# Language Representation Favored Zero-Shot Cross-Domain Cognitive Diagnosis

arXiv: https://arxiv.org/abs/2501.13943 · HTML: https://mlsanity.com/paper/2501.13943/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [On Softmax Direct Preference Optimization for Recommendation](https://mlsanity.com/paper/2406.09215/) (2024)
- [simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing](https://mlsanity.com/paper/2302.06881/) (2023)
- [PMG : Personalized Multimodal Generation with Large Language Models](https://mlsanity.com/paper/2404.08677/) (2024)
- [Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems](https://mlsanity.com/paper/2404.11290/) (2024)
- [Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives](https://mlsanity.com/paper/2312.13434/) (2023)
- [MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs](https://mlsanity.com/paper/2304.02205/) (2023)
- [Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm](https://mlsanity.com/paper/2309.00300/) (2023)
- [Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems](https://mlsanity.com/paper/2401.10840/) (2023)
- [ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems](https://mlsanity.com/paper/2407.17476/) (2024)
- [Enhancing Top-N Item Recommendations by Peer Collaboration](https://mlsanity.com/paper/2111.00429/) (2021)

## Built on by (most cited first, 1 of 1)

- [Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling](https://mlsanity.com/paper/2604.04088/) (2026)

## Abstract (arXiv)

Cognitive diagnosis aims to infer students' mastery levels based on their historical response logs. However, existing cognitive diagnosis models (CDMs), which rely on ID embeddings, often have to train specific models on specific domains. This limitation may hinder their directly practical application in various target domains, such as different subjects (e.g., Math, English and Physics) or different education platforms (e.g., ASSISTments, Junyi Academy and Khan Academy). To address this issue, this paper proposes the language representation favored zero-shot cross-domain cognitive diagnosis (LRCD). Specifically, LRCD first analyzes the behavior patterns of students, exercises and concepts in different domains, and then describes the profiles of students, exercises and concepts using textual descriptions. Via recent advanced text-embedding modules, these profiles can be transformed to vectors in the unified language space. Moreover, to address the discrepancy between the language space and the cognitive diagnosis space, we propose language-cognitive mappers in LRCD to learn the mapping from the former to the latter. Then, these profiles can be easily and efficiently integrated and trained with existing CDMs. Extensive experiments show that training LRCD on real-world datasets can achieve commendable zero-shot performance across different target domains, and in some cases, it can even achieve competitive performance with some classic CDMs trained on the full response data on target domains. Notably, we surprisingly find that LRCD can also provide interesting insights into the differences between various subjects (such as humanities and sciences) and sources (such as primary and secondary education).