Matching Items (3)

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Zero to Tolerant

Description

Academic integrity policies coded specifically for journalism schools or departments are devised for the purpose of fostering a realistic, informative learning environment. Plagiarism and fabrication are two of the most

Academic integrity policies coded specifically for journalism schools or departments are devised for the purpose of fostering a realistic, informative learning environment. Plagiarism and fabrication are two of the most egregious errors of judgment a journalist can commit, and journalism schools and departments address these errors through their academic integrity policies. Some schools take a zero-tolerance approach, often expelling the student after the first or second violation, while other schools take a tolerant approach, in which a student is permitted at least three violations before suspension is considered. In a time where plagiarizing and fabricating stories has never been easier to commit and never easier to catch, students must be prepared to understand plagiarism and fabrication with multimedia elements, such as video, audio, and photos. In this project, journalism academic integrity codes were gathered from across the U.S. and designated to a zero-tolerance, semi-tolerant or tolerant category the researcher designed in order to determine what is preparing students most for the real journalism world, and to suggest how some policies could improve themselves.

Contributors

Created

Date Created
  • 2016-12

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Kitsune: Structurally-Aware and Adaptable Plagiarism Detection

Description

Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the

Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a number of plagiarism detection tools that attempt to encode knowledge about the programming languages they support in order to better detect obscured duplicates. Many such tools do not support a large number of languages because doing so requires too much code and therefore too much maintenance. It is also difficult to add support for new languages because each language is vastly different syntactically. Tools that are more extensible often do so by reducing the features of a language that are encoded and end up closer to text comparison tools than structurally-aware program analysis tools.

Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.

Contributors

Agent

Created

Date Created
  • 2020