Applications of deep learning in mathematics education: A review

Abstract

Traditional classroom settings often fail to account for individual differences, leading to disparities in student performance. Deep learning models offer a solution by analyzing performance data to provide targeted interventions and immediate feedback, thereby personalizing educational experiences. This review examines the transformative potential of deep learning in mathematics education through various applications and methodologies. Studies show that deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can analyze complex student data, identify learning patterns, and offer real-time, personalized support. Notable applications include automated classroom feedback systems, intelligent tutoring systems, and predictive models for student performance. Despite the promising results, challenges such as data quality, computational resources, model interpretability, and potential biases remain. Future research should focus on creating inclusive datasets, improving computational efficiency, and enhancing model transparency to fully harness the benefits of deep learning in educational settings.