Zoom in on learning with a virtual microscope: A convergent parallel mixed-method study
Bibliographic record
Abstract
Knowledge of histology is essential for many disciplines in professional health education. Virtual Microscopes (VMs) are increasingly becoming popular as a cost-effective teaching tool for histology. However, a lack of hands-on experience with traditional light microscopes and glass slides concerns many educators. Although studies have reported improvement or no difference in students' knowledge and/or performance using either virtual or optical microscopes, reports on the impact of VMs on students' understanding of magnification and orientation are scarce. We conducted a convergent, parallel mixed-method study to assess dental students' understanding of magnification and orientation after using a virtual microscope to study oral histology. Six hours of mandatory lab sessions and critical thinking assignment questions were designed for the 1st year students in the Doctor of Dental Surgery (DDS) program. Quantitative data were collected from students' performance in orientation and magnification-related questions in the summative assessments. Students' written responses to reflective questions were the qualitative data, analyzed using manifest content analysis. All 32 students accurately answered 3 out of 5 questions, requiring them to apply the knowledge of magnification and orientation. 30 and 29 students correctly answered the remaining questions, respectively. 97% of the class agreed to improve their understanding of magnification and orientation after using the VM. All (100%) students (n = 32) completed the reflective assignment, generating 64 meaning units. 17 codes were generated and compiled into seven subcategories, which were further condensed into two categories: refinement of mental models and enhanced learning. Although our study is limited by a small sample size, it sheds light on the strategies adopted by dental students to improve their senses of magnification and orientation while using a VM.
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 | 0.039 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".