Use of Multiple-Select Multiple-Choice Items in a Dental Undergraduate Curriculum: Retrospective Study Involving the Application of Different Scoring Methods
Bibliographic record
Abstract
BACKGROUND: Scoring and awarding credit are more complex for multiple-select items than for single-choice items. Forty-one different scoring methods were retrospectively applied to 2 multiple-select multiple-choice item types (Pick-N and Multiple-True-False [MTF]) from existing examination data. OBJECTIVE: This study aimed to calculate and compare the mean scores for both item types by applying different scoring methods, and to investigate the effect of item quality on mean raw scores and the likelihood of resulting scores at or above the pass level (≥0.6). METHODS: Items and responses from examinees (ie, marking events) were retrieved from previous examinations. Different scoring methods were retrospectively applied to the existing examination data to calculate corresponding examination scores. In addition, item quality was assessed using a validated checklist. Statistical analysis was performed using the Kruskal-Wallis test, Wilcoxon rank-sum test, and multiple logistic regression analysis (P<.05). RESULTS: We analyzed 1931 marking events of 48 Pick-N items and 828 marking events of 18 MTF items. For both item types, scoring results widely differed between scoring methods (minimum: 0.02, maximum: 0.98; P<.001). Both the use of an inappropriate item type (34 items) and the presence of cues (30 items) impacted the scoring results. Inappropriately used Pick-N items resulted in lower mean raw scores (0.88 vs 0.93; P<.001), while inappropriately used MTF items resulted in higher mean raw scores (0.88 vs 0.85; P=.001). Mean raw scores were higher for MTF items with cues than for those without cues (0.91 vs 0.8; P<.001), while mean raw scores for Pick-N items with and without cues did not differ (0.89 vs 0.90; P=.09). Item quality also impacted the likelihood of resulting scores at or above the pass level (odds ratio ≤6.977). CONCLUSIONS: Educators should pay attention when using multiple-select multiple-choice items and select the most appropriate item type. Different item types, different scoring methods, and presence of cues are likely to impact examinees' scores and overall examination results.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".