Systematic Collaboration to Promote Academic Integrity During Emergency Crisis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».