*Toward Improving Effectiveness of Crowdsourced, On-Demand Assistance From Authors in Online Learning Platforms* is a project that ran within the [ASSISTments Platform](assistments.org) from February 16th, 2022 to June 1st, 2022. This attempted to determine between any two authors within the platform which was better at generating on-demand assistance (e.g. hints and explanations). This project contains the preregistration and updates of this project through this time period, the datasets compiled, constructed, and used, and the analysis ran to obtain the specified results.
## External Datasets
This project uses datasets compiled outside of this study:
* [Public Elementary/Secondary School Local Codes](https://nces.ed.gov/ccd/CCDLocaleCode.asp)
* [Inferred Gender of Students ASSISTments](https://osf.io/pm4ux/?view_only=84a5ee4af2f24bbb82a65bc99235684b)
## Analysis
### Method 1: Docker
The [Docker Container](https://hub.docker.com/r/ahaim5357/10.17605-osf.io-zcbjx) can be run using:
docker run --name <container_name> ahaim5357/10.17605-osf.io-zcbjx:xprize
docker cp <container_name>:app/results ./results
You can also clone this repository and run using the following [Docker](https://www.docker.com/) commands:
docker build -t <image_name> .
docker run --name <container_name> <image_name>
docker cp <container_name>:app/results ./results
Where `image_name` and `container_name` are specified as whatever identifiers the user desires.
#### Rerunning the Container
If you need to rerun the container for any reason, you can do so by finding the container id using:
docker ps -a
Afterwards, you can create a new container and run it:
docker commit $<container_id> <new_image_name>
docker run -it --entrypoint=sh <new_image_name>
### Method 2: Python
#### Libraries
The analysis code is written in Python 3.8.10. The following libraries are required to run the analysis. Additionally, the following versions were used to provide the results:
* pandas: 0.25.3
* numpy: 1.17.4
* statsmodels: 0.13.2
* matplotlib: 3.1.2
* seaborn: 0.11.2
#### Datasets
The [Inferred Gender of Students ASSISTments Dataset](https://osf.io/b3c56?view_only=84a5ee4af2f24bbb82a65bc99235684b) needs to be downloaded to run the analysis.
#### Setup
Put the necessary Python files and datasets in the same directory.
(folder name)
|- main.py
|- aosc_constants.py
|- preprocessor.py
|- construct.py
|- interactions.py
|- model.py
|- plot.py
|- student_support_logs.csv
|- student_support_features.csv
|- student_prior_stats.csv
|- infered_student_gender_ASSISTments_2021_Aug_15_2022_Jul_15.csv (Obtained from https://osf.io/b3c56?view_only=84a5ee4af2f24bbb82a65bc99235684b)
|- student_locale_info.csv
|- star_authors.csv
Navigate to (folder name) within your terminal and run `main.py`.
For Unix Systems (Linux, MacOS):
python3 ./main.py
For Windows Systems:
py ./main.py
## Acknowledgements
We would like to thank the NSF (e.g., 2118725, 2118904, 1950683, 1917808, 1931523, 1940236, 1917713, 1903304, 1822830, 1759229, 1724889, 1636782, & 1535428), IES (e.g., R305N210049, R305D210031, R305A170137, R305A170243, R305A180401, & R305A120125), GAANN (e.g., P200A180088 & P200A150306), EIR (U411B190024 & S411B210024), ONR (N00014-18-1-2768), and Schmidt Futures. None of the opinions expressed here are that of the funders. We are funded under an NHI grant (R44GM146483) with Teachly as a SBIR.
