An ensemble model that combines three ML algorithms Random Forest, K-nearest Neighbors, and ADABOOST is proposed that is higher than the accuracies of the compared models and integrated through a voting mechanism.
A machine learning-based approach for predicting student performance using relevant academic and personal attributes that can assist educational institutions in identifying students who may need additional guidance and support is presented.
S. S, S. R, P. R. et al.· International Journal for Re...· 0 citations
The present paper explores some of the most important determinants of academic achievement and assesses several predictive models based on the Student Performance Factors (SPF) dataset and implies that the monitoring of attendance should become the central element of any academic early warning system.
Shang-Jia Wang· Mathematical Modeling and Al...· 0 citations
An explainable ensemble learning approach for student performance prediction is proposed by integrating Random Forest, XGBoost, and LightGBM classifiers with SHapley Additive exPlanations (SHAP) to improve both predictive performance and model transparency.
S. P· International Journal of Inn...· 0 citations
A machine learning-based system to forecast student performance, including grade and percentage prediction, while analyzing the impact of various socio-economic, educational, personal, and technological factors is developed.
Shravani P.Pawar, S. Deshmukh, Priya Chandran· Enterprise Development and M...· 0 citations
The results show that both random forest and support vector machine achieved high accuracy, but RF demonstrated better recall for identifying at-risk students, making it more suitable for early intervention systems.
Chenhao Sun, Hewen Sun· Proceedings of the 3rd Inter...· 0 citations
This paper compares three machine learning algorithms—k-Nearest Neighbours, Random Forest, as well as Support Vector Machine—for predicting high school student performance, using actual exam data from 12,211 students in Jorhat, Assam to demonstrate the most accurate predictors of student success.
Rinku Mani Kalita, Dr. Siddhartha Baruah· Journal of Intelligent Decis...· 0 citations
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