Interactive Engagement with Self-Paced Learning Content in a Didactic Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: A growing number of health professional institutions around the world are embracing innovative technologies to increase student engagement, primarily to improve clinical and simulated learning experiences. Didactic learning is an essential component of dental and medical curricula. However, limited research is available regarding the implementation of technology-infused teaching in classroom settings. We developed self-paced interactive learning content using the HTML5 Package (H5P) to promote student engagement in a didactic course within a dental hygiene program. METHODS: A total of 52 interactive artifacts were created and administered to students as supplementary learning material. A descriptive study was conducted to explore student perceptions and engagement with the H5P content, as well as to evaluate the impact of these artifacts on academic performance. RESULTS: Students performed significantly better on exam questions associated with interactive H5P content posted in the learning management system compared to other questions. Most students were highly engaged with the H5P content during the week leading up to each summative assessment. However, two of the three students with the highest course grades demonstrated consistent engagement with this content throughout the course. CONCLUSIONS: Our results highlight the effectiveness of interactive content created using the H5P platform in fostering student engagement. The development of self-paced interactive materials may benefit various aspects of didactic teaching, including both synchronous and asynchronous online learning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it