Practice makes perfect? Association between students’ performance measures in an advanced dental simulation course
Bibliographic record
Abstract
PURPOSE: This study examines the relationship between student performance measures during practice and exams using advanced dental simulation. METHODS: Data from 11 classes of first-year dental students were extracted from Advanced Simulation software (DentSim™) related to Class I and Class II preparations including: total number of practice sessions, average practice score, exam scores, average time preparing teeth during practice/exam, and average time self-evaluating preparations during practice/exam. Comparisons of average practice and exam scores were examined using paired t-test. Relationships between practice/exam measures and exam scores were determined with multiple linear regression. RESULTS: Practice mean and exam scores were significantly associated; exam scores were significantly higher in both procedures. Class I: a significant positive relationship exists between both practice and exam measures: The average practice score was significantly associated with exam score (p < 0.001); time spent preparing the exam tooth was negatively associated with the exam score (p < 0.001); conversely, time spent self-evaluating the exam tooth was significantly associated with an increase in exam score (p = 0.0135). Class II: exam score was significantly associated with two practice measures but neither of the exam measures: exam score for Class II mesioocclusal preparation was significantly associated with average practice score (p < 0.001) and the number of practice attempts (p = 0.025). CONCLUSION: This study emphasizes the predictive value of novice learners' deliberate, repetitive practice using advanced dental simulation, which enhances self-assessment in early stages of psychomotor skill development. Future studies are needed to demonstrate the translation of these skills into a patient care setting.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".