# Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling

arXiv: https://arxiv.org/abs/2411.02066 · HTML: https://mlsanity.com/paper/2411.02066/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 62 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings](https://mlsanity.com/paper/2006.13009/) (2020)
- [Flexibly Fair Representation Learning by Disentanglement](https://mlsanity.com/paper/1906.02589/) (2019)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (2019)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents](https://mlsanity.com/paper/2402.05746/) (2024)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation](https://mlsanity.com/paper/2305.03374/) (2023)
- [Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2007.12061/) (2020)
- [Algorithmic Fairness in Education](https://mlsanity.com/paper/2007.05443/) (2020)
- [Generative Multi-Modal Knowledge Retrieval with Large Language Models](https://mlsanity.com/paper/2401.08206/) (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)
- [Enhancing Cognitive Diagnosis using Un-interacted Exercises: A Collaboration-aware Mixed Sampling Approach](https://mlsanity.com/paper/2312.10110/) (2023)
- [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)
- [ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness](https://mlsanity.com/paper/2401.10749/) (2023)
- [LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation](https://mlsanity.com/paper/2407.02833/) (2024)

## Built on by (most cited first, 3 of 3)

- [Diffusion-Inspired Cold Start with Sufficient Prior in Computerized Adaptive Testing](https://mlsanity.com/paper/2411.12182/) (2024)
- [PEOAT: Personalization-Guided Evolutionary Question Assembly for One-Shot Adaptive Testing](https://mlsanity.com/paper/2512.00439/) (2025)
- [MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing](https://mlsanity.com/paper/2605.08697/) (2026)

## Abstract (arXiv)

Learners sharing similar implicit cognitive states often display comparable observable problem-solving performances. Leveraging collaborative connections among such similar learners proves valuable in comprehending human learning. Motivated by the success of collaborative modeling in various domains, such as recommender systems, we aim to investigate how collaborative signals among learners contribute to the diagnosis of human cognitive states (i.e., knowledge proficiency) in the context of intelligent education. The primary challenges lie in identifying implicit collaborative connections and disentangling the entangled cognitive factors of learners for improved explainability and controllability in learner Cognitive Diagnosis (CD). However, there has been no work on CD capable of simultaneously modeling collaborative and disentangled cognitive states. To address this gap, we present Coral, a Collaborative cognitive diagnosis model with disentangled representation learning. Specifically, Coral first introduces a disentangled state encoder to achieve the initial disentanglement of learners' states. Subsequently, a meticulously designed collaborative representation learning procedure captures collaborative signals. It dynamically constructs a collaborative graph of learners by iteratively searching for optimal neighbors in a context-aware manner. Using the constructed graph, collaborative information is extracted through node representation learning. Finally, a decoding process aligns the initial cognitive states and collaborative states, achieving co-disentanglement with practice performance reconstructions. Extensive experiments demonstrate the superior performance of Coral, showcasing significant improvements over state-of-the-art methods across several real-world datasets. Our code is available at https://github.com/bigdata-ustc/Coral.