Multiple-Choice Testing Using Immediate Feedback—Assessment Technique (IF AT®) Forms: Second-Chance Guessing vs. Second-Chance Learning?
Bibliographic record
Abstract
Multiple choice testing is a common but often ineffective method for evaluating learning. A newer approach, however, using Immediate Feedback Assessment Technique (IF AT®, Epstein Educational Enterprise, Inc.) forms, offers several advantages. In particular, a student learns immediately if his or her answer is correct and, in the case of an incorrect answer, has an opportunity to provide a second response and receive partial credit for a correct second attempt. For a multiple choice question with five possible answers, the IF AT® form covers spaces labeled A through E with a thin opaque film; when the film is scratched away, a star indicates the correct answer. This study was conducted in order to assess learning after an initial incorrect answer. Based on random chance, students should have mathematically a 25% chance of guessing a correct second answer (i.e. 1 of 4 remaining answers on the IF AT® form). Analysis of second responses for 8775 questions on IF AT® forms in 22 classes over 3 years showed that the percent of correct second answers was 44.9%, significantly higher than one might expect from random guessing. This indicates that students learned from an incorrect answer and, possibly by re-reading the problem, were able to demonstrate some level of mastery of the material. This data leads us to conclude that IF AT® forms are useful assessment tools.
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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 | 0.031 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".