At the beginning of the 2010 decade, the world of education and more specifically e-learning was revolutionized by the emergence of Massive Open Online Courses, better known by their acronym MOOC. Proposed more and more by universities and training centers around the world, MOOCs have become an undeniable asset for any student or person seeking to complete their initial training with free distance courses open to all areas. Despite the remarkable number of course enrollees, MOOCs have a huge dropout rate of up to 90%. This rate significantly affects the efforts made by the moderators for the success of this pedagogical model and negatively influences the learners’ experience and their supervision. To address this problem and help instructors streamline their interventions, we present a solution to classify MOOC learners into three distinct classes. The approach proposed in this paper is based on the filters methods to select the most relevant attributes and ensembling methods of machine learning algorithms. This approach has been validated by four MOOC courses from Stanford University. In order to prove the performance of the model (92.2%), a comparative study between the proposed model and other algorithms was made on several performance measures.

中文翻译：

---

基于机器学习的增强Mooc用户分类的方法

在2010年代初，大规模开放在线课程的出现彻底改变了教育领域，尤其是电子学习领域，而后者因其缩写MOOC而闻名。MOOC已被世界各地的大学和培训中心越来越多地提出，对于任何希望通过面向所有地区开放的免费远程课程来完成其初始培训的学生或个人而言，MOOC都是不可否认的资产。尽管课程注册人数众多，但MOOC的辍学率高达90％。这个比率极大地影响了主持人为该教学模型的成功所做的努力，并且对学习者的经验及其监督产生了负面影响。为了解决此问题并帮助讲师简化其干预措施，我们提出了一种将MOOC学习者分为三个不同类别的解决方案。本文提出的方法是基于过滤器方法来选择最相关的属性和机器学习算法的组合方法。斯坦福大学的四门MOOC课程对此方法进行了验证。为了证明该模型的性能（92.2％），在几种性能指标上对该模型与其他算法进行了比较研究。