Nurturing gerontology students’ intrinsic motivation to cocreate: The design of a powerful learning environment
Bibliographic record
Abstract
Professionals such as gerontologists play an important role in the design, development and implementation of age-friendly services. and products, by using working methods and principles of co-creation. A Dutch undergraduate applied gerontology programme aims to train students in the why, how and what of co-creation. The degree to which students are intrinsically motivated to develop competencies depends on how their psychological needs are met. These needs are autonomy, an awareness of competence and a sense of relatedness, as described in the self-determination theory. To nurture the intrinsic motivation of the applied gerontology students, a realistic, powerful learning environment called the Living Lab Applied Gerontology was designed and implemented. The aim of this paper is to present the design of this powerful learning environment and to discuss its value for nurturing the students' intrinsic motivation for co-creation. Based on a focus group with eight students, we identify directions for further research and development of living labs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".