# Expansion on Exploring Common Trends in Online Educational Experiments
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[*Expansion on Exploring Common Trends in Online Educational Experiments*][doi] contains the *50 Experiments+2022* dataset, providing additional information to the *50 Experiments-2021* dataset released with [*Exploring Common Trends in Online Educational Experiments*][odoi]. The expansion contains the raw problem logs associated with the provided experiments and raw student actions prior to the experiment period.
This project serves as a environment setup for integrating with both datasets by downloading them from their OSF projects ([original][oosf] and [expansion][doi]) and merging them into the same subfolder structure. This also downloads the accompanying documentation and licenses.
This was presented at the [*2022 Conference on Digital Experimentation @ MIT (CODE@MIT)*][code].
## License
The content of this Open Science Foundation project is licensed under a [Creative Commons Attribute 4.0 International License][cl]. As the dataset is pulled from ASSISTments, this project is compliant under the [ASSISTments Public Data License v0.1][dl].
The software of this Open Science Foundation project is licensed under the [MIT License][sl]. This is to prevent distribution issues when using the source in other projects. You can read more about this [here][ccsoftware].
## Environment Setup Script
### Method 1: Docker
The [Docker Container][container] can be run using:
```bash
docker run -v ${PWD}:/app/env ahaim5357/10.17605-osf.io-m2jqe:mitcode2022
```
You can also clone this repository, then run the following [Docker][docker] commands:
```bash
docker build -t <image_name> .
docker run -v ${PWD}:/app/env <image_name>
```
Where `image_name` can be specified to whatever identifier the user desires.
This will automatically download the datasets, dataset documentation, and content/dataset license.
#### Environment Variables
There are three environment variables:
* `M2JQE_RAW_ZIP` (default false): When true, will download the raw project zip onto the local machine if it's not already present. The project will be pulled regardless for setup.
* `M2JQE_DOCS` (default true): When true, will download the dataset documentation and the content/dataset license.
* `M2JQE_MULTIPROCESS` (default false): When true, will execute the setup of the original and expansion dataset in two separate subprocesses.
These can be added to the docker script via `-e <ENV_VAR>=<VALUE>`:
```bash
# Download the raw zip, no docs, and setup using two separate subprocesses on the local machine
docker run -v ${PWD}:/app/data -e M2JQE_RAW_ZIP=true -e M2JQE_DOCS=false -e M2JQE_MULTIPROCESS=true <image_name>
```
### Method 2: Python Environment
The compiler script is written in Python 3.8.10. You can install the required libraries using the `requirements.txt` provided:
```bash
pip install -r requirements.txt
```
> You may need to prefix the `pip` command with either `python -m` for Unix systems or `py -m` for Windows systems if `pip` was not properly installed onto the path.
Then navigate to the folder in your terminal and run `env_setup.py`.
For Unix Systems (Linux, MacOS):
```bash
python3 ./env_setup.py
```
For Windows Systems:
```pwsh
py ./env_setup.py
```
#### Command Line Arguments
There are four command line arguments that can be specified after the script:
* `-h`/`--help`: Prints the command and what arguments it can take.
* `-r`/`--raw-zip`: Downloads the raw project zip onto the local machine if it's not already present.
* `-n`/`--no-docs`: Will not download the supplemental documents with the environment setup.
* `-m`/`--multiprocess`: Will execute the setup of the original and expansion dataset in two separate subprocesses.
For Unix Systems (Linux, MacOS):
```bash
# Download the raw zip, no docs, and setup using two separate subprocesses on the local machine
python3 ./env_setup.py -r -n -m
```
For Windows Systems:
```pwsh
# Download the raw zip, no docs, and setup using two separate subprocesses on the local machine
py ./env_setup.py -r -n -m
```
## Citation
### *50 Experiments-2021*
```
@inproceedings{50experimentsoriginal,
author = {Ethan Prihar and
Manaal Syed and
Korinn Ostrow and
Stacy Shaw and
Adam Sales and
Neil Heffernan},
title = {{Exploring Common Trends in Online Educational
Experiments}},
booktitle = {{Proceedings of the 15th International Conference
on Educational Data Mining}},
year = 2022,
pages = {27--38},
publisher = {International Educational Data Mining Society},
month = jul,
venue = {Durham, United Kingdom},
doi = {10.5281/zenodo.6853041},
url = {https://doi.org/10.5281/zenodo.6853041}
}
```
### *50 Experiments+2022*
```
Haim, A., Prihar, E., Shaw, S. T., Sales, A., & Heffernan, N. T., III. (2022, October 20). Expansion on Exploring Common Trends in Online Educational Experiments. Presented at the 2022 Conference on Digital Experimentation @ MIT (CODE@MIT '22), October 20-21, 2022, Boston, MA, US. Accessible at https://doi.org/10.17605/osf.io/m2jqe
```
[docker]: https://www.docker.com/
[container]: https://hub.docker.com/repository/docker/ahaim5357/10.17605-osf.io-m2jqe
[doi]: https://doi.org/10.17605/osf.io/m2jqe
[odoi]: https://doi.org/10.5281/zenodo.6853041
[oosf]: https://doi.org/10.17605/osf.io/59shv
[code]: https://ide.mit.edu/events/2022-conference-on-digital-experimentation-mit-codemit/
[cl]: https://osf.io/qykhb
[dl]: https://osf.io/s7ufj
[sl]: ./LICENSE
[ccsoftware]: https://creativecommons.org/faq/#can-i-apply-a-creative-commons-license-to-software
