Codio detects twice as many cases of code plagiarism than standard tools like MOSS and JPlag
Codio's class monitoring and plagiarism detection tools allow you to check for code copying and potential cases of cheating between members of a class. With a click of a button, Codio will compare the code projects of all students within a class for a specific teaching unit. You can also include other reference code for comparison. Results are available at any time and forever, so you can run a check and always access results.
The VM technology, integrated IDE and class monitoring tools are a killer combination. The time savings for students and faculty are huge.
Learn more about how Codio can save your CS Department and faculty time, money, and headaches!
Code Playback is built into Codio’s integrated development environment (IDE). This feature is available to all teachers and is extremely easy to use. With a few clicks, teachers can access all of the files that students produce in Codio.
Now, when teachers want to see how a particular code file was built, they can simply select Tools → Code Playback and Codio will open the history of the in-focus code file. For a multi-file project, click on the folder icon to pull up the history of other project files too.
Plagiarism detection systems available such as MOSS and JPlag were not developed for university programming courses. Therefore, they can require considerable effort to submit large files of student code projects and to interpret the results.
Codio integrates the Etector plagiarism detection system developed by CS educators for programming courses, providing instructors with enough data and analysis for a lecturer to make a conclusive, final decision. The burden of project data preparation and submission to remote systems such as Moss and JPlag is removed, resulting in a single click process for the lecturer or teacher.
Codio automatically logs all student keystrokes, enabling advanced algorithmic exploration of learner behaviors, easy identification of skill gaps, and previously inaccessible insight into student performance and programming behaviors.