XKT

Multiple Knowledge Tracing models implemented by mxnet-gluon.

The people who like pytorch can visit the sister projects:

• EduKTM
• TKT

where the previous one is easy-to-understanding and the latter one shares the same architecture with XKT.

For convenient dataset downloading and preprocessing of knowledge tracing task, visit Edudata for handy api.

Tutorial

Installation

1. First get the repo in your computer by git or any way you like.
2. Suppose you create the project under your own home directory, then you can use use
   1. pip install -e . to install the package, or
   2. export PYTHONPATH=$PYTHONPATH:~/XKT

Quick Start

To know how to use XKT, readers are encouraged to see

• examples containing script usage and notebook demo and
• scripts containing command-line interfaces which can be used to conduct hyper-parameters searching.

Data Format

In XKT, all sequence is store in json format, such as:

[[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 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.

A demo loading program is presented as follows:

import json
from tqdm import tqdm

def extract(data_src):
    responses = []
    step = 200
    with open(data_src) as f:
        for line in tqdm(f, "reading data from %s" % data_src):
            data = json.loads(line)
            for i in range(0, len(data), step):
                if len(data[i: i + step]) < 2:
                    continue
                responses.append(data[i: i + step])

    return responses

The above program can be found in XKT/utils/etl.py.

To deal with the issue that the dataset is store in tl format:

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

Refer to Edudata Documentation.

Citation

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

@inproceedings{tong2020structure,
  title={Structure-based Knowledge Tracing: An Influence Propagation View},
  author={Tong, Shiwei and Liu, Qi and Huang, Wei and Huang, Zhenya and Chen, Enhong and Liu, Chuanren and Ma, Haiping and Wang, Shijin},
  booktitle={2020 IEEE International Conference on Data Mining (ICDM)},
  pages={541--550},
  year={2020},
  organization={IEEE}
}

Appendix

Model

There are a lot of models that implements different knowledge tracing models in different frameworks, the following are the url of those implemented by python (the stared is the authors version):

• DKT [tensorflow]

• DKT+ [tensorflow*]

• DKVMN [mxnet*]

• KTM [libfm]

• EKT[pytorch*]

More models can be found in here

Dataset

There are some datasets which are suitable for this task, you can refer to BaseData ktbd doc for these datasets