Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, undesirable student detecting, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. Finally, we point out emerging trends and future directions in this research area.

翻译：教育数据挖掘（EDM）已成为一个重要的研究领域，它利用计算技术的强大能力分析教育数据。随着教育数据日益复杂化和多样化，深度学习技术在应对数据建模与分析挑战方面展现出显著优势。本综述旨在系统梳理基于深度学习的EDM研究现状。首先，我们简要介绍EDM与深度学习的基本概念，阐述其与现代教育场景的关联性。随后，详细回顾了深度学习技术在四个典型教育场景中的应用，包括知识追踪、异常学生检测、成绩预测和个性化推荐。此外，全面梳理了EDM领域的公开数据集与处理工具。最后，指出了该研究领域的新兴趋势与未来发展方向。