Learner-Educator Co-creation: A Case for Enhancing Authentic Assessment in Nursing Education
Bibliographic record
Abstract
Authentic assessments support learner needs and meaningful learning by requiring students to use knowledge in a way that develops their competence to solve problems in professional contexts. Improved integration of authentic assessment in nursing education is needed to better prepare learners for practice. The focus of this critical discussion paper is on 1) improving existing definitions of authentic assessment, and 2) arguing that learner-educator co-creation is a strategy for improving authenticity. We advocate for including the following as essential elements of authenticity in nursing assessments; (1) realistic context or tasks; (2) cognitive complexity and appropriate challenge; (3) inclusion of strategies to promote evaluative judgement; and (4) degree of collaboration. Then we illustrate how co-creation improves each of these elements of authenticity, which has not been previously done in the literature. Combining authentic assessment with co-creation is argued to promote meaningful, inclusive, and caring learner-educator relationships that have a lasting impact on learner empowerment and readiness for nursing practice. Arguments in this paper are situated in a nursing context but drawn from multi-disciplinary literature. Readers are encouraged to consider how the value of combining co-creation and authentic assessment may be transferrable to other learning contexts.
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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 | 0.103 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.055 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".