Scenario testing in undergraduate nursing education: Assessment for learning
Bibliographic record
Abstract
This paper describes an assessment activity called scenario testing used in an undergraduate nursing education program at Laurentian University in Sudbury, ON. In addition to describing scenario testing, this paper reports on the participants’ satisfaction with the experience and provides observations about the students’ grades on the scenario test and their final standings in the course. Argument is made for the idea that scenario testing can enhance students’ critical and integrative thinking as well as their personal confidence. The guiding theoretical principles for this activity were Brookfield’s (1987) work on critical thinking and Stiggins’ (2002) ideas about assessment for learning. Résumé Cet article décrit une activité d’évaluation nommée « essais fondés sur les scénarios », utilisée dans le cadre d’un programme de premier cycle en sciences infirmières à l’Université Laurentienne, à Sudbury (Ontario). En plus de décrire les essais fondés sur les scénarios, l’article rend compte de la satisfaction des personnes ayant participé à cette expérience et offre aussi des observations en ce qui concerne le rendement des étudiantes et étudiants à l’essai ainsi que leurs résultats finaux dans le cours. De plus, on invoque l’argument que les essais fondés sur les scénarios pourraient effectivement améliorer la pensée critique et intégrative des étudiantes et étudiants ainsi que leur confiance personnelle. Les principes théoriques qui ont dirigé cette activité sont l’oeuvre de Brookfield (1987) sur la pensée critique et les idées de Stiggins (2002) au sujet de l’évaluation dans l’apprentissage
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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 | 0.027 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".