Eyegrade uses a webcam to grade MCQ (multiple choice question)
exams. Needing just a cheap low-end webcam, it aims to be a low-cost
and portable solution available to everyone, on the contrary to other
solutions based on scanners.

For more information about Eyegrade you can visit:

- Its website: http://www.eyegrade.org/
- Its blog: http://www.eyegrade.org/blog/
- The user manual: http://www.eyegrade.org/doc/user-manual/
- Its source code at GitHub: https://github.com/jfisteus/eyegrade

Eyegrade is still alpha. It is fully functional and has been used in
several courses at Universidad Carlos III de Madrid since 2010.
Its use is encouraged for tech-savvy people. However, it still
needs great improvements in its user-friendliness in order to be
usable for the general public.

The program is free software, licensed under the terms of the GNU
General Public License (GPL) version 3 or any later version.

Bug reports, feature requests and pull requests are welcome at the
Github repository:

https://github.com/jfisteus/eyegrade

The main features of Eyegrade are:

- Typesetting exams: Although you can create your exams with other tools,
Eyegrade integrates an utility to creating MCQ exams. It is able to
create your exams in PDF format.  Eyegrade can automatically build
several versions of the exam by reordering questions and choices
within questions.

- Grading exams: Using a webcam, the graphical user interface of
Eyegrade allows you to grade your exam. Eyegrade is able to recognize
not only the answers to the questions, but also the identity of the
student by using its hand-written digit cognition module. The whole
process is supervised by the user in order to detect and fix potential
errors of the system.

- Extracting statistics: you can view question-by-question statistics in
order to analyze which topics are clear to the majority of your
students, and which topics need further work.

- Exporting grades: grades can be exported in CSV format, compatible
with other programs such as spreadsheets.

An article describing Eyegrade has been published by the Journal of
Science Education and Technology:

Jesus Arias Fisteus, Abelardo Pardo and Norberto Fernández García,
"Grading Multiple Choice Exams with Low-Cost and Portable
Computer-Vision Techniques". Journal of Science Education and
Technology. DOI: 10.1007/s10956-012-9414-8.  It is available at:
http://dx.doi.org/10.1007/s10956-012-9414-8

Note for developers: the Eyegrade repository contains two main
branches, 'master' and 'development'. The 'master' branch will be
placed at the latest stable release of Eyegrade. The 'development'
branch will receive commits of yet-to-be-released features. If you
plan to submit pull-requests, base your work on the development branch
to facilitate their integration.