arXiv cs.CY· Yin Yi, Haiping Ma, Siyu Song, Ziwen Wang, Shangshang Yang·· 11 小时前AI 评分29
TIZSKT：以文本为索引的零样本跨域知识追踪
Text as an Index for Zero-Shot Cross-Domain Knowledge Tracing
AI 导读
TIZSKT 将文本作为索引而非可直接迁移的预测表示，把行为序列建模与域内历史证据构建分离，实现零样本跨域知识追踪。其 Mastery-Dynamics Encoder 不依赖题目 ID 或文本嵌入学习正确性动态，Domain-Local Question Addressing 用冻结文本编码器加无监督域内投影生成固定题目地址。
正文 · 原文
Abstract:Knowledge tracing (KT) predicts a student's next response from their history. New platforms often have question texts but no response logs for training, motivating zero-shot cross-domain KT. Existing transfer and cold-start methods use question semantics, but cross-platform semantic discrepancies limit transferability. We propose TIZSKT, which separates behavioral sequence modeling from domain-local historical evidence construction and uses text as an index rather than a directly transferable predictive representation. The Mastery-Dynamics Encoder learns correctness dynamics without question identifiers or text embeddings. Domain-Local Question Addressing builds fixed question addresses using a frozen text encoder and an unsupervised per-domain projection. The Text-Addressing Kernel uses these addresses to aggregate the current student's previous outcomes into a question-specific historical correctness summary. Cross-Segment Continuation retains access to earlier responses without extending the behavioral encoder's input sequence. Evidence-Volume Read-Out preserves the total kernel mass discarded by normalization and combines its log-compressed value with the behavioral state and historical correctness summary to predict the next response. All trainable parameters are learned from source domains; target inference uses public question texts and the current student's observed history without target-domain training. Leave-one-domain-out experiments on three public datasets show that TIZSKT achieves the highest mean AUC and accuracy among evaluated baselines on all target domains. Ablations confirm the contributions of text addressing and cross-segment continuation.
| Comments: | 10 pages, 5 figures |
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.41883 [cs.CL] |
| (or arXiv:2610.41883v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41883 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shangshang Yang [view email]
[v1]
Fri, 30 Oct 2026 15:29:08 UTC (662 KB)
来源：arXiv cs.CY · arxiv.org