Gradescope’s Role in Detecting Cheating: A Detailed Analysis
Gradescope has become an integral part of the modern education landscape, providing educators with tools to streamline grading processes and enhance efficiency. While Gradescope primarily focuses on assessment and feedback, questions often arise regarding its ability to detect cheating.
In this article, we delve into the intricacies of how Gradescope addresses cheating concerns, examining its features, limitations, and the role instructors play in maintaining academic integrity.
Is Gradescope Built to Detect Cheating?
Gradescope is not explicitly designed as an anti-cheating tool; rather, its core functionality lies in automating the grading process and offering instructors a platform for efficient assessment.
However, certain features indirectly contribute to maintaining academic integrity.
Plagiarism Detection on Gradescope
One of the ways Gradescope addresses cheating is through the integration of external plagiarism detection tools. These tools can identify instances of copied or improperly cited content, assisting instructors in detecting potential plagiarism.
It’s important to note that the effectiveness of plagiarism detection depends on the specific tools employed by the educational institution.
The functions of GradeScope that identify cheating include:
Similarity Detection: GradeScope compares student contributions and finds similarities between them using machine learning methods. This can assist in identifying cases of cooperation or plagiarism.
Gradescope does offer features that can indirectly contribute to maintaining academic integrity:
1. Plagiarism Detection:
Some educational institutions integrate external plagiarism detection tools with Gradescope. These tools can identify instances of copied or improperly cited content, helping instructors detect potential plagiarism.
2. Manual Review by Instructors:
Instructors can manually review submissions for irregularities or signs of cheating. They may compare answers, writing styles, or patterns across different submissions to identify potential academic dishonesty.
3. Audit Trails:
Gradescope maintains an audit trail of activities, which includes information about when students access assignments and submit their work. Instructors can use this information to identify any suspicious patterns or unusual submission behaviors.
4. Proctoring Solutions:
Some educational institutions use external proctoring solutions for online exams, which may not be directly integrated into Gradescope but can be used in conjunction with it.
These solutions may include features like webcam monitoring, screen recording, and other tools to detect and deter cheating during exams.
5. Gradescope Code Similarity:
Gradescope also offers a Code Similarity feature, which allows instructors to identify similarities in code submissions.
This can be particularly useful in computer science or programming courses where students may collaborate inappropriately on coding assignments.
Can the Instructor Find Out if a Student is Cheating Through Gradescope?
Ultimately, the ability to detect cheating on Gradescope depends on the vigilance of the instructor and the tools integrated into the system.
While Gradescope provides some features that indirectly contribute to identifying cheating, such as plagiarism detection and code similarity analysis, it is not foolproof, and the effectiveness largely relies on manual review and interpretation by instructors.
Conclusion
Gradescope serves as a valuable tool for educators, streamlining grading processes and providing transparency in assessments. While it may not be explicitly built for detecting cheating, the platform offers features and tools that, when used in conjunction with institutional policies and additional anti-cheating measures, can contribute to maintaining academic integrity. Instructors play a crucial role in reviewing submissions, interpreting data, and ensuring that assessments are conducted fairly and honestly.
Frequently Asked Questions
1. Does AI detection exist in Gradescope?
The software assists instructors in swiftly grading and providing meaningful feedback on homework, exams, and coding projects by utilizing optical character recognition (OCR) and artificial intelligence (AI).
2. How is code similarity checked using Gradescope?
The functions of GradeScope that identify cheating include: Similarity Detection: GradeScope compares student contributions and finds similarities between them using machine learning methods. This can assist in identifying cases of cooperation or plagiarism.
3. What is the allowable level of code similarity?
In light of this, a student who receives a high similarity score may not be plagiarizing, but rather be overly dependent on direct quotes or secondary sources. As a general rule of thumb, a score of between 15% and 20% could be appropriate to strive for.
