Hi,
This is a small project I started recently, which, from a report, extracts users who started and finished a quiz attempt from the same IP.
I'm sharing the Google Sheets link so everyone can access it and make contributions for improvements and new ideas.
Google Sheets: https://docs.google.com/spreadsheets/d/1il7CD40xiykMBpUAHMLTLh0EciNSIdcgmRol5W4attw/edit?usp=sharing
To edit the worksheet, you should make a copy and edit it.
Moodle
(1) Access the quiz
(2) Under "Actions menu", click on "Logs" link
(3) Select a specific date
(4) Download as "CSV" file
Google Sheet
(1) Select worksheet "Dados"
(2) Under "File", click on "Import"
(3) Upload the downloaded CSV file
(4) "Import File" dialog box: select "Sustitute actual page" and untick "Convert..."
(5) Click "import data" button
(6) Access "Indício de Fraude" worksheet
(7) Under "Suspicious IP" select IP
(8) "Full Name" will show all users. if any, accessing the same IP selected before
All the best,
Ricardo
Hi Ricardo, this sounds really cool! Can I ask for some more context?
Referring to what you said about "users who started and finished a quiz attempt from the same IP," does this mean that if multiple quizzes are being completed or attempted from the same IP, it's considered potential fraud?
Thanks for sharing! 👏
Referring to what you said about "users who started and finished a quiz attempt from the same IP," does this mean that if multiple quizzes are being completed or attempted from the same IP, it's considered potential fraud?
Thanks for sharing! 👏
Hi, Frederico.
After the return of the pandemic, we saw a 7x increase in access to Moodle.
From then on, several teachers began to administer online tests, replacing face-to-face tests.
As a result, we found ourselves faced with a new challenge: how to identify potential fraud when taking online tests.
The approach we are adopting here with this project is to identify which students are accessing the same IP when taking the online test. We are assuming that students are accessing the online test from their home network and not from the university network.
This is not the only data we are collecting to confirm the occurrence of fraud. We are also analyzing identical answers, the time at which the online test was taken, and the student's time spent on the online course.
The idea here is to provide the teacher with a set of information to help him make decisions regarding fraud.
Any new ideas and improvements will be very welcome.
Ricardo
After the return of the pandemic, we saw a 7x increase in access to Moodle.
From then on, several teachers began to administer online tests, replacing face-to-face tests.
As a result, we found ourselves faced with a new challenge: how to identify potential fraud when taking online tests.
The approach we are adopting here with this project is to identify which students are accessing the same IP when taking the online test. We are assuming that students are accessing the online test from their home network and not from the university network.
This is not the only data we are collecting to confirm the occurrence of fraud. We are also analyzing identical answers, the time at which the online test was taken, and the student's time spent on the online course.
The idea here is to provide the teacher with a set of information to help him make decisions regarding fraud.
Any new ideas and improvements will be very welcome.
Ricardo
Thanks for the explanation, Ricardo.