Do multiple true-false items beat the commonly used one-best-answers questions regarding to the Ottawa Criteria for Good Assessment? Results of a literature review
Bibliographic record
Abstract
Background: Multiple True-False-Items (MTF-Items) might offer some advantages compared to one-best-answer-questions (TypeA) as they allow more than one correct answer and may better represent clinical decisions. However, in medical education assessment MTF-Items are seldom used. Summary of Work: With this literature review existing findings on MTF-items and on TypeA were compared along the Ottawa Criteria for Good Assessment, i.e. (1) reproducibility, (2) feasibility, (3) validity, (4) acceptance, (5) educational effect, (6) catalytic effects, and (7) equivalence. We conducted a literature research on ERIC and Google Scholar including papers from the years 1935 to 2014. We used the search terms “multiple true-false”, “true-false”, “true/false”, and “Kprim” combined with “exam”, “test”, and “assessment”. Summary of Results: We included 29 out of 33 studies. Four of them were carried out in the medical field Compared to TypeA, MTF-Items are associated with (1) higher reproducibility (2) lower feasibility (3) similar validity (4) higher acceptance (5) higher educational effect (6) no studies on catalytic effects or (7) equivalence. Discussion and Conclusions: While studies show overall good characteristics of MTF items according to the Ottawa criteria, this type of question seems to be rather seldom used. One reason might be the reported lower feasibility. Overall the literature base is still weak. Furthermore, only 14 % of literature is from the medical domain. Further studies to better understand the characteristics of MTF-Items in the medical domain are warranted. Take-home messages: Overall the literature base is weak and therefore further studies are needed. Existing studies show that: MTF-Items show higher reliability, acceptance and educational effect; MTF-Items are more difficult to produce
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.149 | 0.401 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.032 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".