International Journal of Science, Engineering and TechnologyAuthors: Mrs. Mercy Smilin, N. Vigneshwari, A. Naseeha, M. Madhushree, V. Pon Azhagi, P. Raghunanthan, I. Manikandan
Abstract: Student performance prediction is an important application of Artificial Intelligence in the education sector. Academic performance can be influenced by attendance, internal assessment marks, assignment performance, study habits, previous examination results, and other learning-related factors. Traditional evaluation mainly depends on examinations and manual analysis, which may not i dentify students who require academic support at an early stage. This research proposes an AI-based student performance prediction system using machine learning techniques. The system collects relevant student information, preprocesses the data, selects useful features, trains machine learning models, and predicts expected academic performance. Algorithms such as Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine can be applied and compared. The models can be evaluated using suitable performance measures such as accuracy, precision, recall, F1-score, and confusion matrix. The proposed system can help teachers identify students who may need additional guidance and can support data-driven academic planning. The system is intended as a decision-support tool and not as a replacement for teachers. With reliable data and proper evaluation, machine learning can provide useful information for early academic intervention and improved learning support.
DOI: http://doi.org/10.5281/zenodo.23238306
