Are Faculty Members of Paramedics Able to Designed Accurate Multiple Choice Questions?
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Multiple choice questions (MCQ) are one of the assessment instruments in medical sciences. The overall aim of this study was to perform qualitative and quantitative analysis of the multiple choice question MCQ provided by the professors of Kermanshah University of Medical Sciences-Faculty of Medicine in the academic year 2011-2012.MATERIALS & METHODS: In this descriptive-analytic study, 37 tests of the Faculty of Medicine were analyzed. Quantitative data included difficulty coefficient, discrimination coefficient, whole credibility test, standard deviation of the questions, and the qualitative data consisted of taxonomic percent I, II and III, percentage of questions with no structural problems. The data were analyzed using SPSS software version 20.00 while T-test and chi-square test were applied.RESULTS: The average validity coefficient of the total tests (KR-20) was measured as 0.63, the average difficulty coefficient as 0.58, the average discrimination coefficient as 0.19. The average percentage of the questions without structural problems as 37.1%; all of which were in the acceptable range. The mean Taxonomy I percentage of the questions was38.36% (±11.31), Taxonomy II percentage of the questions was 42.46% (±15.51) with no significant difference in the entire tests. Average percentage of questions with taxonomy III was 20.73% (±12.83) for which independent t-test showed significant difference in the total tests (P=0.00). Average percentage of questions without structural problems was measured as 55.23% (±13.23) for which there was a significant difference in the total tests when independent t-test was used (P=0.041).CONCLUSION: Considering the average validity of the whole test, the mean difficulty coefficient and Taxonomy indexes I, II and III, the tests designed by the professors of the Faculty of Allied Science are within an a acceptable standard range.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".