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EduStudio is a Unified and Templatized Framework for Student Assessment Models including Cognitive Diagnosis(CD) and Knowledge Tracing(KT) based on Pytorch.

• We are working hard to reproduce the results presented in their papers for all models. These results will be published later on https://edustudio.ai/.
• We are organizing more comprehensive resources related to student assessment models to build a complete ecosystem for EduStudio.

EduStudio first decomposes the general algorithmic workflow into five steps: configuration reading, data processing, model implementation, training control, and result evaluation. Subsequently, to enhance the reusability of each step, we extract the commonalities of each algorithm at each step into individual templates for templatization.

As illustrated in the Figure below, to better implement a templatized framework, we implement an inheritance-style EduStudio that contains basic architecture and inherited architecture with different responsibilities. The basic architecture emphasizes domain-irrelevant content and strives to build templatized protocols. The inherited architecture obeys the protocol in the basic architecture and focuses on domain-relevant content. The inheritance-style separates domainrelevant and domain-irrelevant content, greatly simplifying framework structure and enhancing readability.

The documentation is available here.

Figure: Overall Architecture of EduStudio

Install EduStudio:

pip install -U edustudio

Example: Run NCDM model:

from edustudio.quickstart import run_edustudio

run_edustudio(
    dataset='FrcSub',
    cfg_file_name=None,
    traintpl_cfg_dict={
        'cls': 'EduTrainTPL',
    },
    datatpl_cfg_dict={
        'cls': 'CDInterExtendsQDataTPL'
    },
    modeltpl_cfg_dict={
        'cls': 'NCDM',
    },
    evaltpl_cfg_dict={
        'clses': ['BinaryClassificationEvalTPL', 'CognitiveDiagnosisEvalTPL'],
    }
)

To find out which templates are used for a model, we can find in the Reference Table

EduStudio uses MIT License.