Misconceptions amongst dental students: How can they be identified?
Bibliographic record
Abstract
AIM: To compare the frequency of misconceptions amongst dental students resulting from assessments in different subject areas using different types of multiple-choice questions (MCQs). We wanted to know whether misconceptions, or strongly held incorrect beliefs, differed by subject area or question type. METHODS: A total of 104 students completed two assessments that included 20 MCQs on endodontics and 20 MCQs on dental implants. On each examination, 10 questions were scenario-type questions requiring interpretation or analysis and 10 questions were factual-based, knowledge questions. Incorrect responses and confidence levels by student and subject were recorded for a comparison of average misconceptions by question type and for correlations between scenario and knowledge question types for misconceptions on both assessments. RESULTS: Students were overly confident on their incorrect responses and misconceptions for both assessments. On the endodontic examination, students held a statistically significant higher number of mean misconceptions on scenario questions than for knowledge questions, but the difference was not statistically significant for the dental implant examination. There was a moderately weak relationship between scenario and knowledge questions for misconceptions on the endodontic (r=.31) and dental implant (r=.20) assessments, suggesting students who have misconceptions on knowledge questions are somewhat more likely to have misconceptions on scenario questions. CONCLUSION: Students had a consistent rate of overconfidence (75%) in their incorrect responses regardless of question type or dental subject. Questions that prompted a higher per cent of incorrect responses were more likely to detect misconceptions, as students were highly confident in their mistakes, for both assessments.
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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 | 0.017 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".