用智能筛选策略减少数据消耗，提前精准预测学生学业失败。
A Frugal Model for Accurate Early Student Failure Prediction
- 只在必要时引入外部数据，实现数据节约。
- 数据用量减少27%，准确率提升7.3%。
- 适合资源有限的教育机构快速部署。
预测学生学业成败对及时干预和个性化支持至关重要。早期失败预测尤为关键，但初期数据不足带来挑战。现有方法常依赖额外数据，却可能造成资源浪费且效果不升反降。为此，我们提出轻量高效的弗鲁加尔早期预测（FEP）模型，通过智能选择性引入外部数据，在保障性能的同时显著降低数据消耗。在公开的虚拟学习环境（VLE）数据集上的实验表明，相比系统性使用额外数据，FEP可实现27%的数据用量减少；同时相比传统方法，平均准确率提升7.3%。该模型兼顾效率与精度，为追求数据节制的教育机构提供了切实可行的解决方案。
原文摘要 · Abstract (English)
Predicting student success or failure is vital for timely interventions and personalized support. Early failure prediction is particularly crucial, yet limited data availability in the early stages poses challenges, one of the possible solutions is to make use of additional data from other contexts, however, this might lead to overconsumption with no guarantee of better results. To address this, we propose the Frugal Early Prediction (FEP) model, a new hybrid model that selectively incorporates additional data, promoting data frugality and efficient resource utilization. Experiments conducted on a public dataset from a VLE demonstrate FEP's effectiveness in reducing data usage, a primary goal of this research.Experiments showcase a remarkable 27% reduction in data consumption, compared to a systematic use of additional data, aligning with our commitment to data frugality and offering substantial benefits to educational institutions seeking efficient data consumption. Additionally, FEP also excels in enhancing prediction accuracy. Compared to traditional approaches, FEP achieves an average accuracy gain of 7.3%. This not only highlights the practicality and efficiency of FEP but also its superiority in performance, while respecting resource constraints, providing beneficial findings for educational institutions seeking data frugality.
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