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。随着教育数据的复杂性和多样性的增加，深度学习技术在应对这些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EDM和深度学习，强调它们在现代教育背景下的相关性。接着，我们详细回顾了深度学习技术在四种典型教育场景中的应用，包括知识追踪、不良学生检测、成绩预测和个性化推荐。此外，我们还提供了EDM常用公共数据集和处理工具的全面概述。最后，我们指出了该研究领域的新兴趋势和未来方向。