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DIKT: A Diagnostic and Interpretable Knowledge Tracing Framework for Prediction and Model-based Learning Analysis — arXiv:2611.02774

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02774
License (as archived): CC0-1.0
Archived: 2026-11-03T11:08:26.596Z
SHA-256 of the archived text: 4afbeaa5f937defc86e7975015cbabea0ff6f05bea8a46d687d6d1d53ddad3c0
Stable URL of this page: https://metraipolis.com/documento/dc_685dfaa84347

How to cite

DIKT: A Diagnostic and Interpretable Knowledge Tracing Framework for Prediction and Model-based Learning Analysis — arXiv:2611.02774 — arXiv (en), https://arxiv.org/abs/2611.02774 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_685dfaa84347 (Metraipolis archive dc_685dfaa84347, sha256 4afbeaa5f937defc86e7975015cbabea0ff6f05bea8a46d687d6d1d53ddad3c0)

BibTeX

@misc{dc685dfaa84347,
  title = {DIKT: A Diagnostic and Interpretable Knowledge Tracing Framework for Prediction and Model-based Learning Analysis — arXiv:2611.02774},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02774},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_685dfaa84347; sha256 4afbeaa5f937defc86e7975015cbabea0ff6f05bea8a46d687d6d1d53ddad3c0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02774. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Knowledge tracing is a key component of intelligent educational systems. However, most existing methods mainly focus on response prediction, providing limited support for explaining predicted outcomes and improving students'learning states. This paper proposes Diagnostic and Interpretable Knowledge Tracing (DIKT), which integrates response prediction, skill-level diagnosis, and model-based learning analysis. DIKT generates bounded cognitive-state trajectories and decomposes the final response probability into skill-wise probabilities. The trajectories reveal students'evolving strengths and weaknesses, while the skill-wise probabilities identify the skills that most strongly constrain the prediction. Based on these diagnostic results, DIKT supports direct cognitive-state intervention and effective-learning-duration-based what-if analysis to examine how hypothetical adjustments affect current predictions and subsequent state evolution. DIKT combines Bayesian multi-head attention, conditional variational recurrent inference, a logistic-growth decoder, and multiplicative skill-wise prediction. Experiments on three benchmark datasets show that DIKT outperforms all evaluated baselines, with an average absolute AUC gain of 2.04 percentage points. Other experiments further demonstrate that DIKT supports educationally meaningful diagnostic analysis and provides informative diagnostic indicators for downstream applications, extending knowledge tracing from response prediction to interpretable diagnosis and model-based learning analysis.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02774 — 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 2026-11-03T10:59:46.127Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).