PREDICTING STUDENT DROPOUT AND ACADEMIC SUCCESS THROUGH EDUCATIONAL ANALYTICS
DOI:
https://doi.org/10.69980/tndfzk62Keywords:
educational analytics, Agroforestry systems, academic success, machine learning, explainable artificial intelligenceAbstract
Students drop out is one of the major concerns in higher education as it impacts learners, institutions and educational resources. The objective of this study was to use educational analytics and supervised machine learning for predicting the academic outcomes: dropping out, continued enrolment and graduation. The following variables were analyzed: demographic, socioeconomic, admission-related, semester-level academic, and macroeconomic. Logistic Regression, Decision Tree, Random Forest, XGBoost, Support Vector Machine and K-Nearest Neighbours algorithms were used for classification and compared. Performance of the models was assessed by accuracy, balanced accuracy, precision, recall, F1-score, confusion matrices and multiclass ROC-AUC. Random Forest had the highest class balanced accuracy (70.8%) and highest macro F1 score (0.708), and the highest accuracy (75.9%). The overall accuracy of XGBoost was 76.2% with the highest macro-ROC-AUC of 0.890. The most dominant predictors were identified through explainable artificial intelligence analysis, and were approved curricular units in semester 2, course, approved units in semester 1, tuition-fee status, age at enrolment and semester grades. Both the graduations and the dropouts were better classified than the enrolled students. The results show that interpretable machine learning models can be used to assist early-warning systems and guide the implementation of specific academic, financial and counselling services and support to students identified as being at risk.
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