用学习行为数据提前识别计算机系新生挂科风险，准确率超七成。
Which CS1 Students Will Fail? Identifying Digital Markers from Learning Analytics in Computer Systems and Architecture Using Weighted Academic Momentum and Interaction Logs
- 融合学业进展与平台互动数据，构建加权学业动量模型。
- 五周内可识别87%的潜在不及格学生，召回率高。
- 结果可解释，适合高校部署早期预警系统。
数字学习平台生成丰富的行为痕迹（数字标记），可用于早期识别学习困难的学生。本文研究是否结合传统与数字标记，能有效预测首年计算机系统与架构课程（CS1）的学业失败，以实现及时干预。基于非洲某大型公立大学2017-2021年四届学生数据（N=284），通过混合方法利益相关者调研确定十项候选因素，并构建涵盖人口统计、自评问卷、Moodle互动日志和持续评估成绩的综合特征集。采用逻辑回归与5折交叉验证，结合SMOTE+ENN重采样，进行系统性消融分析。最优特征子集为基线+人口统计+LMS：加权学业动量（M = 0.1Q1 + 0.15Q2 + 0.2Q3 + 0.55T1）、性别、资助情况、疫情入学年级，以及任意学习管理系统活动的二值指标。在独立测试集上，模型达74.7%准确率、0.742宏F1、AUC 0.800。默认阈值0.5下，召回率达0.87，误报率41%。SHAP分析确认加权学业动量为最强预测因子，其次为与LMS参与度的交互作用。结果表明，简单数字标记可在第五周内构建实用早期预警系统。主要贡献包括：(1) 多源数据集与利益相关者引导的方法论；(2) 特征组贡献量化分析；(3) 可解释、高召回率模型可直接部署。
原文摘要 · Abstract (English)
Digital learning platforms generate rich behavioural traces (digital markers) that offer the potential to identify struggling students early. This paper investigates whether a combination of traditional and digital markers can predict failure in a first-year CS1 course (Computer Systems and Architecture) with sufficient recall to enable timely intervention. Using data from four cohorts (2017-2021, N=284) at a large public university in sub-Saharan Africa, we conducted a mixed-methods stakeholder elicitation to identify ten candidate factors. These were operationalised into a comprehensive feature set spanning demographics, self-reported surveys, Moodle interaction logs, and continuous assessment scores. A systematic ablation study using logistic regression with 5-fold cross-validation and SMOTE+ENN resampling revealed that the most predictive feature subset was Base + Demo + LMS: weighted academic momentum (M = 0.1Q1 + 0.15Q2 + 0.2Q3 + 0.55T1), basic demographics (gender, sponsorship, COVID-19 cohort), and a binary indicator of any LMS activity. On a held-out test set, logistic regression achieved 74.7% accuracy, 0.742 macro F1, and an AUC of 0.800. At the default threshold of 0.5, the model identified 87% of failing students (recall = 0.87) with a 41% false positive rate. SHAP analysis confirmed that weighted academic momentum is the strongest predictor, followed by its interaction with LMS engagement. These results demonstrate that simple digital markers can power a practical early-warning system by the fifth week of the semester. Our main contributions are: (1) a multi-source dataset and a stakeholder-guided methodology; (2) an ablation study quantifying feature group contributions; and (3) an interpretable, high-recall model ready for deployment.
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