Assessment of the students’ performance and support needs in a novel digital carving exercise
Bibliographic record
Abstract
OBJECTIVES: In recent years, digital technology has been rapidly expanding in dental practice, which entails an early integration of digital dentistry into the preclinical dental curriculum. This study introduces first-year dental students to a digital carving exercise and investigates its role in enhancing their understanding and performance in traditional wax carving activities. Another objective was to explore the students' challenges and needs for support in the digital carving activity. METHODS: Digital carving exercise was introduced into the first-year dental morphology curriculum in 2020. Students' performance in anterior wax carving was quantitively compared prior to and following the implementation of the exercise. The students' grades in the digital carving exercise were also compared across three academic years: 2020, 2021, and 2022. Qualitatively, an interpretive description approach using focus group with 31 first-year dental students was utilized to explore their perspectives regarding the digital exercise. RESULTS: A statistically significant improvement was found in the students wax carving performance following the incorporation of the digital carving activity (p-value = 0.0001). Students' performance in the digital carving exercise also statistically improved over the years. Students' challenges included the technology's unfamiliarity, and a perceived irrelevance of the exercise. Additional guidance, resources, and timely feedback were reported among the students' support needs during the exercise. CONCLUSION: Digital carving is a promising tool in anatomical education that can improve the students' spatial understanding and manual dexterity. However, educators need to carefully integrate it into the curriculum to address the students' challenges and optimize their learning experience.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".