A Systematic Review Protocol to Identify the Key Benefits and Efficacy of Nature-Based Learning in Outdoor Educational Settings
Bibliographic record
Abstract
Outdoor Learning in natural environments is a burgeoning approach in the educational sector. However, the evidence-base of research has not kept pace with teacher perceptions and increased practitioner usage. Anecdotal evidence and formal research suggest the significant health and wellbeing benefits of nature connection. Offering low-cost, non-invasive pedagogical solutions to public health challenges-particularly around mental health, wellbeing, physical literacy, and increasing physical activity-the pedagogical benefits of Outdoor Learning are yet to be fully enunciated. The proposed systematic review will search for studies across eight academic databases which measure the academic and socio-emotional benefits of Outdoor Learning, with a focus on school-aged educational settings. Using the inclusion criteria set out in this paper (and registered with PROSPERO: CRD42020153171), relevant studies will be identified then summarised to provide a synthesis of the current literature on Outdoor Learning. The goal of this review is to document the widespread international investigation into Outdoor Learning and its associated benefits for development, wellbeing, and personal growth. The systematic review will provide insights for teacher-training institutions, educational policy makers, and frontline teachers to improve the learning experiences of future students.
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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 | 0.097 | 0.093 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.014 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.084 | 0.015 |
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".