I. Introduction.
The plagiarism checking system plays a vital role in the academic world, as it detects duplicates by comparing textual content such as citations and abstracts cited by students in the text, and judges the quality of the text. However, in reality, many students find that their plagiarism results vary widely, sometimes dramatically. This article will delve into the reasons for this phenomenon.
Second, the detection principle.
First of all, we need to understand the basic principles of the plagiarism checking system. The system usually compares students with existing literature databases through large-scale database comparisons to find duplicates. However, this text-matching-based detection method is not foolproof.
3. Influencing factors.
1.Database updating: First of all, if the database of the plagiarism checking system is not updated in a timely manner, then it may not accurately reflect the true repetition rate of **. This is because new research literature and online resources are emerging every day, and if the plagiarism checking system does not update the database in a timely manner, it will lead to biased detection results.
2.Revisions: Students may rephrase parts of the content when they make changes, and the plagiarism checking system does not recognize this change. As a result, even if the original text does not exist in the database, the repetition rate will be greatly reduced as long as it is reorganized by the student.
3.*Content differences: Some students' ** content may be quite different from the literature referenced, but due to the influence of formatting, font, spacing and other factors, the plagiarism check results are high.
4.Limitations of system algorithms: Although the current plagiarism checking system has undergone a lot of data training and algorithm optimization, there are still certain limitations. For example, for some complex long sentences, the system may not be able to accurately determine which parts are quoted and which parts are original to the student.
5.Machine learning bias: Although most of the current plagiarism checking systems are based on machine learning methods, these models often have a certain bias, which can lead to bias in detection results.