Creating simple whiteboard animations to explain complex tooth microanatomy
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".