Notice bibliographique
Résumé
The purpose of this study was to examine faculty responses to academic integrity violations. The study focused on the ability of Theory of Planned Behavior model to predict the target behavior of whether faculty would speak face-to-face with a student suspected of cheating. After an elicitation phase to determine modal salient beliefs, a questionnaire was developed to measure the Theory of Planned Behavior variables, respondent demographics and additional characteristics of interest related to faculty responses and opinions on academic integrity violations. The respondent database contained 206 tenured and non-tenured faculty from two large comprehensive universities, one located in United States and the other in Canada. A stepwise multiple regression demonstrated the usefulness of the Theory of Planned Behavior. Overall the model explained 43% of the variance in predicting faculty members' intention to speak face-to-face with a student suspected of cheating. The most significant contribution was made by subjective norms (β = 0.39), followed by attitude (β = 0.34), and perceived behavioral control (β = 0.24). Specific questions pertaining to demographic, cultural, gender, and experiential variables were also addressed. Among the significant findings were that the departmental chair was the most important influence as to whether faculty members dealt with cheating; that individuals with previous bad experiences were less likely to confront cheaters; and that both male and female faculty believed that male faculty have an easier time dealing with students suspected of cheating. Particular attention was paid to faculty members who ignored or failed to act on student cheating. While the majority of faculty (76.7%) felt that instructors should always deal with instances of cheating when suspected, 40.3% of respondents indicated they had ignored suspected incidents of cheating. Based upon the results, recommendations were made regarding administrative policy and support for faculty who must deal with academic integrity violations, a problem which 61.7% of faculty members reported was one of the most negative aspects of the job.
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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 | 0,000 | 0,000 |
| 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 | 0,001 | 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) | 0,000 | 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 ».