inscd-tookit 1.3.1

InsCD: A Modularized, Comprehensive and User-Friendly Toolkit for Machine Learning Empowered Cognitive Diagnosis

InsCD: A Modularized, Comprehensive and User-Friendly Toolkit for Machine Learning Empowered Cognitive Diagnosis

Shanghai Institute of AI Education, School of Computer Science and Technology

East China Normal University

InsCD, namely Instant Cognitive Diagnosis (Chinese: 时诊), is a highly modularized python library for cognitive diagnosis in intelligent education systems. This library incorporates both traditional methods (e.g., solving IRT via statistics) and deep learning-based methods (e.g., modelling students and exercises via graph neural networks).

📰 News

• [2025.7.10] InsCD toolkit v1.3 is released. What's New: We implement one new model: Disentangled Graph Cognitive Diagnosis (DisenGCD)
• [2024.8.31] InsCD toolkit v1.2 is released. What's New: We implement two new models: symbolic cognitive diagnosis model (SymbolCD) and hypergraph cognitive diagnosis model (HyperCD)
• [2024.7.14] InsCD toolkit v1.1 is released and available for downloading.
• [2024.4.20] InsCD toolkit v1.0 is released.

🚀 Getting Started

Installation

Git and install with pip:

git clone https://github.com/ECNU-ILOG/InsCD.git
cd <path of code>
pip install .

or install the library from pypi

pip install inscd-tookit==1.3.1

Quick Example

The following code is a simple example of cognitive diagnosis implemented by inscd. We load build-in datasets, create cognitive diagnosis model, train model and show its performance:

inscd_run --model NCDM --datahub_name Math1

If you want to use multi-GPU parallel training, please use the following command-line:

accelerate launch -m inscd_run --model NCDM --datahub_name Math1

🛠 Implementation

We incoporate classical, famous and state-of-the-art methods published or accepted by leading journals and conferences in the field of psychometric, machine learning and data mining. The reason why we call this toolkit "modulaized" is that we not only provide the "model", but also divide the model into two parts (i.e., extractor and interaction function), which enables us to design new models (e.g., extractor of Hypergraph with interaction function of KaNCD). To evaluate the model, we also provide vairous open-source datasets in online or offline scenarios.

List of Models

Model	Release	Paper
Item Response Theory (IRT)	1952	Frederic Lord. A Theory of Test Scores. Psychometric Monographs.
Multidimentional Item Response Theory (MIRT)	2009	Mark D. Reckase. Multidimensional Item Response Theory Models.
Neural Cognitive Diagnosis Model (NCDM)	2020	Fei Wang et al. Neural Cognitive Diagnosis for Intelligent Education Systems. AAAI'20.
Relation Map-driven Cognitive Diagnosis Model (RCD)	2021	Weibo Gao et al. RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems. SIGIR'21.
Knowledge-association Neural Cognitive Diagnosis (KaNCD)	2022	Fei Wang et al. NeuralCD: A General Framework for Cognitive Diagnosis. TKDE.
Knowledge-sensed Cognitive Diagnosis Model (KSCD)	2022	Haiping Ma et al. Knowledge-Sensed Cognitive Diagnosis for Intelligent Education Platforms. CIKM'22.
Cognitive Diagnosis Model Focusing on Knowledge Concepts (CDMFKC)	2022	Sheng Li et al. Cognitive Diagnosis Focusing on Knowledge Concepts. CIKM'22.
Self-supervised Cognitive Diagnosis Model (SCD)	2023	Shanshan Wang et al. Self-Supervised Graph Learning for Long-Tailed Cognitive Diagnosis. AAAI'23.
Disentangled Graph Cognitive Diagnosis (DisenGCD)	2024	Shanshan Wang et al. DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis. NeurIPS'24.
Inductive Cognitive Diagnosis Model (ICDM)	2024	Shuo Liu et al. Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Intelligent Education Systems. WWW'24.
Symbolic Cognitive Diganosis Model (SymbolCD)	2024	Junhao Shen et al. Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems. AAAl'24.
Oversmoothing-Resistant Cognitive Diagnosis Framework (ORCDF)	2024	Shuo Liu et al. ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems. KDD'24.
Hypergraph Cognitive Diagnosis Model (HyperCDM)	2024	Junhao Shen et al. Capturing Homogeneous Influence among Students: Hypergraph Cognitive Diagnosis for Intelligent Education Systems. KDD'24.

List of Build-in Datasets

Dataset	Release	Source
inscd.datahub.Assist17	2018	https://sites.google.com/view/assistmentsdatamining/dataset
inscd.datahub.FracSub	2015	http://staff.ustc.edu.cn/%7Eqiliuql/data/math2015.rar
inscd.datahub.Junyi734	2015	https://www.educationaldatamining.org/EDM2015/proceedings/short532-535.pdf
inscd.datahub.Math1	2015	http://staff.ustc.edu.cn/%7Eqiliuql/data/math2015.rar
inscd.datahub.Math2	2015	http://staff.ustc.edu.cn/%7Eqiliuql/data/math2015.rar
inscd.datahub.Matmat	2019	https://github.com/adaptive-learning/matmat-web
inscd.datahub.NeurIPS20	2020	https://eedi.com/projects/neurips-education-challenge
inscd.datahub.XES3G5M	2023	https://github.com/ai4ed/XES3G5M

Note that we preprocess these datasets and filter invalid response logs. We will continuously update preprocessed datasets to foster the community.

🤔 Frequent Asked Questions

Why I cannot download the dataset when using build-in datasets class (e.g., NeurIPS20 in inscd.datahub)?

Since these datasets are saved in the Google Driver, they may be not available in some countries and regions. You can use proxy and add the following commands in your terminal:

export http_proxy = 'http://<IP address of proxy>:<Port of proxy>'
export https_proxy = 'http://<IP address of proxy>:<Port of proxy>'
export all_proxy = 'socks5://<IP address of proxy>:<Port of proxy>'

💡 Note: These settings are only effective for the current terminal session.