Content assessment has broadly improved in e-learning scenarios in recent decades. However, the e-Learning process can give rise to a spatial and temporal gap that poses interesting challenges for assessment of not only content, but also students’ acquisition of core skills such as self-regulated learning. Our objective was to discover students’ self-regulated learning processes during an e-Learning course by using Process Mining Techniques. We applied a new algorithm in the educational domain called Inductive Miner over the interaction traces from 101 university students in a course given over one semester on the Moodle 2.0 platform. Data was extracted from the platform’s event logs with 21,629 traces in order to discover students’ self-regulation models that contribute to improving the instructional process. The Inductive Miner algorithm discovered optimal models in terms of fitness for both Pass and Fail students in this dataset, as well as models at a certain level of granularity that can be interpreted in educational terms, which are the most important achievement in model discovery. We can conclude that although students who passed did not follow the instructors’ suggestions exactly, they did follow the logic of a successful self-regulated learning process as opposed to their failing classmates. The Process Mining models also allow us to examine which specific actions the students performed, and it was particularly interesting to see a high presence of actions related to forum-supported collaborative learning in the Pass group and an absence of those in the Fail group.

中文翻译：

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用于在线学习中自我调节学习评估的过程挖掘

近几十年来，内容评估在电子学习场景中已得到广泛改善。但是，电子学习过程可能会引起时空差距，这不仅对内容评估，而且对学生获得诸如自我调节学习之类的核心技能的评估都提出了有趣的挑战。我们的目标是通过使用过程挖掘技术，在电子学习课程中发现学生的自我调节学习过程。我们在教育领域应用了一种称为归纳矿工的新算法在一个Moodle 2.0平台上的一学期课程中，来自101位大学生的互动轨迹。从该平台的事件日志中提取了21,629条记录，以发现学生的自我调节模型，这些模型有助于改进教学过程。该感应矿工该算法在此数据集中针对合格和不合格学生的适应性发现了最佳模型，以及可以用教育术语解释的一定粒度级别的模型，这是模型发现中最重要的成就。我们可以得出结论，尽管通过的学生没有完全遵循教师的建议，但他们确实遵循了成功的自我调节学习过程的逻辑，而不是失败的同学。流程挖掘模型还使我们能够检查学生执行了哪些特定操作，尤其有趣的是，Pass组中与论坛支持的协作学习相关的活动大量存在，而Fail组中则没有此类活动。