Supporting Academic Integrity: Approaches and Resources for Higher Education
Bibliographic record
Abstract
This guide is for the higher education community as a whole . Readers might be lecturers, educational developers, student services managers or academic conduct officers who would like a better feel for the current issues relating to student plagiarism and associated concerns . It is designed to provide ‘a bird’s-eye view’: to pull together key institutional approaches and resources that have been developed since 2000 . Case studies and perspectives from a number of higher education institutions (HEIs) highlight practice at the institution, programme and course level . The intention is to make them accessible and easy to follow up, whatever your link to academic integrity issues . The notion of academic integrity has been defined as adherence to the values of “honesty, trust, fairness, respect, and responsibility” (Center for Academic Integrity, 1999, p4) . The Center for Academic Integrity, a consortium of over 360 institutions including member institutions from Australia, Canada and the US, provides expertise The Higher Education Academy – 2010 3on practice and policies that can help to foster a ‘culture’ of integrity1 . Established guidance, developed from a UK perspective, has also emphasised how HEIs need to consider issues of academic integrity and associated values when reviewing policy and practice on plagiarism within their institution (JISC, 2005) . On a more practical note, it is important to think about the principles and values that might inform the development of institutional policies, where, for example, statements on the importance of academic honesty are included in policy documents (Carroll, 2009; Morris, 2010) . The focus of this guide is on student plagiarism, but it illustrates how strategies and methods employed at a range of levels within an institution can enable students to develop an understanding of and the necessary skills for good academic practice . It is clear that HEIs in the UK have done much in recent years to address and manage student plagiarism . Initiatives, working groups and projects have been set up, and have worked to improve policies, introduce preventive measures through assessment practices, make effective use of plagiarism detection tools and develop online resources for students: the intention being to ensure that students fully grasp the concept of plagiarism and the skills they need to follow good academic practice . There is a wealth of resources available in this area and this guide is designed to provide a valuable up-to-date selection of these . It is vital that we share these resources and examples of good practice . The Academic Integrity Service was set up by the Higher Education Academy and JISC in 2008 . This initiative was charged with raising awareness of issues relating to academic integrity in UK higher education and encouraging the sharing of best practice in this area . One priority was to build on expertise by consulting with the Higher Education Academy subject centres to identify generic and subject-specific issues, and existing resources relating to academic integrity (e .g . assessment strategies, students’ skills development, plagiarism, disciplinary perspectives, examples of good practice, support resources for staff and relevant guidance for students) . This information gathering exercise informed the development of this guide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.070 | 0.049 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".