Identifying at-risk students is one of the most important issues in online education. During different stages of a semester, students display various online learning behaviors. Therefore, we propose a phased prediction model to predict at-risk students at different stages of a semester. We analyze students’ individual characteristics and online learning behaviors, extract features that are closely related to their learning performance, and propose combined feature sets based on a time window constraint strategy and a learning time threshold constraint strategy. The results of our experiments show that the precision of the proposed model in different phases is from 90.4 to 93.6%.

中文翻译：

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基于分阶段预测模型识别高风险学生

识别高危学生是在线教育中最重要的问题之一。在一个学期的不同阶段，学生会显示各种在线学习行为。因此，我们提出了一个阶段性的预测模型来预测一个学期不同阶段的高风险学生。我们分析学生的个人特征和在线学习行为，提取与他们的学习表现密切相关的特征，并基于时间窗约束策略和学习时间阈值约束策​​略提出组合特征集。实验结果表明，该模型在不同阶段的精度为90.4至93.6％。