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Using Process Mining for Learning Resource Recommendation: A Moodle Case Study

机译：使用流程挖掘来学习资源建议：Moodle案例研究

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Nowadays, Learning Management Systems (LMS) play an intrinsic role in education. They gather traces about the learner (course view, wiki view, quiz attempt, etc.) in event logs. These logs offer the opportunity to provide dashboards and analysis on learners. There are several techniques that analyze event logs for different purposes (adaptation, recommendation, performance detection, etc.). Within this framework, our central focus is upon Educational Process Mining technique which generates process models for improving learning resource recommendation.We set forward an architecture leading to discover process models and recommend to the learner not only learning resource but also process models, each of which is relative to a specific learning resource. These models exert a certain influence on the result of learning resource recommendation. One of the reason that endows our work with an original aspect is that it automatically analyses event logs based on multi-features extracted from the learner’s profiles. However, the state of the art works require a manual analysis step based on learning results uniquely. We evaluated the discovered process models grounded on the event logs of Moodle LMS. These event logs contain 42,438 traces of 100 students who learned a course over one semester. Results corroborate the good performance of our work.
机译：如今，学习管理系统（LMS）在教育中发挥着内在的作用。他们在事件日志中收集关于学习者（课程视图，Wiki视图，测验尝试等）的痕迹。这些日志提供了为学习者提供仪表板和分析的机会。有几种技术可以分析不同目的的事件日志（适应，推荐，性能检测等）。在此框架内，我们的中央重点是教育过程挖掘技术，为改善学习资源推荐的过程模型产生了过程模型。我们设置了导致发现流程模型的架构，并推荐给学习者，而不仅要学习资源，还要进程模型，每个架构是相对于特定的学习资源。这些模型对学习资源推荐的结果产生了一定的影响。与原始方面赋予我们的工作的原因之一是，它根据从学习者的配置文件中提取的多个功能自动分析事件日志。然而，最先进的作品需要基于学习结果的手动分​​析步骤唯一。我们评估了在Moodle LMS的事件日志上接地的发现的过程模型。这些事件日志包含42,438名100名学生的痕迹，他们在一个学期中学到了一门课程。结果证实了我们工作的良好表现。

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