Creating simple whiteboard animations to explain complex tooth microanatomy
Notice bibliographique
Résumé
The knowledge of tooth microanatomy is essential for dental education.1 However, it is challenging for educators to teach the complex organization of hydroxyapatite crystals, rods, and interrods in human teeth solely from photomicrograph and textbook images. To comprehend the complex microanatomy of the tooth from microscopic images, students must integrate the knowledge of angle of the tooth section, magnification, orientation, and composition of the tissue structure. Whiteboard animation, where the content appears to be hand-drawn on a school whiteboard, is a powerful pedagogical tool that can stimulate visual thinking and explain complex concepts in an engaging manner.2, 3 Though there are known benefits of these animations,4, 5 reports of applying this tool in dental education are scarce. We have used a graphics editor, Procreate (Available at: https://procreate.com/) on an iPad, and an animation maker, VideoScribe (Available at: https://www.videoscribe.co/) on a laptop to create whiteboard animations for the students in the Doctor of Dental Surgery (DDS) program at the University of Alberta. Content experts outlined the learning outcomes of the videos. Next, a storyboard consisting of a sequence of rough drawings and directions was developed, outlining the plan of the animated video. The images not available in the image gallery of VideoScribe were hand-drawn in Procreate using textbook images as references. The narrative script was recorded separately. The images and audio were imported into VideoScribe to compile the final whiteboard animation, which takes around 5 hours for each video (Figure 1). Two whiteboard animations (tooth microanatomy I and II) were created explaining the internal structure of tooth enamel (Figure 2A–D). The animated videos were posted in the learning management system (LMS) as supplementary learning resources for 1st year DDS students. Student interactions with the videos in the LMS were analyzed. An anonymous survey was also conducted to explore students’ experiences with the animations as learning tools. The study design was approved by the University of Alberta Research Ethics Board (ID: Pro00117409). LMS data showed that 91% of the class (n = 29) watched tooth microanatomy-I, and 81% (n = 26) watched tooth microanatomy-II (Figure 2E). Fifty-three percent of the DDS class (n = 17) participated in the survey. Forty-seven percent of the survey participants watched the animations between 2 and 5 times, 6% watched for more than 5 times (Figure 2F). Most survey participants (82%) agreed or strongly agreed that the whiteboard animations helped clarify concepts for them. Seventy-six percent recognized them as an effective learning tool. Most survey participants (88%) agreed that these animated videos made learning easier and more enjoyable. Seventy-six percent found these videos engaging and would like similar videos in other areas of their studies (Figure 3A). The features of the whiteboard animations perceived to be the most beneficial by the students were the videos being simple (94%) and concise (88%) (Figure 3B). The free-form student comments also showed their positive attitude and satisfaction with these animated videos (Figure 3C). The authors declare no conflicts of interest.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,075 | 0,008 |
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 ».