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Student Diversity & Academic Writing Project (SDAW)

Student Diversity & Academic Writing Project (SDAW). Prof. Lucas Introna Lancaster University Dr. Edgar Whitley London School of Economics. Student Diversity and Academic Writing Project. HEFCE-funded FDTL 5 project 2005-2008

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Student Diversity & Academic Writing Project (SDAW)

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  1. Student Diversity & Academic Writing Project (SDAW) Prof. Lucas Introna Lancaster University Dr. Edgar Whitley London School of Economics

  2. Student Diversity and Academic Writing Project • HEFCE-funded FDTL 5 project • 2005-2008 • Collaboration between the London School of Economics and Lancaster University • Research and Development

  3. SDAW project structure Four sub projects • A: Country visits (India, Greece, China) • B: UK fieldwork • C: Detection Software • D: Dissemination of results and development of resources

  4. SDAW project aims • To inject timely and topical research results into the debate about the way international students are recruited, prepared and taught and how plagiarism can be deterred • To develop evidence based resources to support the different stakeholders in dealing with plagiarism as an integral part of teaching and learning practice in a holistic manner

  5. ‘Plagiarism’ Detection SystemsPilot work with Turnitin Student Diversity & Academic Writing Project (SDAW)

  6. The Turnitin Pilot project • When / what / why • Key questions • Preliminary results • Some conclusions and implications • Next steps

  7. When / what / why • In Summer and Michaelmas term of 2005 • Overall objective: • to learn about the actual behaviour of Turnitin system through detailed systematic trials • Why Turnitin? • Used be 5000 institutions (12 million students and educators) worldwide.  • 50,000 papers submitted per day   • Used in over 80 countries • Turnitin crawler has downloaded over 9.5 billion Internet pages and updates itself at a rate of 60 million pages per day.

  8. Key questions… • What is the actual level of coverage of Turnitin? • How much must one change text before it become unrecognisable by Turnitin? • When faced with paraphrasing difficult text would writing strategies of non-native speakers be more recognisable to Turnitin?

  9. Actual level of coverage… Electronic Sources Used for the data collection • Total of 103 fragments were submitted to Turnitin • 47 were found (similarity index > 25%) • 45.6% of fragments were found

  10. Actual level of coverage…

  11. How much must one change text… • Resequencing • Will always recognise • Short fragments (< 30 words) • Where you change is very significant • Long fragments (> 30 words) • Significant changes required

  12. Paraphrasing difficult text / non-native

  13. Some conclusions and implications • Coverage of Turnitin database much less than expected • Small fragments recognition unexpected (implication for non-native paraphrasing) • Faced with difficult text non-native speakers will tend to use ‘as is’ significant phrases from the source

  14. Next steps • Turnitin Experiments • Large scale coverage experiment (automated?) • Extending experiments with text changes • Large scale paraphrasing experiment (50 – 100) • Institutional research • Interviews with staff using it • Review of institutional policies

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