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@article{156211,
        author = {Kruthika K  and Bhoomika C and Anitha.K and J Andrea Kagoo and Aruna MG},
        title = {Student grade Prediction using multiclass model},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {3},
        pages = {13-25},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156211},
        abstract = {Today, predictive analytics applications became an urgent desire in higher educational institutions. Predictive analytics use advanced analytics that encompasses machine learning implementation to derive high-quality performance and meaningful information for all education levels. We know that student grade is one of the key performance indicators that can help educators monitor academic performance. During the past decade, researchers have proposed many variants of machine learning techniques in education domains. However, there are several challenges in handling imbalanced datasets for enhancing the performance of predicting student grades. This project 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. This is carried out in a two-step process. 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 based on data given},
        keywords = {},
        month = {},
        }