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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".