Using PowerPoint and H5P to Create Interactive Animated Instructional Videos
Bibliographic record
Abstract
BACKGROUND: The advantages of animated videos in education are widely recognised, yet their use by educators is restricted by the technical skills and resources needed for their production. APPROACH: We have used the built-in animation and voice-over narration features of Microsoft PowerPoint to create full-fledged animated videos for students in the Doctor of Dental Surgery (DDS) program. H5P was used to add interactive self-assessment questions to the videos. Three interactive animated videos on oral epithelium and biofilms were posted in the learning management system (LMS) for the first and second year DDS students. EVALUATION: A descriptive study was conducted to demonstrate student interactions and perceptions of interactive animated videos. First and second-year DDS students were automatically enrolled in the study. Student engagement data, in the form of the number of interactions with the interactive animated videos, were collected from the LMS. They were also invited to participate in a voluntary survey. Although the interactive animated videos were posted as supplementary learning content, most students accessed the videos. Ninety-four percent of the survey respondents indicated that the videos helped clarify concepts and made learning enjoyable. They also positively valued the interactive self-assessment questions incorporated into the videos. IMPLICATIONS: PowerPoint can be a simple yet effective way to create animated videos. Although this study has a small number of participants, the findings highlight the potential of interactive animated videos as an effective teaching tool to enhance student engagement and learning experiences.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".