Class monitoring tools and IDE are a "killer combination"
Behavior-based cheating detection
By analyzing students' learning behaviors with a code plagiarism checker within the context of assignments or exam questions, Behavior Insights helps instructors identify and tackle plagiarism and academic dishonesty at scale.
Out-of-the-box, Codio’s Behavior Insights comes equipped with indicators specifically configured to detect plagiarism. The intuitive dashboard displays tiles corresponding to indicators above or below predetermined thresholds.
Code plagiarism checker for programming courses
Codio's class monitoring and code plagiarism detector tools enable checking for code copying, improving detection of code plagiarism and potential cases of cheating between class members.
All this can be done with a click of a button. Codio’s code plagiarism checker compares the code projects of all students within a class for a specific teaching unit. Include other reference codes for comparison to use as a code similarity checker. Results are available anytime and last forever.
Develop an evergreen curriculum to prevent plagiarism & cheating
Create many unique programming assessments for a large course in minutes with parameterized assessments. Or assign a bank of pre-existing assessments to students at random to individualize each student's assignments.
In just a few clicks, create and assign different assessments for each student. This individualizes their course materials and saves countless hours in course maintenance and configuration. By giving each student unique programming assessments, you can avoid plagiarism or cheating altogether.
Playback Student Code
Code Playback is built into Codio’s integrated development environment (IDE) and is extremely easy to use. With a few clicks, 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.
Keystroke-driven academic integrity insights
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.
According to the creators of the plagiarism detection tool MOSS, the software checks code for plagiarism in two ways. This is done through keyword analysis and by looking at groups and sequences of words.
However, in some cases, paraphrasing is not detected because the duplicate content in the text may be different enough that it doesn’t get identified as a match. The software also scans for matches by comparing the style of writing and identifying the order of words. This creates a “fingerprint” of the document, making it easier to detect plagiarism.
This combination of methods to detect copied code is the best method to ensure original content.
Your students learn by completing original work and submitting that work for grading and feedback. Codio can help prevent plagiarism and detect cheating. To help ensure academic integrity, Codio’s platform offers the following code cheating and plagiarism checking and prevention tools.
Behavior Insights enable teachers to see at scale whether students have copied and pasted their work from tools like ChatGPT or elsewhere. Our Plagiarism detection tool looks for similarities between student code submissions.
In addition to that, a cheating or plagiarism “scanner”—Code Playback—can help instructors understand how a student writes or produces code. This can help detect plagiarized content in order to promote and maintain academic integrity.
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 Dolos plagiarism detection system developed by CS educators for programming courses. This integration provides 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. The result is a single-click process for the lecturer or teacher.