In higher education, data is collected that indicate the term(s) that a course is taken and when it is passed. Often, study plans propose a suggested course order to students. Study planners can adjust these based on detected deviations between the proposed and actual order of the courses being taken. In this work, we detect deviations by combining (1) the deviation between the proposed and actual course order with (2) the temporal difference between the expected and actual course-taking term(s). Partially ordered alignments identify the deviations between the proposed and actual order. We compute a partial order alignment by modeling a study plan as a process model and a student's course-taking behavior as a partial order. Using partial orders in such use cases allows one to relax the constraints of strictly ordered traces. This makes our approach less prone to the order in which courses are offered. Further, when modeling course-taking behavior as partial orders, we propose distinguishing intended course-taking behavior from actual course-passing behavior of students by including either all terms in which a course is attempted or only the term that a course is passed, respectively. This provides more perspectives when comparing the proposed and actual course-taking behavior. The proposed deviation measuring approach is evaluated on real-life data from RWTH Aachen University.

翻译：在高等教育中，收集的数据通常包含课程选修学期与通过学期的信息。学习计划通常会向学生推荐建议的课程修读顺序。学习规划者可以根据检测到的建议顺序与实际修读顺序之间的偏差来调整这些计划。本研究通过结合（1）建议课程顺序与实际课程顺序之间的偏差与（2）预期修读学期与实际修读学期的时间差异来检测偏差。偏序对齐方法可识别建议顺序与实际顺序之间的偏差。我们通过将学习计划建模为过程模型、将学生选课行为建模为偏序来计算偏序对齐。在此类应用场景中使用偏序可以放宽严格有序迹的约束条件，从而使我们的方法更不易受课程开设顺序的影响。此外，在将选课行为建模为偏序时，我们建议通过分别纳入课程尝试的所有学期或仅包含课程通过的学期，来区分学生的预期选课行为与实际通过行为。这为比较建议选课行为与实际选课行为提供了更多视角。所提出的偏差测量方法已在亚琛工业大学的实际数据上进行了评估。