Abstract
Today, predictive analytics applications became an urgent desire in higher educational institutions. Predictive analytics used advanced analytics that encompasses machine learning implementation to derive high-quality performance and meaningful information for all education levels. Mostly know that student grade is one of the key performance indicators that can help educators monitor their academic performance. During the past decade, researchers have proposed many variants of machine learning techniques in education domains. However, there are severe challenges in handling imbalanced datasets for enhancing the performance of predicting student grades. Therefore, this paper presents a comprehensive analysis of machine learning techniques to predict the final student grades in the first semester courses by improving the performance of predictive accuracy. Two modules will be highlighted in this paper. First, we compare the accuracy performance of six well-known machine learning techniques namely Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (kNN), Logistic Regression (LR) and Random Forest (RF) using 1282 real student's course grade dataset. Second, we proposed a multiclass prediction model to reduce the overfitting and misclassification results caused by imbalanced multi-classification based on oversampling Synthetic Minority Oversampling Technique (SMOTE) with two features selection methods. The obtained results show that the proposed model integrates with RF give significant improvement with the highest f-measure of 99.5%. This proposed model indicates the comparable and promising results that can enhance the prediction performance model for imbalanced multi-classification for student grade prediction.
Highlights
In higher education institutions (HEI), every institution has its student academic management system to record all studentThe associate editor coordinating the review of this manuscript and approving it for publication was Syed Islam .data containing information about student academic results in final examination marks and grades in different courses and programs
RQ1: COMPARISON OF THE PREDICTIVE MODEL USING MACHINE LEARNING ALGORITHMS Our main objective is to compare the predictive model based on the accuracy performance
The result showed that K-Nearest Neighbor (kNN) exhibited the highest performance f-measure score up to 98.8% and 98.9% with the optimal selected features set obtained from feature selection (FS)-2 and FS-3 algorithm respectively compared to others predictive models
Summary
Data containing information about student academic results in final examination marks and grades in different courses and programs. All student marks and grades have been recorded and used to generate a student academic performance report to evaluate the course achievement every semester. Most common factors are relying on socioeconomic background, demographics [3] and learning activities [4] compared to final student grades in the final examination [5]. As for this reason, we observe that the trend of predicting student grades can be one of the solutions that are applicable to improve student academic performance [6]
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