# Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives

arXiv: https://arxiv.org/abs/2312.13434 · HTML: https://mlsanity.com/paper/2312.13434/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [CoNet: Collaborative Cross Networks for Cross-Domain Recommendation](https://mlsanity.com/paper/1804.06769/) (2018)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation](https://mlsanity.com/paper/1707.00383/) (2017)
- [Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing](https://mlsanity.com/paper/2111.12608/) (2021)
- [APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://mlsanity.com/paper/2311.02816/) (2023)

## Built on by (most cited first, 15 of 15)

- [GenFusion: Closing the Loop between Reconstruction and Generation via Videos](https://mlsanity.com/paper/2503.21219/) (2025)
- [Cooperative Classification and Rationalization for Graph Generalization](https://mlsanity.com/paper/2403.06239/) (2024)
- [A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions](https://mlsanity.com/paper/2407.05458/) (2024)
- [Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis](https://mlsanity.com/paper/2502.13996/) (2025)
- [Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling](https://mlsanity.com/paper/2411.02066/) (2024)
- [A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments](https://mlsanity.com/paper/2410.15054/) (2024)
- [DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework](https://mlsanity.com/paper/2509.24821/) (2025)
- [Diffusion-Inspired Cold Start with Sufficient Prior in Computerized Adaptive Testing](https://mlsanity.com/paper/2411.12182/) (2024)
- [Language Representation Favored Zero-Shot Cross-Domain Cognitive Diagnosis](https://mlsanity.com/paper/2501.13943/) (2025)
- [LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis](https://mlsanity.com/paper/2505.13492/) (2025)
- [A Unified LLM-Adaptable Framework for Cold-Start Cognitive Diagnosis](https://mlsanity.com/paper/2505.21239/) (2025)
- [Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor](https://mlsanity.com/paper/2506.11107/) (2025)
- [PEOAT: Personalization-Guided Evolutionary Question Assembly for One-Shot Adaptive Testing](https://mlsanity.com/paper/2512.00439/) (2025)
- [Breaking Robustness Barriers in Cognitive Diagnosis: A One-Shot Neural Architecture Search Perspective](https://mlsanity.com/paper/2601.04918/) (2026)
- [Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling](https://mlsanity.com/paper/2604.04088/) (2026)

## Abstract (arXiv)

Cognitive diagnosis seeks to estimate the cognitive states of students by exploring their logged practice quiz data. It plays a pivotal role in personalized learning guidance within intelligent education systems. In this paper, we focus on an important, practical, yet often underexplored task: domain-level zero-shot cognitive diagnosis (DZCD), which arises due to the absence of student practice logs in newly launched domains. Recent cross-domain diagnostic models have been demonstrated to be a promising strategy for DZCD. These methods primarily focus on how to transfer student states across domains. However, they might inadvertently incorporate non-transferable information into student representations, thereby limiting the efficacy of knowledge transfer. To tackle this, we propose Zero-1-to-3, a domain-level zero-shot cognitive diagnosis framework via one batch of early-bird students towards three diagnostic objectives. Our approach initiates with pre-training a diagnosis model with dual regularizers, which decouples student states into domain-shared and domain-specific parts. The shared cognitive signals can be transferred to the target domain, enriching the cognitive priors for the new domain, which ensures the cognitive state propagation objective. Subsequently, we devise a strategy to generate simulated practice logs for cold-start students through analyzing the behavioral patterns from early-bird students, fulfilling the domain-adaption goal. Consequently, we refine the cognitive states of cold-start students as diagnostic outcomes via virtual data, aligning with the diagnosis-oriented goal. Finally, extensive experiments on six real-world datasets highlight the efficacy of our model for DZCD and its practical application in question recommendation. The code is publicly available at https://github.com/bigdata-ustc/Zero-1-to-3.