Using Automatic Item Generation to Improve the Quality of MCQ Distractors
Bibliographic record
Abstract
UNLABELLED: CONSTRUCT: Automatic item generation (AIG) is an alternative method for producing large numbers of test items that integrate cognitive modeling with computer technology to systematically generate multiple-choice questions (MCQs). The purpose of our study is to describe and validate a method of generating plausible but incorrect distractors. Initial applications of AIG demonstrated its effectiveness in producing test items. However, expert review of the initial items identified a key limitation where the generation of implausible incorrect options, or distractors, might limit the applicability of items in real testing situations. BACKGROUND: Medical educators require development of test items in large quantities to facilitate the continual assessment of student knowledge. Traditional item development processes are time-consuming and resource intensive. Studies have validated the quality of generated items through content expert review. However, no study has yet documented how generated items perform in a test administration. Moreover, no study has yet to validate AIG through student responses to generated test items. APPROACH: To validate our refined AIG method in generating plausible distractors, we collected psychometric evidence from a field test of the generated test items. A three-step process was used to generate test items in the area of jaundice. At least 455 Canadian and international medical graduates responded to each of the 13 generated items embedded in a high-stake exam administration. Item difficulty, discrimination, and index of discrimination estimates were calculated for the correct option as well as each distractor. RESULTS: Item analysis results for the correct options suggest that the generated items measured candidate performances across a range of ability levels while providing a consistent level of discrimination for each item. Results for the distractors reveal that the generated items differentiated the low- from the high-performing candidates. CONCLUSIONS: Previous research on AIG highlighted how this item development method can be used to produce high-quality stems and correct options for MCQ exams. The purpose of the current study was to describe, illustrate, and evaluate a method for modeling plausible but incorrect options. Evidence provided in this study demonstrates that AIG can produce psychometrically sound test items. More important, by adapting the distractors to match the unique features presented in the stem and correct option, the generation of MCQs using automated procedure has the potential to produce plausible distractors and yield large numbers of high-quality items for medical education.
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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 | 0.053 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".