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Record W4362454614 · doi:10.1002/jdd.13215

Whiteboard animation simulation for dental education

2023· article· en· W4362454614 on OpenAlexaff
Nazlee Sharmin, Mike Carbonaro, A. Chow

Bibliographic record

VenueJournal of Dental Education · 2023
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnimationWhiteboardStorytellingComputer scienceMultimediaInteractive whiteboardComputer animationGraphicsSoftwareComputer graphics (images)NarrativeArt

Abstract

fetched live from OpenAlex

Whiteboard animation is a specific type of animation where line drawing and narration are used to explain concepts. The combination of visual thinking and storytelling makes whiteboard animations a unique and effective educational tool. Storytelling is a multimodal teaching approach that simultaneously engages listeners’ thinking, emotions, and imagination.1 However, this powerful tool is underutilized in higher education, particularly science education.1 Whiteboard animations, powered by storytelling, have successfully explained complex health-related topics to pharmacology students2 and positively impacted the retention, engagement, and enjoyment of university-level students.3 We planned to create whiteboard animations for dental students to explain complex science topics using graphics and storytelling. However, we have encountered two challenges while attempting to make whiteboard animations as an educator: (1) Making a good quality whiteboard animation requires paid software, which can be expensive. (2) Most whiteboard animation software is designed for business advertising and does not provide the graphics gallery needed to explain health science-related topics. The graphics gallery of the software do not have images representing the microanatomy of the tooth, oral cavity, and cellular processes. As a solution, we have used traditional PowerPoint and hand-drawn digital images to mimic whiteboard animation. Procreate, a stylus pen, and an iPad were used for drawings that were exported into PowerPoint. The built-in animations of PowerPoint were used to animate the images and supporting text (Figure 1). Screen-capture software was used to record the animation screen as a video with a voice-over narration. Our whiteboard animation explains the types of epithelial cells in a storytelling manner. First-year dentistry students learn about the epithelium as part of histology. The animation starts with the story of a boy who wants to eat cereal and milk with a fork. The narration then draws a connection between the structure-function relationships of a utensil to the structure-function relations of cells in the epithelium (Figure 2). We have incorporated interactive self-assessment questions allowing viewers to assess their learning. The learning objective of the animation and corresponding assessment questions are shown in Table 1. Learning objective whiteboard animation: Epithelium of the gastrointestinal (GI) tract Identify the types of epithelial cells based on their shape and function. Differentiate between simple and stratified epithelium. Define the type of epithelium found in different parts of the gastrointestinal tract. Describe the correlation between the shape of an epithelial cell and its function in the gastrointestinal tract. Q1. Which epithelium comprises cells whose height is two–three times greater than their width and are arranged in an ordered layer? Simple cuboidal epithelium Simple columnar epithelium *** Simple squamous epithelium Stratified epithelium Q2. Which epithelium comprises several layers, such that cells high up in the epithelium are not in contact with the underlying extracellular matrix? Simple cuboidal epithelium Simple columnar epithelium Simple squamous epithelium Stratified epithelium *** Characterize the epithelium of the oral cavity. Explain how the epithelium of the oral cavity is different from the rest of the gastrointestinal tract. Q3. What type of epithelium is found in our oral cavity? Stratified squamous epithelium *** Simple columnar epithelium Stratified columnar epithelium Simple squamous epithelium Traditionally whiteboard animations use hand motion to draw and write on a whiteboard. However, PowerPoint does not have that option. As an alternative, we used the animated “human hand” to bring and stick the picture on a white screen or color the important regions instead of appearing it to draw. Adding background music and sound effect can make the video engaging and entertaining. According to Cognitive Load Theory, the learning load of working memory can be reduced by alternating how a topic is presented.4 Whiteboard animations offer an alternative learning scaffold, therefore expected to reduce the extrinsic load of understanding complex concepts. Stories with real-world examples will also help students understand and retain the information. The authors declare no conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0470.004

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.

Opus teacher head0.040
GPT teacher head0.442
Teacher spread0.402 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations4
Published2023
Admission routes1
Has abstractyes

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