Text similarity analysis employs natural language processing techniques to compare and evaluate student responses based on their textual content and structure. While primarily focused on identifying similar answer patterns for automated grouping and cheating detection, it can also support grading consistency by highlighting answers that received different scores despite high similarity. The technology faces challenges in distinguishing between legitimate shared knowledge (e.g., definitions from course materials) and potential academic misconduct, and requires careful calibration to focus on meaningful content rather than surface-level similarities.
Prerequisites
- Text Recognition for digitized submissions
- Keyword Extraction for content-focused comparison
In use
Not yet implemented as a standalone feature in grading tools, though similar techniques are used in plagiarism detection systems.