Online Medical Education: It Is Time to Listen to the Silence
Notice bibliographique
Résumé
To the Editor: Recently, I have seen greater recognition of the role of silence in medical education, helping reverse the negative connotations traditionally associated with silence. While silence has been interpreted to indicate a lack of knowledge, interest, or competence, it has been increasingly encouraged as an effective pedagogical tool in the classroom and the clinical setting 1: faculty make pauses during their presentations to add dramatic effect, attract students’ attention, and allow students to learn at their own pace. Clinical educators may also incorporate silence into their bedside teaching to help trainees reflect, process, interact, and ask questions. Silence is a complex, multidimensional phenomenon influenced by personality factors and sociocultural ones. For instance, introverted learners might be silent, as they save up their questions for the appropriate time, and nonverbal students tend to concentrate without feeling the urge to express their opinions. Also, there are cultural differences regarding how silence is employed and acknowledged. While silence is avoided in some cultures, it is positively viewed in others, implying respect and openness. Hence, besides the educational benefits, diversity and equality could be promoted if silence was appropriately addressed. Although silence has been discussed in face-to-face teaching, little is known about “online silence.” During the pandemic, everyone has experienced those awkward moments of silence in an online meeting. When there is no answer or comment, particularly in the absence of visible body language, it is difficult to guess whether silence is a sign of agreement, lack of interest, or a way to avoid expressing opinions. Also, whereas taking turns in speaking comes naturally in the spontaneity of free-flowing classroom conversations, taking turns online is sometimes more difficult because speakers have trouble sensing when to give up control of the conversation. Strategies enabling silence as a teaching asset in online education include listening without interrupting, offering purposeful silence, and providing active silent time. 2 Because of the discomfort around silence, teachers may evade situations with a risk of silence. Yet they could help students overcome that discomfort and contribute authentically to discussions by being explicit about silence, clarifying expectations, and maintaining a safe environment. 2 Current scholarship around silence is scarce. I therefore expect a call for further investigation to develop a deeper understanding of the notion of silence in online medical education. In-depth interviews, real-time observations, and analyses of video recordings could afford insight into how silence is perceived and addressed in online medical education and how silence is integrated into such education. Research on the role and influence of silence could equip teachers with more subtle teaching skills and help students be more engaged in learning. The findings of such research could offer practical implications to all who will probably continue teaching and learning via online platforms, even on the far side of the COVID-19 outbreak.
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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 | 0,004 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,016 | 0,020 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,005 |
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 ».