← Back to all news
arXiv Computation and Language By Minghang Liu, Yuanzhuo Wang, Qiang Qiu, Huawei Shen, Xueqi Cheng

Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis

Read the original on arXiv Computation and Language →

The paper introduces Process-aware Language Cognitive Diagnosis (PLCD), a framework that replaces traditional ID-based embeddings in Cognitive Diagnosis Models with language-derived structures and response records. PLCD employs large language models to build concept schemas and cognitive process graphs, and uses a Language-to-Cognition Mapper with DA-MoE experts and contrastive learning to map textual evidence into a unified cognitive space. Experiments demonstrate that PLCD outperforms conventional baselines in student performance prediction and shows strong cognitive transfer, improving cold-start robustness and cognitive grounding.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv Machine Learning
Oct 22

SkillEval: Learning Interpretable Ability Profiles of LLMs via Cognitive Diagnosis Models

SkillEval introduces a new evaluation framework for large language models that moves beyond single scalar scores to perform cross-model, skill-level analysis. It automatically discovers interpretable skills from benchmark questions using LLM meta‑cognition and applies cognitive diagnosis models to estimate per‑skill abilities. Experiments on 3,811 LLMs across five benchmarks show that SkillEval yields more calibrated, informative model profiles, enabling tasks such as efficient LLM routing and adaptive testing.

By Berke Arda, Peng Cui, Qiaoyuan Zheng, Rudolf Debelak, Mubashara Akhtar, Mrinmaya Sachan
llmsbenchmarks
More like this →
arXiv AI
Aug 26

Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing

The paper introduces LT‑MKT, a new method for multi‑domain knowledge tracing that incorporates cognitive load and knowledge transfer. It constructs a multi‑domain hierarchical graph using textual information from questions and concepts, then explicitly models cross‑domain temporal and knowledge features to capture cognitive load effects. A knowledge transfer module further captures propagation of knowledge states within and across domains, leading to more accurate predictions of students’ future performance.

By Haotian Zhang, Shucun Wang, Jinze Wu, Liang Ding, Shuochen Liu, Zhenya Huang, Jing Sha, Shijin Wang, Qi Liu
llmsbenchmarks
More like this →
arXiv AI
Oct 27

Embedding-Based Educational Taxonomies

The paper explores using semantic text embeddings to automatically build educational taxonomies by clustering math question embeddings. It compares these embedding‑based taxonomies to existing Common Core State Standards (CCSS) taxonomies in two ASSISTments datasets, finding substantial but incomplete overlap. When applied to Deep Knowledge Tracing, the new taxonomies improve predictive performance by about 3‑5 AUC percentage points.

By Kia Karbasi, Kaveh Karbasi, Gregory Pottie
rag
More like this →