Oct 04, 2023
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Service categories
Service Lines
Artificial Intelligence
Big Data
Cloud Consulting
Domain focus
Education
Legal
Programming language
Python
Subcategories
Artificial Intelligence
Machine Learning
Big Data
Data Analytics
Text Analytics
Cloud Consulting
SaaS
Challenge
Plagiarism remains a pressing challenge for universities and educators. Identifying suspicious exam scripts among hundreds is time-consuming. Legitimate commonalities further complicate manual review, requiring a solution that can efficiently pinpoint potential cases of academic dishonesty.
Tailored for universities, Plagiarix swiftly processes large exams, presenting instructors with highly similar pairs for manual inspection. Based on Silk Data's advanced platform, it acts as a filter, efficiently detecting potential plagiarism cases, and allowing educators to focus on maintaining academic integrity effortlessly.
Plagiarism remains a pressing challenge for universities and educators. Identifying suspicious exam scripts among hundreds is time-consuming. Legitimate commonalities further complicate manual review, requiring a solution that can efficiently pinpoint potential cases of academic dishonesty.
Tailored for universities, Plagiarix swiftly processes large exams, presenting instructors with highly similar pairs for manual inspection. Based on Silk Data's advanced platform, it acts as a filter, efficiently detecting potential plagiarism cases, and allowing educators to focus on maintaining academic integrity effortlessly.
Solution
Process several hundreds of exam scripts in just a matter of hours, finding for the instructors only few highly similar pairs of exam scripts for manual inspection.
Extraction from PDF and other formats, text comparison engine, and visualization of results for analysis.
Because the exam scripts may contain legitimate common pieces, the final solution acts more like a filter: it locates suspicious pairs of documents that reach a specified similarity threshold.
Process several hundreds of exam scripts in just a matter of hours, finding for the instructors only few highly similar pairs of exam scripts for manual inspection.
Extraction from PDF and other formats, text comparison engine, and visualization of results for analysis.
Because the exam scripts may contain legitimate common pieces, the final solution acts more like a filter: it locates suspicious pairs of documents that reach a specified similarity threshold.
Results
As a result of APT and Silk Data collaboration, APT was able to minimize the involvement of their instructors. Without automated text comparison provided by Silk Data, such analysis was impossible within reasonable timeframes. The implemented solution can be easily implemented for similar cases in education and professional training. Silk Data technology is mainly task- and language-independent, therefore adapting it to other use-cases will only require support of specific document formats.
As a result of APT and Silk Data collaboration, APT was able to minimize the involvement of their instructors. Without automated text comparison provided by Silk Data, such analysis was impossible within reasonable timeframes. The implemented solution can be easily implemented for similar cases in education and professional training. Silk Data technology is mainly task- and language-independent, therefore adapting it to other use-cases will only require support of specific document formats.