Student Response to a Blended Radiology Course: A Multi-Year Study in Dental Education
Bibliographic record
Abstract
Universities around the world are increasingly moving towards blended learning models to engage their 21st century learners (Alammary et al., 2014; Brenard et al., 2014; Tandoh et al., 2014). However, students’ engagement and satisfaction with blended learning in dental education remain understudied. To address this gap, this study examines the effects of a blended learning approach on students’ satisfaction and engagement within dental hygiene and dentistry oral radiology courses. Thirty-five students participated in a survey designed to measure two main constructs: student engagement (per Fredericks et al., 2005) and student satisfaction (per Owston et al., 2013) with the addition of one student providing interview data on each of these constructs. It was found that students were generally satisfied (67%) with the blended learning course format with 65% of students expressing a preference for the blended format. This finding was complemented by students’ also expressing that they were emotionally engaged (70% engagement score), cognitively engaged (69% engagement score), and behaviourally engaged (61% engagement score). These findings suggest that blended learning may be of benefit to the engagement and satisfaction of dental students’ learning the interpretation of dental radiographs.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".