Notice bibliographique
Résumé
Quality assurance and academic integrity are intertwined, and you cannot have one without the other. Academic integrity (AI) issues are a constant threat to the quality of Canadian academic programs, degrees, and educational institutions, and it has been an on-going challenge to maintain and promote AI in higher education. However, it is rarely acknowledged that AI is embedded in our national degree standards. Therefore, Canadian universities can and should leverage this connection in their academic program reviews (i.e., quality assurance process). This will lead to better support for AI initiatives and it will hold programs accountable for efforts in this area. Moreover, embedding academic integrity in academic program reviews ensures that AI will be regularly examined approximately every 8 years. It also reinforces the importance of AI, and helps protect the credibility of academia. Breaches of integrity chip away at the foundation of academia and it puts the credibility of higher education at risk. In particular, here are two main concerns involving students: 1) the potential for students to graduate without having the required degree competencies, and 2) the possibility that students who engaged in academic misconduct in school might engage in this behaviour in their career. From a job readiness perspective, students who have not earned or demonstrated their degree qualifications will not be prepared to contribute to their field; moreover, they can also pose a danger to others (IIEP-UNESCO, 2016; ICAI, 2016). In addition, research indicates that students who engaged in academic misconduct may be more inclined to act with misconduct in their careers (Denisova-Schmidt, 2018; IIEP-UNESCO, 2016). Maintaining integrity in higher education is key to preparing students as social and civically-responsible members of society (International Center for Academic Integrity, 2014, pg. 15). According to an advisory statement released by the International Institute for Educational Planning (IIEP) of the United Nations Educational, Scientific and Cultural Organization (UNESCO) in 2016, “corruption in higher education has a high cost to society” (p. 2), and “...quality assurance systems must take a leading role in this battle” (p. 1). Promoting and maintaining AI in higher education is a constant challenge that many institutions struggle with. However, there is a little-known fact that can assist educational institutions: academic integrity is embedded in our national degree standards under the section of Professional Capacity/Autonomy (Council of Ministers of Education, 2007). Canadian universities can and should leverage this connection in their academic program reviews (i.e., quality assurance process). Emphasis on academic integrity could be raised by ensuring that program reviews explicitly address AI. This reinforces the importance of AI and encourages programs to develop initiatives that promote academic integrity. Enhancing attention on academic integrity in all academic program reviews at universities across Canada would help solidify students, instructors, staff and administration’s understanding of AI, and its place as the foundation for academia and maintaining the quality of our degrees.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,004 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».