Interactive H5P content for increased student engagement in a dental hygiene program.
Bibliographic record
Abstract
Background: Presently, dental hygiene education is primarily divided into classroom lectures, simulation labs, and clinical experiences. Although the recent surge of curriculum renovation in dental and medical schools centres around enhancing student engagement and active learning, classroom teaching remains teacher-focussed, involving students mainly as passive learners. H5P is an open platform for creating and sharing interactive HTML5 learning content. A large set of H5P content was created and provided to students through the learning management system as supplementary material for an oral biology course in the dental hygiene program at a Canadian university. This study was conducted to evaluate the impact of this interactive H5P content on the students' learning experiences. Methods: The third-year dental hygiene students enrolled in the oral biology course were invited to participate in the study. Anonymised student performance data from the summative exam were analysed, and a survey regarding the student experience with the supplementary H5P content was administered. Results: Students performed better on questions for which H5P supplements were provided. The results from the survey showed satisfaction and perceived benefit of using H5P as supplementary content in didactic lectures. Discussion: The H5P content allowed students to apply knowledge and reproduce understanding, promoting active learning in the didactic oral biology course. Students appreciated the content's interactive nature and expressed willingness to have similar experiences in other courses. Conclusion: Using H5P, interactive learning content can promote self-directed and personalized learning. This open learning platform has the potential to redefine didactic teaching by fostering an active learning environment.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".