增量知识图谱学习数字孪生：实现有证据支撑的学业预警
An Incremental Knowledge-Graph Learner Digital Twin for Evidence-Grounded Early Warning
AI 导读
一篇 arXiv 论文提出增量式学习数字孪生（Learner Digital Twin），以轻量知识图谱实现有证据支撑的学业风险预警。该孪生在时间检查点通过重放学习轨迹和评估记录更新，既作特征提取基座也作解释依据，在公开学习分析基准的时间协议下评估，图中介特征提取保持与原始表特征相当的预测表现。生成的预警被实体化为证据链，将风险决策关联到特征归因、证据项和原始平台记录，增加可追溯性与可验证性。
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