An Interactive Feedback Model for Enhancing Teaching and Learning in Dental and Undergraduate Medical Science Education
Notice bibliographique
Résumé
Introduction and Objective As higher education institutions transitioned to fully online teaching and learning environments due to the Covid‐19 pandemic, the importance of effective pedagogical models was reinforced. This unexpected and abrupt change of curriculum delivery from previously well‐established educational programs has not been without challenges, and subsequently identified a need for improvements in online teaching and learning practices. In September of 2020, a group of dental students and motivated faculty at Western University Schulich School of Medicine and Dentistry came together in response to the pandemic and established an online feedback model (V1). This feedback model created the foundation for a Students as Partners program (SaP). SaP programs provide students and faculty the opportunity to foster academic partnerships and together be part of the teaching and learning process. This program allowed dentistry students to provide real‐time feedback on curricular content and delivery as teaching and learning shifted online. In order to evaluate the impact a real‐time feedback model has in enhancing teaching and learning, it is crucial to determine whether the feedback model closes the feedback loop. Closing the feedback loop is a systematic process where student feedback is shared with students and faculty, timely actions are taken to implement changes from the student voice, and the effectiveness of actioned improvements is monitored. Materials and Methods To assess if the V1 feedback system closes the feedback loop to enhance teaching and learning, we distributed Qualtrics surveys with 7‐point Likert scale statements and free‐text response questions to obtain student and faculty perspectives on their awareness, use, and perceived effectiveness of the V1 feedback system. Results After collecting pilot data from students and faculty on their perspectives of the V1feedback system, changes were made to the model of SaP and essential elements were re‐established. The student perspective reported that 73% (n=11) of students disagreed that they were well aware of the V1 feedback system and its intended use, 78% (n=10) disagreed that the V1 feedback system provided a solution for real‐time improvements, and 80% (n=5) disagreed that they were notified of changes being made to the feedback they provided. These results identified closing the feedback loop was not taking place in this V1 model. Conclusion Since increasing the awareness of the SaP program, an updated feedback system (V2) that closes the feedback loop to enhance teaching and learning in dental and undergraduate medical science education was developed and is being evaluated. Further analysis will determine if the V2 feedback system closes the feedback loop in a real‐time feedback model. Significance This research is intended to assist researchers in dental and undergraduate medical science education with a feedback model that elicits student voices while enhancing the teaching and learning experience.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».