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Ability-Residual Decoupled Modeling for Affective Cognitive Diagnosis — arXiv:2609.21214

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21214
License (as archived): CC0-1.0
Archived: 2026-10-10T01:01:43.132Z
SHA-256 of the archived text: bc578069e38cb6bdada0d4f928a2a20fb120f26518e953461b1d4c95d26f03bb
Stable URL of this page: https://metraipolis.com/documento/dc_cdb034199364

How to cite

Ability-Residual Decoupled Modeling for Affective Cognitive Diagnosis — arXiv:2609.21214 — arXiv (en), https://arxiv.org/abs/2609.21214 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cdb034199364 (Metraipolis archive dc_cdb034199364, sha256 bc578069e38cb6bdada0d4f928a2a20fb120f26518e953461b1d4c95d26f03bb)

BibTeX

@misc{dccdb034199364,
  title = {Ability-Residual Decoupled Modeling for Affective Cognitive Diagnosis — arXiv:2609.21214},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21214},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cdb034199364; sha256 bc578069e38cb6bdada0d4f928a2a20fb120f26518e953461b1d4c95d26f03bb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21214. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Cognitive diagnosis infers students' concept mastery from response logs. However, students' responses are not determined by mastery alone: non-cognitive factors such as emotion, engagement, and fatigue can also affect performance. Affective cognitive diagnosis therefore extends conventional cognitive diagnosis by incorporating affective states. Existing methods often assume that the cognitive diagnosis backbone has already explained ability, item, and concept effects, so the remaining errors can be attributed mainly to affect. We argue that this assumption can be insufficient in real educational data: item calibration bias, systematic concept bias, personalized student-concept deviations, and latent student-item matching can form stable cognitive residuals. Without an explicit modeling pathway, these residuals may leak into affective representations, producing affect contamination. To address this problem, we propose an ability-residual decoupled framework for affective cognitive diagnosis. The model first captures unmodeled cognitive residuals through student, item, concept, student-concept, and low-rank student-item components, and then uses an affective module to modulate guess/slip effects. A Q-matrix-constrained concept residual attention mechanism adaptively aggregates only item-relevant concept residuals. Experiments on ASSIST2017, ASSIST2012, ASSIST2009, and Junyi with six cognitive diagnosis backbones show response-prediction gains across the reported comparisons and generally improved affect alignment when affect labels are available. Ablation studies, leakage probes, principal component analysis visualization, long-tail analysis, and case studies further indicate that ability residuals absorb stable cognitive bias, reduce cognitive contamination in the affective branch, and enhance the robustness and predictive accuracy of cognitive diagnosis models.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.21214 — 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 2026-10-10T00:23:42.438Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).