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, student behavior detection, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. We then analyze the practical challenges in EDM and propose targeted solutions. 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，它利用计算技术的力量来分析教育数据。随着教育数据日益复杂和多样化，深度学习技术在应对与此类数据分析和建模相关的挑战方面展现出显著优势。本综述旨在系统回顾深度学习在教育数据挖掘中的最新进展。我们首先简要介绍教育数据挖掘与深度学习，并强调其在现代教育背景下的相关性。接着，我们详细回顾了应用于四种典型教育场景的深度学习技术，包括知识追踪、学生行为检测、成绩预测和个性化推荐。此外，本文提供了教育数据挖掘领域公开数据集与处理工具的全面概述。随后，我们分析了教育数据挖掘中的实际挑战并提出了针对性的解决方案。最后，我们指出了该研究领域的新兴趋势与未来发展方向。