Scoring Single-Response Multiple-Choice Items: Scoping Review and Comparison of Different Scoring Methods
Bibliographic record
Abstract
BACKGROUND: Single-choice items (eg, best-answer items, alternate-choice items, single true-false items) are 1 type of multiple-choice items and have been used in examinations for over 100 years. At the end of every examination, the examinees' responses have to be analyzed and scored to derive information about examinees' true knowledge. OBJECTIVE: The aim of this paper is to compile scoring methods for individual single-choice items described in the literature. Furthermore, the metric expected chance score and the relation between examinees' true knowledge and expected scoring results (averaged percentage score) are analyzed. Besides, implications for potential pass marks to be used in examinations to test examinees for a predefined level of true knowledge are derived. METHODS: Scoring methods for individual single-choice items were extracted from various databases (ERIC, PsycInfo, Embase via Ovid, MEDLINE via PubMed) in September 2020. Eligible sources reported on scoring methods for individual single-choice items in written examinations including but not limited to medical education. Separately for items with n=2 answer options (eg, alternate-choice items, single true-false items) and best-answer items with n=5 answer options (eg, Type A items) and for each identified scoring method, the metric expected chance score and the expected scoring results as a function of examinees' true knowledge using fictitious examinations with 100 single-choice items were calculated. RESULTS: A total of 21 different scoring methods were identified from the 258 included sources, with varying consideration of correctly marked, omitted, and incorrectly marked items. Resulting credit varied between -3 and +1 credit points per item. For items with n=2 answer options, expected chance scores from random guessing ranged between -1 and +0.75 credit points. For items with n=5 answer options, expected chance scores ranged between -2.2 and +0.84 credit points. All scoring methods showed a linear relation between examinees' true knowledge and the expected scoring results. Depending on the scoring method used, examination results differed considerably: Expected scoring results from examinees with 50% true knowledge ranged between 0.0% (95% CI 0% to 0%) and 87.5% (95% CI 81.0% to 94.0%) for items with n=2 and between -60.0% (95% CI -60% to -60%) and 92.0% (95% CI 86.7% to 97.3%) for items with n=5. CONCLUSIONS: In examinations with single-choice items, the scoring result is not always equivalent to examinees' true knowledge. When interpreting examination scores and setting pass marks, the number of answer options per item must usually be taken into account in addition to the scoring method used.
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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 | 0.115 | 0.366 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.070 | 0.068 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".