Uncovering evidence: Transitioning from face-to-face to online learning.
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
Background: There is a need for evidence-based choices when integrating innovative teaching technologies into dental hygiene and dentistry education. Examining both the direct and indirect effects of these technologies can help to inform and enhance teaching practices. The aim of this study was to compare traditional lectures with online modules tailored to diverse learning style preferences, exploring how these approaches influence student engagement, retention, and recall. Methods: Second-year dental hygiene and first-year dentistry students were randomly assigned to 1 of 2 teaching conditions (in-person lecture, online lecture) in a common communications course. Baseline measures of content achievement, Edmonds learning style preferences, and comfort levels with learning online and in-person were recorded prior to the lecture using the pre-lecture assessment survey. Students completed post-lecture assessments immediately after the lecture and again 6 months later. Results: Regardless of the teaching condition, students showed significant improvement in their academic performance compared to the baseline measures. Their learning style preferences were found to be linked with higher engagement levels, a sense of accomplishment, and control over their learning environment. Conclusions: Teaching health sciences students presents challenges, especially when transitioning from traditional in-person classes to online learning, which may lack engagement for some. Accommodating diverse learning style preferences is crucial for maximizing technology's benefits in education and enhancing learning outcomes. A blended approach, combining face-to-face and online lectures, can optimize student learning experiences, emphasizing the importance of considering varied preferences in educational strategies, particularly in the post-pandemic era.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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