Drawings to explore faculties‘ and students‘ perceptions from different generations cohorts about dental education: A pilot study
Bibliographic record
Abstract
INTRODUCTION/AIMS: We aimed at using drawings as a form of data collection to give voice to older and younger generations in regards to educational practices in undergraduate dentistry. MATERIALS AND METHODS: First year dental students (younger generations) and faculty members (older generations) produced drawings depicting their perceptions of the current dental education learning environment. Qualitative analysis was conducted independently by two researchers using the drawings to produce codes, categories and themes. RESULTS: 15 drawings were produced: 9/34 (26.4%) made by students and 6/20 (30%) made by faculty members. The generated themes indicated that students and faculties found that dental education is going through a challenging time, because of the restrictions caused by the COVID-19 pandemic; and that they were aware about the evident division between basic/preclinical and applied/clinical courses. Faculties showed hopeful signs that the situation may get better. Students' drawings evoked the following topics: digital connectedness, diversity, time goes by, and future aspirations in Dentistry. DISCUSSION: This study reinforced the validity of visual methods as an approach in research and showed different graphical features (features that might be intentionally or unintentionally represented in the drawings) that gave voice to participants. These voices could have been invisible in more traditional qualitative approaches, such as interviews or questionnaires. CONCLUSIONS: Although the two groups of participants came from different generation cohorts, they had aligned perceptions regarding challenges in dental education, and mentioned the separation between preclinic and clinic. Drawings were unique, innovative, and an interesting tool to express perceptions regarding today's learning environment. These insights can consequently help educators to personalize teaching approaches to better meet the needs of the students.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".