The recent diffusion of online education (both MOOCs and e-courses) has led to an increased economic and scientific interest in e-learning environments. As widely documented, online students have a much higher chance of dropping out than those attending conventional classrooms. It is of paramount interest for institutions, students, and faculty members to find more efficient methodologies to mitigate withdrawals. Following the rise of attention on the Student Dropout Prediction (SDP) problem, the literature has witnessed a significant increase in contributions to this subject. In this survey, we present an in-depth analysis of the state-of-the-art literature in the field of SDP, under the central perspective, but not exclusive, of machine learning predictive algorithms. Our main contributions are the following: (i) we propose a comprehensive hierarchical classification of existing literature that follows the workflow of design choices in the SDP; (ii) to facilitate the comparative analysis, we introduce a formal notation to describe in a uniform way the alternative dropout models investigated by the researchers in the field; (iii) we analyse some other relevant aspects to which the literature has given less attention, such as evaluation metrics, gathered data, and privacy concerns; (iv) we pay specific attention to deep sequential machine learning methods—recently proposed by some contributors—which represent one of the most effective solutions in this area. Overall, our survey provides novice readers who address these topics with practical guidance on design choices, as well as directs researchers to the most promising approaches, highlighting current limitations and open challenges in the field.

中文翻译：

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在线课程中学生辍学预测的机器学习方法调查

最近在线教育（包括 MOOC 和电子课程）的普及导致对电子学习环境的经济和科学兴趣增加。正如广泛记录的那样，在线学生辍学的机会比参加传统课堂的学生要高得多。机构、学生和教职员工最感兴趣的是找到更有效的方法来减少退学。随着对学生辍学预测（SDP）问题的关注度上升，文献见证了对该主题的贡献显着增加。在本次调查中，我们在机器学习预测算法的中心视角下，对 SDP 领域的最新文献进行了深入分析，但并不排斥。我们的主要贡献如下：(i) 我们建议对现有文献进行全面的层次分类，遵循 SDP 中的设计选择工作流程；(ii) 为了便于比较分析，我们引入了一个正式的符号，以统一的方式描述该领域研究人员研究的替代 dropout 模型；(iii) 我们分析了文献较少关注的其他一些相关方面，例如评估指标、收集的数据和隐私问题；(iv) 我们特别关注深度序列机器学习方法——最近由一些贡献者提出——这是该领域最有效的解决方案之一。总体而言，我们的调查为解决这些主题的新手读者提供了有关设计选择的实用指导，并指导研究人员采用最有前途的方法，