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.
Student academic performance is an important factor in evaluating the effectiveness of the learning process and
identifying students who may require additional academic support. Traditional methods of evaluating student performance
mainly depend on examination marks and teacher observations, which may not provide suffi...
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
Student performance prediction has become an important research area in educational data mining because it
enables educational institutions to identify academically at-risk students and implement timely intervention strategies.
Although machine learning techniques have significantly improved prediction accuracy, many e...
S. P· International Journal of Inn...· 0 citations
Predicting secondary school students' academic accomplishments is crucial for early intervention and personalized learning strategies. This study develops a machine learning-based system to forecast student performance, including grade and percentage prediction, while analyzing the impact of various socio-economic, edu...
Shravani P.Pawar, S. Deshmukh, Priya Chandran· Enterprise Development and M...· 0 citations
: Amid the global acceleration of digital transformation in education, achieving precision teaching and improving student learning outcomes have become central concerns in the educational sector. This study explores the application of machine learning models — Random Forest and Support Vector Machine — in predicting st...
Chenhao Sun, Hewen Sun· Proceedings of the 3rd Inter...· 0 citations
This paper compares three machine learning algorithms—k-Nearest Neighbours (k-NN), Random Forest, as well as Support Vector Machine (SVM)—for predicting high school student performance, using actual exam data from 12,211 students in Jorhat, Assam. The most important decision was to keep all student records (109 absent...
Rinku Mani Kalita, Dr. Siddhartha Baruah· Journal of Intelligent Decis...· 0 citations
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