Unlocking Student Dropout Patterns: A Machine Learning-Based Data Analysis

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Anikinee Deb

Unlocking Student Dropout Patterns: A Machine Learning-Based Data Analysis

Authors: Anikinee Deb

Unique Paper ID: 180431
Volume: 12
Issue: 1
PageNo: 1530-1537

Keywords: Educational Data Mining Random Forest KNN SVC Logistic Regression Decision Tree Naïve Bayes Machine Learning ROC-AUC Dropout Prediction Data Science

Abstract:
In higher education, student dropout is still a major problem that has an impact on both the student outcome as well as institutional performance. This study uses a multi-dimensional dataset that includes academic, demographic, and economic features to examine how supervised machine learning algorithms can be used for predicting student dropout. These features were used to train six classifiers - Random Forest, Support Vector Classifier (SVC), Logistic Regression, K-Nearest Neighbours (KNN), Decision Tree, and Naive Bayes and evaluated using the performance metrics – Accuracy and ROC-AUC. With ROC-AUC scores of 0.956, 0.951, and 0.948, respectively, and accuracies of up to 0.91, Logistic Regression, Random Forest, and SVM outperformed the other classifiers in terms of predictive performance. To evaluate each feature group's relative contribution, a methodical feature ablation analysis was performed. The Logistic Regression and Random Forest models' ROC-AUC scores dropped from 0.956 to 0.810 and 0.951 to 0.816, respectively, when academic features were removed, resulting in the biggest performance drop of any model. On the other hand, the performance of the model was only marginally affected by the elimination of demographic or economic characteristics. The findings show that the most important predictors of student dropout are academic indicators, which are followed by economic and demographic characteristics.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 1
PageNo: 1530-1537

Unlocking Student Dropout Patterns: A Machine Learning-Based Data Analysis

Available:https://ijirt.org/article?manuscript=180431

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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