MULTICLASS PREDECTION MODEL FOR STUDENTS GRADE PREDECTION USING MACHINE LEARNING
G.JAYANTH SATYA
, SHAIK MULLA ALMAS , D.PAVAN KUMAR , MOHAMMED ASRAR , M.YASHWANTH, N.SAI SUBHASH
Today, there is a growing demand for predictive analytics applications in higher education institutions. These applications utilize advanced analytics, including machine learning, to extract valuable insights and improve performance across all levels of education. Student grades are a crucial performance indicator that educators use to track academic progress. Over the past decade, various machine learning techniques have been proposed for educational purposes. However, challenges persist in dealing with imbalanced datasets to enhance the accuracy of predicting student grades. This study offers a detailed analysis of machine learning techniques to predict final student grades in first-semester courses, aiming to boost predictive accuracy. The paper focuses on two main modules. Firstly, it compares the performance of six popular machine learning techniques - Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (kNN ), Logistic Regression (LR), and Random Forest (RF) - using a dataset of 1282 real student course grades. Secondly, a multiclass prediction model is proposed to address overfitting and misclassification issues in imbalanced multi-class scenarios, employing oversampling techniques like Synthetic Minority Oversampling Technique (SMOTE) along with feature selection methods. The results demonstrate that integrating the proposed model with RF leads to a significant improvement, achieving the highest f-measure of 99.5%. This model shows promising results in enhancing prediction performance for imbalanced multi-class student grade prediction.
"MULTICLASS PREDECTION MODEL FOR STUDENTS GRADE PREDECTION USING MACHINE LEARNING", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 3, page no.832 - 836, March-2024, Available :https://ijsdr.org/papers/IJSDR2403117.pdf
Volume 9
Issue 3,
March-2024
Pages : 832 - 836
Paper Reg. ID: IJSDR_210531
Published Paper Id: IJSDR2403117
Downloads: 000347088
Research Area: Engineering
Country: vijayawada, Andhra Pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
