# Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems

arXiv: https://arxiv.org/abs/2404.11290 · HTML: https://mlsanity.com/paper/2404.11290/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning](https://mlsanity.com/paper/2105.09111/) (2021)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [Inductive Matrix Completion Based on Graph Neural Networks](https://mlsanity.com/paper/1904.12058/) (2019)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [Deep Models of Interactions Across Sets](https://mlsanity.com/paper/1803.02879/) (2018)
- [Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2007.12061/) (2020)
- [Exploiting Cognitive Structure for Adaptive Learning](https://mlsanity.com/paper/1905.12470/) (2019)
- [Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach](https://mlsanity.com/paper/2007.04833/) (2020)
- [Inductive Matrix Completion Using Graph Autoencoder](https://mlsanity.com/paper/2108.11124/) (2021)
- [Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems](https://mlsanity.com/paper/2401.10840/) (2023)
- [INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering](https://mlsanity.com/paper/2107.05247/) (2021)

## Built on by (most cited first, 16 of 16)

- [Knowledge is Power: Harnessing Large Language Models for Enhanced Cognitive Diagnosis](https://mlsanity.com/paper/2502.05556/) (2025)
- [Preference Diffusion for Recommendation](https://mlsanity.com/paper/2410.13117/) (2024)
- [A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions](https://mlsanity.com/paper/2407.05458/) (2024)
- [ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems](https://mlsanity.com/paper/2407.17476/) (2024)
- [Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis](https://mlsanity.com/paper/2502.13996/) (2025)
- [A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments](https://mlsanity.com/paper/2410.15054/) (2024)
- [FedCD: A Fairness-aware Federated Cognitive Diagnosis Framework](https://mlsanity.com/paper/2508.01296/) (2025)
- [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)
- [A Forced-Choice Neural Cognitive Diagnostic Model of Personality Testing](https://mlsanity.com/paper/2507.15013/) (2025)
- [Cognitive Structure Generation: From Educational Priors to Policy Optimization](https://mlsanity.com/paper/2508.12647/) (2025)
- [Harnessing LLM for Noise-Robust Cognitive Diagnosis in Web-Based Intelligent Education Systems](https://mlsanity.com/paper/2510.04093/) (2025)
- [Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling](https://mlsanity.com/paper/2604.04088/) (2026)
- [CLEAN: Psychometrically Consistent Incremental Cognitive Diagnosis under Concept-Space Expansion via Architectural Isolation](https://mlsanity.com/paper/2610.02278/) (2026)

## Abstract (arXiv)

Cognitive diagnosis aims to gauge students' mastery levels based on their response logs. Serving as a pivotal module in web-based online intelligent education systems (WOIESs), it plays an upstream and fundamental role in downstream tasks like learning item recommendation and computerized adaptive testing. WOIESs are open learning environment where numerous new students constantly register and complete exercises. In WOIESs, efficient cognitive diagnosis is crucial to fast feedback and accelerating student learning. However, the existing cognitive diagnosis methods always employ intrinsically transductive student-specific embeddings, which become slow and costly due to retraining when dealing with new students who are unseen during training. To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs. Specifically, in ICDM, we propose a novel student-centered graph (SCG). Rather than inferring mastery levels through updating student-specific embedding, we derive the inductive mastery levels as the aggregated outcomes of students' neighbors in SCG. Namely, SCG enables to shift the task from finding the most suitable student-specific embedding that fits the response logs to finding the most suitable representations for different node types in SCG, and the latter is more efficient since it no longer requires retraining. To obtain this representation, ICDM consists of a construction-aggregation-generation-transformation process to learn the final representation of students, exercises and concepts. Extensive experiments across real-world datasets show that, compared with the existing cognitive diagnosis methods that are always transductive, ICDM is much more faster while maintains the competitive inference performance for new students.