Convenient interface for downloading and preprocessing datasets in education. For more details, please refer to the full documentation.

The datasets include:

• ASSISTments (2009-2010, 2012-2013, 2015, 2017)

  • 2009-2010 [Analysis]
  • 2012-2013 [Analysis(TBA)]
  • 2015 [Analysis]
  • 2017 [Analysis]

• KDD Cup 2010 [Analysis]

• OLI Engineering Statics 2011 [Analysis]

• JunyiAcademy Math Practicing Log [Analysis]

• slepemapy.cz

• synthetic

• math2015 [Analysis]

• EdNet [Analysis]

• pisa2015math [Analysis]

• workbankr

• critlangacq

• math23k [Analysis]

• MOOCCube [Analysis]

• NIPS2020

• OpenLUNA

Your can also visit our datashop BaseData to get those mentioned-above (most of them) datasets.

Except those mentioned-above dataset, we also provide some benchmark dataset for some specified task, which is listed as follows:

• knowledge tracing benchmark dataset
• cognitive diagnosis benchmark dataset

Git and install by pip

pip install -e .

or install from pypi:

pip install EduData

edudata $subcommand $parameters1 $parameters2

To see the help information:

edudata -- --help
edudata $subcommand --help

The cli tools is constructed based on fire. Refer to the documentation for detailed usage.

Before downloading dataset, first check the available dataset:

edudata ls

and get:

assistment-2009-2010-skill
assistment-2012-2013-non-skill
assistment-2015
junyi
...
ktbd
ktbd-a0910
ktbd-junyi
ktbd-synthetic
...

Download the dataset by specifying the name of dataset:

edudata download assistment-2009-2010-skill

In order to change the storing directory, use the following order:

edudata download assistment-2009-2010-skill $dir

For detailed information of each dataset, refer to the docs

In Knowledge Tracing task, there is a popular format (we named it triple line (tl) format) to represent the interaction sequence records:

5
419,419,419,665,665
1,1,1,0,0

which can be found in Deep Knowledge Tracing. In this format, three lines are composed of an interaction sequence. The first line indicates the length of the interaction sequence, and the second line represents the exercise id followed by the third line, where each elements stands for correct answer (i.e., 1) or wrong answer (i.e., 0)

In order to deal with the issue that some special symbols are hard to be stored in the mentioned-above format, we offer another one format, named json sequence to represent the interaction sequence records:

[[419, 1], [419, 1], [419, 1], [665, 0], [665, 0]]

Each item in the sequence represent one interaction. The first element of the item is the exercise id (in some works, the exercise id is not one-to-one mapped to one knowledge unit(ku)/concept, but in junyi, one exercise contains one ku) and the second one indicates whether the learner correctly answer the exercise, 0 for wrongly while 1 for correctly
One line, one json record, which is corresponded to a learner's interaction sequence.

We provide tools for converting two format:

# convert tl sequence to json sequence, by default, the exercise tag and answer will be converted into int type
edudata tl2json $src $tar
# convert tl sequence to json sequence without converting
edudata tl2json $src $tar False
# convert json sequence to tl sequence
edudata json2tl $src $tar

The cli tools to quickly convert the "raw" data of the dataset into "mature" data for knowledge tracing task. The "mature" data is in json sequence format and can be modeled by XKT and TKT(TBA)

# download junyi dataset to junyi/
>>> edudata download junyi
# build knolwedge graph
>>> edudata dataset junyi kt extract_relations junyi/ junyi/data/
# prepare dataset for knwoeldge tracing task, which is represented in json sequence
>>> edudata dataset junyi kt build_json_sequence junyi/ junyi/data/ junyi/data/graph_vertex.json 1000
# after preprocessing, a json sequence file, named student_log_kt_1000, can be found in junyi/data/
# further preprocessing like spliting dataset into train and test can be performed
>>> edudata train_valid_test junyi/data/student_log_kt_1000 -- --train_ratio 0.8 --valid_ratio 0.1 --test_ratio 0.1

This tool only supports the json sequence format. To check the following statical indexes of the dataset:

• knowledge units number
• correct records number
• the number of sequence

edudata kt_stat $filename

In order to better verify the effectiveness of model, the dataset is usually divided into train/valid/test or using kfold method.

edudata train_valid_test $filename1 $filename2 --train_size 0.8 --valid_size 0.1 --test_size 0.1
edudata kfold $filename1 $filename2 --n_splits 5

Refer to longling for more tools and detailed information.

If this repository is helpful for you, please cite our work

@misc{bigdata2021edudata,
  title={EduData},
  author={bigdata-ustc},
  publisher = {GitHub},
  journal = {GitHub repository},
  year = {2021},
  howpublished = {\url{https://github.com/bigdata-ustc/EduData}},
}

Refer to our website and github for our publications and more projects