Bibliographic record
Abstract
Universities are currently embracing community engagement strategies to increase opportunities for student learning in community settings such as community organizations. Experiential learning is often touted as the pedagogy underlying such experiences. We undertook a research project exploring the challenges and benefits for students and faculty who are offering integrated experiential curriculum in universities within North American Recreation and Leisure studies programs. We also address the ways in which interviewees defined experiential, and in particular, integrated experiential education. In the paper, we propose a model, defining the breadth of integrated experiential education approaches across continua of place, curriculum, philosophy, instructor role, and content. The model provides a tool for both understanding common aspects of integrated experiential approaches and identifying where specific experiential activities lie across these continua. De nos jours, les universités ont recours aux stratégies d’engagement communautaire afin de donner aux étudiants de plus grandes possibilités d’apprentissage dans les milieux communautaires tels que les organismes communautaires. L’apprentissage par l’expérience est souvent présenté comme étant la pédagogie sous-jacente à de telles expériences. Nous avons entrepris un projet de recherche visant à explorer les défis et les avantages pour les étudiants et les professeurs qui offrent des programmes d’études intégrés basés sur l’expérience dans les universités nord-américaines au sein de programmes d’Études en loisirs et en récréologie. Nous nous sommes également intéressés aux manières dont les personnes interviewées définissent les termes « basé sur l’expérience », en particulier l’enseignement intégré basé sur l’expérience. Dans cet article, nous proposons un modèle qui définit l’étendue des approches de l’enseignement intégré basé sur l’expérience indépendamment des lieux, des programmes d’études, des philosophies, des rôles des instructeurs et des contenus. Le modèle présente un outil pour comprendre les aspects communs des approches de l’enseignement intégré basé sur l’expérience et par ailleurs, il identifie où se situent les activités spécifiquement basées sur l’expérience d’un bout à l’autre du continuum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".