The interrelationship between confidence and correctness in a multiple-choice assessment: pointing out misconceptions and assuring valuable questions
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to better understand the interfaces of being correct or incorrect and confident or unconfident; aiming to point out misconceptions and assure valuable questions. METHODS: This cross-sectional study was conducted using a convenience sample of second-year dental students (n = 29) attending a preclinical endodontics course. Students answered 20 multiple-choice questions ("basic" or "moderate" level) on endodontics, all of which were followed by one confidence question (scale). Our two research questions were: (1) How was the students' performance, considering correctness, misconceptions, and level of confidence? (2) Were the questions valuable, appropriate and friendly, and which ones led to misconceptions? Four situations arouse from the interrelationship between question correctness and confidence level: (1st) correct and confident, (2nd) correct and unconfident, (3rd) incorrect and confident (misconception) and (4th) incorrect and unconfident. Statistical analysis (α = 5%) considered the interaction between (a) students' performance with misconceptions and confidence; (b) question's difficulty with correctness and confidence; and (c) misconceptions with clinical and negative questions. RESULTS: Students had 92.5% of correctness and 84.6% of confidence level. Nine students were responsible for the 12 misconceptions. Students who had more misconceptions had lower correctness (P < 0.001). High achieving students had low confidence in their incorrect responses (P = 0.047). 'Moderate' questions had more incorrectness (P < 0.05) and less confidence (P = 0.02) than 'basic'. All questions were considered valuable [for example, the ones that presented images or required a mental picture of a clinical scenario, since they induced less misconception (P = 0.007)]. There was no difference in misconceptions between negative questions and other questions (P = 0.96). CONCLUSION: Preclinical endodontic students were highly correct and very confident in their responses. Students who had more misconceptions had also the lowest performance in the assessment. Questions were valuable; but some will worth further improvement for the future. A multiple-choice assessment, when combined with confidence questions, provided helpful information regarding misconceptions and questions value.
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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 | 0.011 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".