Identifying at-risk students based on the phased prediction model
Identifying at-risk students based on the phased prediction model
复制标题
基于分阶段预测模型识别高风险学生
DOI：
10.1007/s10115-019-01374-x
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发表时间：
2020-03-01
影响因子：
2.7
通讯作者：
Liu, Min
中科院分区：
文献类型：
--
作者：
Chen, Yan;Zheng, Qinghua;Liu, Min
识别有风险的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，学生表现出不同的在线学习行为。因此，我们提出了一个阶段性预测模型来预测在一个学期的不同阶段的风险学生。我们分析了学生的个体特征和在线学习行为，提取了与其学习成绩密切相关的特征，并提出了基于时间窗约束策略和学习时间阈值约束策略的组合特征集。实验结果表明，该模型在不同阶段的预测精度为90.4%~ 93.6%。
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%.