Educational Process Mining (EPM) is a data analysis technique that is used to improve educational processes. It is based on Process Mining (PM), which involves gathering records (logs) of events to discover process models and analyze the data from a process-centric perspective. One specific application of EPM is curriculum mining, which focuses on understanding the learning program students follow to achieve educational goals. This is important for institutional curriculum decision-making and quality improvement. Therefore, academic institutions can benefit from organizing the existing techniques, capabilities, and limitations. We conducted a systematic literature review to identify works on applying PM to curricular analysis and provide insights for further research. We reviewed 27 primary studies published across seven major databases. Our analysis classified these studies into five main research objectives: discovery of educational trajectories, identification of deviations in student behavior, bottleneck analysis, dropout / stopout analysis, and generation of recommendations. Key findings highlight challenges such as standardization to facilitate cross-university analysis, better integration of process and data mining techniques, and improved tools for educational stakeholders. This review provides a comprehensive overview of the current landscape in curricular process mining and outlines specific research opportunities to support more robust and actionable curricular analyses in educational settings.

翻译：教育过程挖掘（EPM）是一种用于改进教育过程的数据分析技术。它基于过程挖掘（PM），通过收集事件记录（日志）来发现过程模型，并从过程中心视角分析数据。EPM的一个具体应用是课程挖掘，其重点在于理解学生为达成教育目标所遵循的学习路径。这对于机构课程决策与质量提升至关重要。因此，学术机构可通过梳理现有技术、能力与局限性而获益。我们开展了系统性文献综述，以识别将PM应用于课程分析的研究工作，并为后续研究提供见解。我们审阅了发表于七大主要数据库的27项核心研究。通过分析，我们将这些研究归纳为五大研究目标：教育轨迹发现、学生行为偏差识别、瓶颈分析、辍学/休学分析以及推荐生成。主要发现强调了若干挑战，例如：需建立标准化框架以促进跨校分析、更好地融合过程挖掘与数据挖掘技术，以及为教育相关方开发更完善的工具。本综述全面概述了课程过程挖掘的当前发展态势，并指明了具体的研究机遇，以支持在教育环境中开展更稳健、更具可操作性的课程分析。