whigg/visual-analytics-approach-online-proctoring

CHI2021 - A Visual Analytics Approach to Facilitate Proctoring of Online Exams

A Visual Analytics Approach to Facilitate Proctoring of Online Exams

This paper is accepted to CHI21.

How to use it?

Step 1: go the folder "client" and typing in command npm install to install the front end dependencies.

Step 2: unzip all json files in data.zip in server/static

Step 3: run the backend by typing in the command python run-data-backend.py

Step 4: run the frontend by typing in the command npm run serve

Backend package version

Package Name	Version
flask	1.1.1
flask-cors	3.0.8
flask-pymongo	2.3.0
gevent	1.4.0
pandas	0.25.1
pydash	4.8.0
numpy	1.17.2
pymongo	3.11.0

Others

1. Demo data is available upon request.
2. Please use Chrome to open the system
3. To have best experience, please use MacBook Pro 16 and adjust the Zoom level of Chrome to 80%.
4. Since it is a prototype, perhaps it is not perfect. We welcome any suggestions.
5. If you find our code or paper is helpful to your research, please kindly cite our paper as below:

@inbook{li2021proctoring,
author = {Li, Haotian and Xu, Min and Wang, Yong and Wei, Huan and Qu, Huamin},
title = {A Visual Analytics Approach to Facilitate the Proctoring of Online Exams},
year = {2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
articleno = {682},
numpages = {17}
}