Notice bibliographique
Résumé
Our research team in conjunction with the Teaching Centre investigated academic integrity on campus in order to better understand academic dishonesty within our institution. Before the SARS-CoV-2 pandemic, we surveyed the perceptions of, and engagements with, academic dishonesty on campus. We surveyed both student (n=1,142) and faculty (n=130) populations in order to get a broad sense of academic dishonesty at our university. These samples represented 13% of the student population at the time, and 22% of the faculty population. Overall, we found that the majority of students and faculty surveyed believed academic integrity was important, and that unlike many universities across Canada, the University of Lethbridge had low rates of academic dishonesty, though the possible reason for these low rates was unclear. Like many universities around the world, the University of Lethbridge transitioned to a remote teaching model in the spring semester of 2020 in response to the SARS-CoV-2 pandemic. Quickly, faculty and students became concerned with how assessments that were usually proctored in-person would translate to an online learning space. After a year of remote-delivery instruction, we again surveyed students (n=1,134; 13.9% of the student population) and faculty (n=94; 15% of the faculty population) in the spring of 2021, both to further explore why we experienced relatively low rates of academic dishonesty, but also to understand how remote teaching may have impacted perceptions of and engagements with academic dishonesty at our university. We found that despite the majority of student participants reporting that they felt the opportunity to engage in academic dishonesty had increased since the university transitioned to remote learning, they again reported that they engaged in low rates of academic dishonesty were still low compared to other Canadian institutions. Furthermore, faculty participants said they had reported incidents of academic dishonesty less often during this period. If selected, we plan to present the results of our surveys and highlight key differences that emerged, exploring that our low rates may be due to the academic culture of our institution. We also plan to comment on how the transition to online learning effected the perception of academic integrity at our university in comparison to pre-pandemic times. Additionally, we will present qualitative data from open-ended survey responses to illustrate the concerns of our students and faculty members about academic integrity throughout the transition to online learning.
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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,003 | 0,022 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».