Use of Feedback-Oriented Online Exercises to Help Physiology Students Construct Well-Organized Answers to Short-Answer Questions
Bibliographic record
Abstract
Postsecondary education often requires students to use higher-order cognitive skills (HOCS) such as analysis, evaluation, and creation as they assess situations and apply what they have learned during lecture to the formulation of solutions. Summative assessment of these abilities is often accomplished using short-answer questions (SAQs). Quandary was used to create feedback-oriented interactive online exercises to help students strengthen certain HOCS as they actively constructed answers to questions concerning the regulation of 1) metabolic rate, 2) blood sugar, 3) erythropoiesis, and 4) stroke volume. Each exercise began with a SAQ presenting an endocrine dysfunction or a physiological challenge; students were prompted to answer between six and eight multiple-choice questions while building their answer to the SAQ. Student outcomes on the SAQ sections of summative exams were compared before and after the introduction of the online tool and also between subgroups of students within the posttool-introduction population who demonstrated different levels of participation in the online exercises. While overall SAQ outcomes were not different before and after the introduction of the online exercises, once the SAQ tool had become available, those students who chose to use it had improved SAQ outcomes compared with those who did not.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".