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Text as an Index for Zero-Shot Cross-Domain Knowledge Tracing — arXiv:2610.41883

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41883
License (as archived): CC0-1.0
Archived: 2026-11-02T07:12:45.640Z
SHA-256 of the archived text: 9563a4023802b96f95bbd45b1d8993a2dcecd64e5996a8c6db4970d2853a0fd9
Stable URL of this page: https://metraipolis.com/documento/dc_5fb0caa460bc

How to cite

Text as an Index for Zero-Shot Cross-Domain Knowledge Tracing — arXiv:2610.41883 — arXiv (en), https://arxiv.org/abs/2610.41883 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5fb0caa460bc (Metraipolis archive dc_5fb0caa460bc, sha256 9563a4023802b96f95bbd45b1d8993a2dcecd64e5996a8c6db4970d2853a0fd9)

BibTeX

@misc{dc5fb0caa460bc,
  title = {Text as an Index for Zero-Shot Cross-Domain Knowledge Tracing — arXiv:2610.41883},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41883},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5fb0caa460bc; sha256 9563a4023802b96f95bbd45b1d8993a2dcecd64e5996a8c6db4970d2853a0fd9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41883. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Computers and Society (cs.CY); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.41883 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:07:14.207Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).