Drop-Out Prediction in Higher Education Using Imbalanced Multiclass Dataset
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Abstract
Introduction: High quality education has the potential to drive social change, promote equity and alleviate poverty. The prosperity of nations is closely linked to the calibre of their education systems. However, student attrition at the university level poses a major obstacle to mitigating social disparities. While many factors contribute to this phenomenon, leveraging machine learning and data analytics to identify influencing variables and predict potential student dropout is an effective approach to address this problem.
Objectives: To analyses risk-factors for attrition (drop out) of students at Higher Education Institutions and machine learning algorithms for early detection of such students that could benefit all the stakeholders.
Methods: The study used an unbalanced dataset from a higher education institution to build a classification model to predict academic dropout. The dataset was balanced using oversampling technique and tested using three machine learning algorithms: Random Forest (RF), Support Vector Machines (SVM) and Multinomial Logistic Regression (LR).
Results: The best result was achieved with RF model, with high values of recall, specificity, F1 and balanced accuracy for each of classes: Dropout, Enrolled and Graduate.
Conclusions: A total of 23 features were selected. With 80% of the balanced data, the training of three machine learning models was carried out. For the validation process, the remaining 20% of the data from the original (unbalanced) dataset was used. The results showed a high accuracy in two of the trained models: RM and SVM, with an overall accuracy higher than 0.93.
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References
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