哪些CS1学生会不及格？利用加权学业动量与交互日志，从计算机系统与架构的学习分析中识别数字标记
Which CS1 Students Will Fail? Identifying Digital Markers from Learning Analytics in Computer Systems and Architecture Using Weighted Academic Momentum and Interaction Logs
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中文总结 AI 辅助
该研究基于284名学生数据，结合加权学业动量等数字标记与传统特征，构建可在学期第5周预警大一CS1课程不及格的高召回模型，贡献了多源数据集、特征消融研究及可部署的可解释模型。
中文摘要 AI 辅助
数字学习平台会生成丰富的行为轨迹（数字标记），这些轨迹有望早期识别学习困难的学生。本文探究传统标记与数字标记的结合能否以足够的召回率预测大一CS1课程（计算机系统与架构）的不及格情况，从而实现及时干预。研究使用撒哈拉以南非洲某大型公立大学2017-2021年的4届队列数据（样本量N=284），开展混合方法的利益相关者征询，确定10个候选因素，将其转化为涵盖人口统计学、自报告调查、Moodle交互日志和连续评估成绩的综合特征集。采用逻辑回归结合5折交叉验证与SMOTE+ENN重采样的系统消融研究显示，最具预测性的特征子集为Base+Demo+LMS：加权学业动量（M=0.1Q1+0.15Q2+0.2Q3+0.55T1）、基础人口统计学（性别、资助情况、新冠疫情队列）及任何LMS活动的二元指标。在保留的测试集上，逻辑回归取得74.7%的准确率、0.742的宏F1值和0.800的AUC。在0.5的默认阈值下，模型识别出87%的不及格学生（召回率=0.87），假阳性率为41%。SHAP分析证实，加权学业动量是最强预测因子，其次是其与LMS参与度的交互作用。结果表明，简单的数字标记可在学期第5周前构建实用的预警系统。本文主要贡献为：（1）多源数据集与利益相关者引导的方法；（2）量化特征组贡献的消融研究；（3）可部署的可解释高召回模型。
英文摘要
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.
发表机构
- University of Zambia（赞比亚大学）
- DataLab Research Group, University of Zambia（赞比亚大学数据实验室研究组）
机构由 AI 辅助整理，请以论文原文为准。
