Student Dropout Risk Assessment in Undergraduate Course at Residential University

TL;DR

This paper presents a machine learning-based prototype tool for predicting student dropout risk at a university, utilizing decision trees and association rule mining to identify key factors and patterns influencing dropout.

Contribution

It introduces an integrated approach combining decision trees, discriminant analysis, and association rule mining for dropout prediction and factor analysis in university students.

Findings

Decision tree effectively classifies dropout risk.

Key dropout factors identified through discriminant analysis.

Interesting correlations discovered via association rule mining.

Abstract

Student dropout prediction is an indispensable for numerous intelligent systems to measure the education system and success rate of any university as well as throughout the university in the world. Therefore, it becomes essential to develop efficient methods for prediction of the students at risk of dropping out, enabling the adoption of proactive process to minimize the situation. Thus, this research work propose a prototype machine learning tool which can automatically recognize whether the student will continue their study or drop their study using classification technique based on decision tree and extract hidden information from large data about what factors are responsible for dropout student. Further the contribution of factors responsible for dropout risk was studied using discriminant analysis and to extract interesting correlations, frequent patterns, associations or casual structures among significant datasets, Association rule mining was applied. In this study, the descriptive statistics analysis was carried out to measure the quality of data using SPSS 20.0 statistical software and application of decision tree and association rule were carried out by using WEKA data mining tool.