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Stylometry subsumes statistical methods for quantifying an author’s unique writing style and is mainly used for authorship attribution or intrinsic CaPD. By constructing and comparing stylometric models for different text segments, passages that are stylistically different from others, hence potentially plagiarized, can be detected. There are two main forms of metric stylometry, they are intrinsic and external. Intrinsic is used for identifying the passages plagiarized by looking at only the analyzed document, deciding and checking to make sure that parts of the material are or not written by the same author. Intrinsic plagiarism identification technique uses the writing style of an author as a basis for comparison External involves comparing the document with other existing documents within the database of material and identifying the pair of similar documents.[Bag of words analysis represent the adoption of vector space retrieval, a traditional IR concept, to the domain of plagiarism detection. Documents are represented as one or multiple vectors, e.g. for different document parts, which are used for pair wise similarity computations. The vector space model focuses on finding more weights for terms that do not frequently exist in the dataset

The figure below represents a classification of all detection approaches currently in use for computer-assisted plagiarism detection. That have been explained in detail throughout the article. The approaches are characterized by the type of similarity assessment they undertake: global or local. Global similarity assessment approaches use the characteristics taken from larger parts of the text or the document as a whole to compute similarity, while local methods only examine pre-selected text segments as input.

Self-efficiency is a big of students learning abilities when it comes to schoolwork and any learning activities. Self-efficiency for learning refers to students believe in their capabilities. Self-efficiency is important in the learning process. Perecieved academic self-efficiency is when students believe they have the skills necessary for successful learning. A study was conducted to test the effectiveness of plagiarism detection software in a higher education setting. One part of the study assigned one group of students to write a paper. These students were first educated about plagiarism and informed that their work was to be run through a plagiarism detection system. A second group of students was assigned to write a paper without any information about plagiarism. The researchers expected to find lower rates in group one but found roughly the same rates of plagiarism in both groups.

String matching
String matching is a prevalent approach used in computer science. When applied to the problem of plagiarism detection, documents are compared for verbatim text overlaps. Numerous methods have been proposed to tackle this task, of which some have been adapted to external plagiarism detection. String matching refers to the problem of accruing strings of a pattern of a text. It also plays a very important role in plagiarism detection, as it has been used as a tool in software metrics. The string matching problems have many algorithms to solve plagiarism detection. Parameterized string matching is able to detect plagiarism in a software code. Checking a suspicious document in this setting requires the computation and storage of efficiently comparable representations for all documents in the reference collection to compare them pairwise. Generally, suffix document models, such as suffix trees or suffix vectors, have been used for this task. Nonetheless, substring matching remains computationally expensive, which makes it a non-viable solution for checking large collections of documents.