The Immediate Feedback Assessment Technique: A Learner-centered Multiple-choice Response Form
Bibliographic record
Abstract
The Immediate Feedback Assessment Technique (IFAT) is a new multiple-choice response form that has advantages over more commonly used response techniques. The IFAT, which is commercially available at reasonable cost and can be used conveniently with large classes, has an answer-until-correct format that provides students with immediate, corrective, item-by-item feedback. Advantages of this learner-centered response form are that it: (a) actively promotes learning; (b) allows students’ partial knowledge to be rewarded with partial credit; (c) is strongly preferred by students over other response techniques; and (d) lets instructors more easily maintain the security of multiple choice (MC) items so that they can be reused from one semester to the next. The IFAT’s major shortcoming is that grading must be done manually because it does not yet have a compatible optical scanning device. Helpful suggestions are presented for instructors who may be considering using the IFAT for the first time. RÉSUMÉ La Technique D’Évaluation Immédiat (Immediate Feedback Assessment Technique ou IFAT) est un nouveau formulaire pour examens à choix multiple qui a plusieurs avantages. Le IFAT, disponible à un prix raisonable et convenable pour les cours suivis par de nombreux étudiants, est constitué d’un format dans lequel les édudiants selectionnent alternative-par- alternative parmi les choix disponibles jusqu’à ce que la réponse correcte soit indiquée. En suite, la correction est automatique et informe la réponse correcte immédiatement. Le IFAT a plusieurs avantages: (a) il favorise l’apprentissage; (b) les étudiants peuvent obtenir des points partiels avec connaissances partiels; (c) les étudiants préferent ce formulaire à comparer à autres formats à choix multiple; et (d) les instructeurs peuvent maintenir plus facilement leurs questions et alternatives en sécurité et les réutiliser au cours des prochaines sessions. Le défaut principal du IFAT est que la notation est manuele car il n’y a pas encore de lecteur optique compatible avec ce formulaire. Des suggestions utiles sont ici données pour les instructeurs qui envisagent d’utiliser cette technique pour la première fois.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".