Grade Expectations: Mapping Stakeholder Views of Online Plagiarism Detection
Based upon a pilot study of the leading online plagiarism detection service, this article examines the views of faculty and students as the main stakeholders in the controversy over online plagiarism detection. Rather than give advice outside of a specific institutional context, this study offers an...
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| Veröffentlicht in: | The journal of effective teaching 2007, Vol.7 (2), p.68 |
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| container_title | The journal of effective teaching |
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| creator | Ashe, Diana Manning, Michelle |
| description | Based upon a pilot study of the leading online plagiarism detection service, this article examines the views of faculty and students as the main stakeholders in the controversy over online plagiarism detection. Rather than give advice outside of a specific institutional context, this study offers an understanding of the reasoning that informs the diverging points of view, explaining both support of and resistance to online plagiarism detection among each group. The article makes recommendations of best practices for those who choose to incorporate plagiarism detection services into course management. More importantly, the article closes with recommendations for addressing academic integrity on a campus-wide scale--a practice that could alleviate the pressures that encourage many campuses to adopt online plagiarism detection. |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 1935-7869 |
| ispartof | The journal of effective teaching, 2007, Vol.7 (2), p.68 |
| issn | 1935-7869 |
| language | eng |
| recordid | cdi_eric_primary_EJ1055644 |
| source | ERIC - Full Text Only (Discovery); EZB-FREE-00999 freely available EZB journals |
| subjects | Best Practices Cheating College Faculty College Students Deception Educational Technology Ethics Expectation Grades (Scholastic) Integrity Intellectual Property North Carolina Plagiarism Student Attitudes Teacher Attitudes |
| title | Grade Expectations: Mapping Stakeholder Views of Online Plagiarism Detection |
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