arXiv cs.AI· Ngoc Luyen Le, Marie-H\'el\`ene Abel, Bertrand Laforge·· 13 小时前AI 评分18
增量知识图谱学习数字孪生：面向证据可溯源的早期预警
An Incremental Knowledge-Graph Learner Digital Twin for Evidence-Grounded Early Warning
AI 导读
研究提出一种以轻量知识图谱实现的增量学习数字孪生，通过按时间检查点回放学习轨迹与评估记录持续更新，同时作为特征提取与解释依据的底层表示。在公开学习分析基准的时间协议评测中，图介导的特征提取在不同模型族与检查点上保持了与原始表格特征等价的预测表现。生成的预警以证据链形式落地，将风险决策关联到特征归因、证据条目与原始平台记录，从而在保持预测性能的同时增加可追溯性与可验证的解释。
正文 · 原文
Abstract:Early-warning models in learning analytics can identify learners at risk of failure or withdrawal, but the resulting alerts are often only weakly connected to inspectable learning evidence. A risk score may indicate that intervention is needed without showing which traces, assessment events, or temporal patterns justify the alert. This paper introduces an incremental Learner Digital Twin for evidence-grounded early warning, implemented as a lightweight knowledge graph. The twin is updated at temporal checkpoints by replaying learning traces and assessment records, and it serves both as a feature-extraction substrate and as an explanation-grounding substrate. Predictive features are derived through graph-mediated queries, while features, attributions, alerts, evidence items, and raw records are represented as queryable graph entities. The framework is instantiated and evaluated on a public learning-analytics benchmark under a temporal protocol. The results show that graph-mediated feature extraction preserves the predictive behavior of equivalent raw-table features across model families and checkpoints. Generated alerts are materialized as evidence chains linking risk decisions to feature attributions, evidence items, and raw platform records. The study shows that early-warning prediction can be routed through a semantic and provenance-aware representation while preserving predictive performance and adding traceability, verifiability, and evidence-grounded explanation.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36820 [cs.AI] |
| (or arXiv:2610.36820v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36820 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ngoc Luyen Le [view email]
[v1]
Tue, 27 Oct 2026 08:22:26 UTC (253 KB)
来源：arXiv cs.AI · arxiv.org