Bibliographic record
Abstract
Authentic learning (AL) is a learner-centred approach in which learners co-construct their own knowledge by engaging in and addressing real life problems that demand the use of higher order thinking skills (HOTS), real world resources and tools while thinking and acting like experts. However, AL is a concept that is ambiguous and abstract therefore challenges nurse educators in fully engaging learners in such problems thus limiting their development of HOTS. The purpose of this article was to describe the concept analysis process that was followed to clarify AL, provide conceptual meaning in nursing education, and formulate a theoretical definition using Walker and Avant’s eight-step method. Definitions, nature, characteristics and uses of AL were sought and the researchers explored 160 publications which included dictionaries, encyclopaedias, thesauri, conference papers, research reports, journal articles and subject-related literature across multiple disciplines to critically analyse AL. A 17-year period from 1988 to 2015 was used to search several databases. The defining attributes which included antecedents, the process and consequences of AL emerged. The consequence of AL in nursing education is a competent, critical, autonomous, independent, lifelong graduate desirable for the 21st-century global healthcare system. A theoretical definition of AL was also formulated. The study findings indicated that nurse educators can be assisted to design AL tasks that expose learners to AL thus implications were stated and recommendations were made.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".