Systematic Collaboration to Promote Academic Integrity During Emergency Crisis
Bibliographic record
Abstract
Increasing emphasis on proactive approaches to academic integrity in institutional strategies and policies can be seen as a response to both the challenges of on-line learning and a search for more effective educational models in promoting fundamental values of academic integrity for higher education institutions globally. Thus, towards the end of 2020 the European Network for Academic Integrity established the “Academic Integrity Policies Working Group”. The working group aims to collect examples of effective policies to serve as practical recommendations for educational institutions developing proactive institutional policies towards the establishment of a culture of academic integrity. To achieve this purpose, the WG members are 10 academics from 7 different countries spread over 3 continents who are collaborating on a voluntary basis. The working group facilitates international collaboration on research and development of institutional policies, addressing the roles and responsibilities of stakeholders including pedagogical aspects and assessment design. Within the last six months, the WG has held several virtual meetings during which each of the members presented their achievements in this field, to reach a common understanding. The WG decided to begin by reviewing the relevant literature to identify potential gaps and categorize existing sources in terms of the approaches proposed or adopted and underlying strategic objectives. We aim to reveal how the occurring shift from a punitive to an educative approach to academic misconduct is reflected at different levels of strategies, policies and procedures within the matrix of five indices of consistency, accountability, fairness, proportionality, and clarity of definitions. The multi-country collaborative notion of the WG brings different perspectives to the analyses, adding value to the experiences of the members. Considering the digitalization of education as an emergency reaction to COVID-19, the relevance and importance of academic integrity values has been elevated due to increased concerns of academic misconduct in emergency remote teaching (Eaton, 2020; Khan et al., in press; Razi & Sahan, 2020). Unreadiness and unfamiliarity with on-line learning resulted in many institutions failing to adequately guide lecturers to design appropriate educational models for effective delivery. Implementing effective solutions to meet these challenges has proved difficult for some institutions. The working group is very new and still establishing its identity and direction. In this presentation we will share our experiences about collaborating virtually as a multi-national, trans-continental team to achieve a common goal focused on academic integrity policy. We will also highlight integrity issues faced by the academic communities during COVID-19 and provide some examples of pro-/re-active measures taken in some institutions to address the post-Covid integrity challenges. The presentation to the conference audience will provide an opportunity for the WG members to present their initial ideas and get feedback from interested participants. We are also happy to welcome new members who share an interest in this important subject.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".