Predicting Student Performance Using Artificial Neural Networks

Abstract

<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of student performance predictions. Future research directions may include further optimization of the model's algorithms and expansion of the data sample size to improve prediction accuracy and applicability. The method demonstrated exceptional performance in predicting student outcomes, offering high accuracy and efficacy. By mining and analyzing extensive educational data, a predictive model was established and validated through experiments. We introduce a novel predictive model to the field of education, providing robust support for student learning and educational decision-making. Future enhancements can optimize the model, increase prediction precision, and expand application fields to better serve the development of educational endeavors.</span></p>