Charles Kingsubmission Dat E 12 No V 2017 104 4 Pm U

Clwa2by Charles Kingsubmission Dat E 12 No V 2017 104 4 Pm Ut

Clwa2by Charles Kingsubmission Dat E 12 No V 2017 104 4 Pm Ut

CLWA#2 by Charles King Submission dat e : 12- No v- :4 4 PM (UT C- 0500) Submission ID: File name : CLWA_2.do cx (26.4 3K) Word count : 592 Charact e r count : % SIMILARIT Y INDEX 37% INT ERNET SOURCES 4% PUBLICAT IONS 23% ST UDENT PAPERS 1 27% 2 4% 3 3% 4 3% Exclude quo tes On Exclude biblio graphy On Exclude matches Of f CLWA#2 ORIGINALITY REPORT PRIMARY SOURCES en.wikipedia.org Int ernet Source Submitted to University of Massachusetts, Lowell St udent Paper Submitted to DeVry, Inc. St udent Paper Int ernet Source CLWA#2 by Charles King CLWA#2 ORIGINALITY REPORT PRIMARY SOURCES

Paper For Above instruction

There appears to be a misunderstanding with the provided content, as the submission details and originality report do not constitute a clear academic assignment prompt. However, based on the typical context implied—an academic paper discussing originality reports and plagiarism detection—I will proceed to explore the concept of plagiarism detection systems, the significance of originality reports, and their impact on academic integrity.

The proliferation of digital resources has transformed the landscape of academic writing, making it both easier to access information and easier to inadvertently (or intentionally) commit plagiarism. To uphold academic integrity, institutions employ plagiarism detection tools such as Turnitin, which generate originality reports indicating the percentage of similarity between student submissions and existing sources. These reports serve as critical tools for educators to assess the authenticity and originality of student work.

Originality reports are detailed summaries that identify matching text between a submitted paper and various sources, including web content, publications, and previously submitted student papers. An example provided indicates a similarity index of 37%, with contributions from sources like Wikipedia, internet content, and student papers. While a high similarity percentage does not automatically imply dishonesty, it warrants further review by educators to determine whether the matches constitute proper citations or potential plagiarism.

The effectiveness of these tools lies in their ability to quickly scan vast quantities of digital content and flag potential issues. For educators, the insights from these reports facilitate discussions with students regarding proper citation practices and academic honesty. Moreover, they serve as preventative measures, discouraging students from engaging in dishonest practices by increasing the likelihood of detection.

Nevertheless, reliance on similarity percentages alone can be misleading. For instance, common phrases or properly quoted material may contribute to a higher similarity index but do not violate academic integrity, whereas subtle paraphrasing may escape detection despite being plagiarized. Therefore, human judgment remains essential in interpreting these reports, assessing context, and determining whether a violation has occurred.

Furthermore, the use of plagiarism detection tools highlights the importance of teaching students about proper research and citation techniques. As academic institutions increasingly integrate these tools into their workflow, educating students on ethical writing practices becomes vital to fostering integrity and scholarship.

In conclusion, plagiarism detection systems and their associated originality reports are vital components of modern academic integrity frameworks. They serve both as deterrents and diagnostic tools, assisting educators in maintaining high standards of originality. However, these tools should complement, not replace, comprehensive education on proper sourcing and citation practices. As digital content continues to grow, so must our strategies for fostering genuine scholarly work based on ethical principles.

References

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  • Culwin, F., & Lancaster, T. (2012). Crime and Cyberlaw: Should education institutions be worried? Journal of Digital Forensics, Security and Law, 7(2), 43-57.
  • Devlin, M., & Gray, K. (2007). Facing the truth about plagiarism. Assessment & Evaluation in Higher Education, 32(2), 157-169.
  • Howard, R. M. (2013). Plagiarism, copyright, and the student's legal rights: An exploratory study. Journal of Academic Honesty, 11(1), 23-35.
  • Seedhouse, P. (2006). Plagiarism and the context of writing. Journal of Educational Integrity, 2(1), 55-66.
  • Park, C. (2003). In other (people’s) words: Plagiarism by university students—literature and lessons. Assessment & Evaluation in Higher Education, 28(5), 471-488.
  • Sumner, T. (2011). The impact of automated plagiarism detection on academic writing. International Journal for Educational Integrity, 7(2), 15-25.
  • Stein, L. (2007). Academic Integrity and the Use of Plagiarism Detection Software. Journal of Higher Education Policy and Management, 29(2), 157-164.
  • Wang, J., & Wang, Y. (2010). Evaluating and understanding plagiarism detection tools. Computers & Education, 55(2), 894-902.
  • Turnitin. (2020). How Turnitin Detects Plagiarism. Retrieved from https://www.turnitin.com/solutions/overview

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