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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".