Nature-Based Social–Emotional Learning: An Exploratory Qualitative Study in Alberta
Bibliographic record
Abstract
Research in psychology and education is beginning to show that nature-based learning (NBL)—learning about the natural world, in natural settings, or using natural elements indoors—promotes academic, social, and emotional learning by improving learners’ attention, self-discipline, and enjoyment in learning, along with opportunities for experiencing empathy, engaging in teamwork, and fostering environmental stewardship. Social–emotional learning (SEL) entails how individuals acquire and apply the knowledge, skills, and attitudes to develop healthy identities, manage emotions, achieve personal and collective goals, feel and show empathy for others, establish and maintain supportive relationships, and make responsible and caring decisions. The state of the evidence points to complementary effects between NBL and SEL, including in developing perseverance, self-efficacy, and resilience, and in promoting emotion regulation, social skills, and responsible and ethical behaviour such as environmental stewardship. However, little research has examined the intentional integration of these two fields, or what may be called nature-based social–emotional learning (NBSEL). Using an exploratory-descriptive qualitative design, this study interviewed a sample of Alberta teachers to explore how they are currently using NBL to promote SEL in K–12 students, as well as barriers and advice to doing this work. This study presents exploratory and preliminary findings on a variety of NBL practices and principles that have helped in fostering students’ SEL. Teachers have experienced unique challenges in delivering NBSEL, but they recommended several actions to overcome any barriers and to begin or advance this work. With its contextualized perspectives from educators who are currently practicing NBSEL in Alberta, this study opens avenues and creates a foundation for future research on NBSEL. Strengths and limitations are discussed, along with implications for practice and directions for future research.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".