Leveraging Machine and Ensemble Learning Techniques to Timely Predict Student Academic Achievements and Performance
Keywords:
Student Performance Analysis and Prediction, Machine Learning, Ensemble Learning, SMOTE, ENN and Voting ClassifierAbstract
Student achievement analysis and prediction seems to be the most helpful when used to assist instructors and students enhance their pedagogical practices. The use of various analytical tools to forecast student performance has been looked at in recent studies on this topic. Researchers have most frequently employed two data sets: internal evaluation and external evaluation that gives Cumulative Grade Point Average (CGPA). Techniques of machine learning such as Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, Decision Tree, Multi-Layer Perceptron, AdaBoost, or Ensemble learning are suggested for this study. Support Vector Machine, Random Forest, Decision Trees, and Ensemble Learning all acquire a Precision, Recall and F1 score of 100, while Naive Bayes achieves a Precision, Recall and F1 score of 68, 65, and 66, respectively, according to the performance evaluation for Models with Matrices. Also included is a performance evaluation of the models that consider the metrics accuracy, the macro average accuracy and the weighted average accuracy. The most accurate models have a weighted average, with K-Nearest Neighbors, Naive Bayes or AdaBoost being the least accurate. Support Vector Machine, Random Forest, Decision trees Multi-Layer Perceptron and Ensemble learning Models are 100 percent accurate which provide the highest accuracy, macro average accuracy and also weighted average accuracy.
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