• Shashirekha H
• Dr. ChetanaPrakash

Abstract—Recently, significant growth in using online-based learning stream (i.e., e-Learning systems)have been seen due to pandemic such as COVID-19. Forecasting student performance has become a major task as an institution is focusing on improving the quality of education and students’ performance. Data Mining (DM) employing Machine Learning (ML) techniques have been employed in the eLearning platform for analyzing student session streams and predicting academic performance with good effects. A recent, study shows ML-based methodologies exhibit when data is imbalanced. In addressing ensemble learning by combining multiple ML algorithms for choosing the best model according to data. However, the existing ensemble-based model doesn’t incorporate feature importance into the student performance prediction model; Thus, exhibits poor performance, especially for multi-label classification. In addressing this, this paper presents an improved ensemble learning mechanism by modifying the XGBoost algorithm, namely MXGB. The MXGB incorporates an effective cross-validation scheme that learns correlation among features more efficiently. The experiment outcome shows the proposed MXGB-a-based student performance prediction model achieves much better prediction accuracy contrary to the state-of-art ensemble-based student performance prediction model.

E-Learning, Data Imbalance, Ensemble Algorithm, Feature Importance, Machine Learning

"Student Performance Prediction Model using Modified XG Boost algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.c684-c693, February-2023, Available :http://www.jetir.org/papers/JETIR2302289.pdf

"Student Performance Prediction Model using Modified XG Boost algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppc684-c693, February-2023, Available at : http://www.jetir.org/papers/JETIR2302289.pdf