This Python library offers a streamlined solution for rapidly developing a Computerized Adaptive Testing (CAT) system. It encompasses a comprehensive suite of tools that integrate both traditional statistical methods and recent machine learning and deep learning techniques.
Computerized Adaptive Testing (CAT) stands as one of the earliest and most successful integrations of educational practices and computing technology.
CAT is a dynamic and interactive process between a student and a testing system. If traditional paper-and-pencil tests are "one-for-all," then CAT is "one-for-each". Each student gets a personalized test that adapts to their proficiency level and knowledge, ensuring each question accurately assesses and challenges them. CAT tailors the selection of questions to each student’s level of proficiency, thereby maximizing the accuracy of the assessment while minimizing the test length.
The CAT system is split into two main components that take turns: At each test step, the Cognitive Diagnosis Model (CDM), as the user model, first uses the student’s previous responses to estimate their current proficiency, based on cognitive science or psychometrics. Then, the Selection Algorithm picks the next question from the bank according to certain criteria.This two-step process repeats until a predefined stopping rule is met, and the final estimated proficiency (i.e., diagnostic report) of individual students will be fed back to themselves as the outcome of this assessment or for facilitating future learning.
This repository implements basic functionalities of CAT. It includes the implements three types of CDM: Item Response Theory, Multidimensional Item Response Theory and Neural Cognitive Diagnosis. And each CDM has its corresponding selection algorithm:
- IRT: Item Response Theory
- MFI: Maximum Fisher Information strategy
- KLI: Kullback-Leibler Information strategy
- MAAT: Model-Agnostic Adaptive Testing strategy
- BECAT: Bounded Ability Estimation Adaptive Testing strategy
- BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing strategy
- NCAT: Neural Computerized Adaptive Testing strategy
- MIRT: Multidimensional Item Response Theory
- NCD: Neural Cognitive Diagnosis
It is worth noting that the data needs to be processed before it can be used. In the script/dataset directory, we provide the preprocessing files for the ASSISTment dataset for reference.
To make use of our work, you should do these below:
Git and install by pip
pip install -e .
or install from pypi
pip install EduCAT
See the examples in scripts directory.
By default, we use tensorboard to help visualize the reward of each iteration, see demos in scripts and use
tensorboard --logdir /path/to/logs
to see the visualization result.
Cognitive Diagnosis Model (CDM), as the user model, first uses the student's previous responses to estimate their current proficiency, based on cognitive science or psychometrics.
Then, the \textbf{Selection Algorithm} picks the next question from the \textbf{Question Bank} according to certain criteria \cite{lord2012applications, chang1996global, bi2020quality}. Most traditional statistical criteria are informativeness metrics, e.g., selecting the question whose difficulty matches the student's current proficiency estimate, meaning the student has roughly a 50% chance of getting it right \cite{lord2012applications}. The above process repeats until a predefined stopping rule is met, and the final estimated proficiency (i.e., diagnostic report) of individual students will be fed back to themselves as the outcome of this assessment or for facilitating future learning.
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If this repository is helpful for you, please cite our work
@misc{liu2024survey,
title={Survey of Computerized Adaptive Testing: A Machine Learning Perspective},
author={Qi Liu and Yan Zhuang and Haoyang Bi and Zhenya Huang and Weizhe Huang and Jiatong Li and Junhao Yu and Zirui Liu and Zirui Hu and Yuting Hong and Zachary A. Pardos and Haiping Ma and Mengxiao Zhu and Shijin Wang and Enhong Chen},
year={2024},
eprint={2404.00712},
archivePrefix={arXiv},
primaryClass={cs.LG}
}