Student Dropout Prediction Using Machine Learning Techniques
Keywords:
Support Vector Machine (SVM), Neural Networks (NN), Naïve Bayes (NB), Dropout prediction, Decision Tree (DT), Machine Learning (ML), Random Forest (RF), Logistic Regression (LR)Abstract
In recent years, the number of students that drop out of school has substantially increased. Many educational institutions or universities have been threatened by the high percentage of students who abandon a registered course, the common study subject in the learning analytics field is the early and accurate prediction of Student Dropout depending on the available educational data. Despite the volume of completed study, there has been little advancement and this trend has persisted across all levels of educational data. Although many features have already been studied, it is still unclear which features may be used with various machine learning classifiers for the forecasting of student drop out. A major objective of this research is to highlight the importance of understanding and gathering data, emphasize the limitations of the available educational datasets, compare machine learning classifier performance, and demonstrate that, if performance metrics are carefully taken into consideration, even a limited set of features teachers can use to predict student dropout in an e-learning course can be accurate. Four academic years' worth of data was evaluated. The features chosen for this investigation worked well in identifying course completers and dropouts. On unobserved data from the upcoming school year, the prediction accuracy ranged between 92 and 93%. Along with the commonly employed performance measurements, the homogeneity of machine learning classifiers was compared and studied in order to mitigate the effect of the small dataset size on the high performance metrics values. The outcomes demonstrated that a number of machine learning methods might be used successfully to analyse an academic data which is of tiny size. this can result in a page being rejected by search engines. Ensure that your abstract reads well and is grammatically correct.
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