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. From the analysis of 22 primary studies, we found that results can be classified into five categories concerning the objectives they pursue: the discovery of educational trajectories, the identification of deviations in the observed behavior of students, the analysis of bottlenecks, the analysis of stopout and dropout problems, and the generation of recommendation. Moreover, we identified some open challenges and opportunities, such as standardizing for replicating studies to perform cross-university curricular analysis and strengthening the connection between PM and data mining for improving curricular analysis.

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），后者通过收集事件记录（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实现教育目标所遵循的学习路径。这对于机构的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。通过对22项核心研究的分析，我们发现相关成果可按其目标分为五类：教育轨迹发现、学生观察行为偏差识别、瓶颈分析、休学与辍学问题分析以及推荐生成。此外，我们指出了一些开放挑战与机遇，例如建立标准化流程以复现研究从而实现跨校课程分析，以及加强PM与数据挖掘的关联以提升课程分析效能。