhanced data quality for educational research

Effective removal of irrelevant features

Abstract

Gathering relevant information to predict student academic progress is a tedious task. Due to the large amount of irrelevant data present in databases which provides inaccurate results. Currently, it is not possible to accurately measure and analyze student data because there are too many irrelevant attributes and features in the data. With the help of Educational Data Mining (EDM), the quality of information can be improved. This research demonstrates how EDM helps to measure the accuracy of data using relevant attributes and machine learning algorithms performed. With EDM, irrelevant features are removed without changing the original data. The data set used in this study was taken from Kaggle.com. The results compared on the basis of recall, precision and f-measure to check the accuracy of the student data. The importance of this research is to help improve the quality of educational research by providing more accurate results for researchers.