Supplementary Results of the CAIS-1 Survey on Cheating in Undergraduate Engineering Programs in Saskatchewan
Notice bibliographique
Résumé
Abstract – In early 2016, engineering students and staff at the Universities of Saskatchewan and Regina were surveyed regarding their views and experiences as they relate to academic dishonesty. This paper summarizes some of the results from the gathered data. Our first version of the Canadian Academic Integrity Survey (CAIS-1) was very similar to the Perceptions and Attitudes toward Cheating among Engineering Students (PACES-1) survey, as discussed in Carpenter et al [2]. With CAIS-1, a different set of demographic questions was posed along with some minor additions to the main bank of PACES-1 academic integrity questions, including three additional open-ended questions. The focus of this paper is on the results that came from the new questions as well as on those results that were not covered in the Carpenter paper although they did come from the original PACES-1 questions.
 Certain demographics were found to be predictors of self-reported cheating frequency. There was a small but significant difference in cheating frequency based on gender, with males reporting cheating slightly more often than females. Academic average was found to negatively correlate with cheating frequency. The frequency of cheating in high school was a significant predictor for the frequency of cheating in university. These demographic results agreed with the results of PACES-1 and other research on academic integrity. One demographic result that did not agree with prior research was that cheating frequency did not increase with increased extracurricular involvement. 
 To better understand what influences engineering students to cheat, each respondent was given a score based on their self-reported frequency of cheating. This score was used to compare student responses in each of the following four categories: "situational cheating", "diffusion of responsibility", "personal responsibility", and "no choice but to cheat". The first three of these were used to analyze respondents to PACES-1 in Passow et al [11] and the construct "no choice to but to cheat" was added in the analysis of our CAIS-1 data. Situational cheating sub-scale scores were found to be a significant predictor of academic dishonesty. The other three were significant, but accounted for only a small portion of variance. In short, situations where a student judges the benefits of cheating to outweigh the risks are predictors of student cheating. 
 When asked if there was an acceptable time to cheat, most student respondents said that it was "never" OK to cheat. Despite this, many of these respondents reported engaging in cheating. Neutralizations were used to justify such behaviours, usually by putting the responsibility on instructors e.g. the workload forced us to cheat. For those who did say that there were acceptable times to cheat, a sizeable portion of the respondents said that they cheated to "help with their learning". Approximately 50% of the students felt that faculty did not care about or were not engaged in preventing cheating behaviours. The students who held those views most strongly tended to care about cheating more than most other students.
 Few statistical differences were found between students enrolled in ethics courses and those that were not. The differences that were found were similar to those between upper year and lower year students. These differences were confounded as the students in ethics courses were almost always upper year students.
 Overall, the Canadians surveyed in this initial CAIS-1 study were similar to Americans surveyed over a decade ago. However, there were some notable differences and we found some new results that were not discussed in the earlier American studies.
 
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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; 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 ».