MétaCan
Menu
← Back to cohort
Record W6884651361 · doi:10.11575/prism/41598

Nature-Based Social–Emotional Learning: An Exploratory Qualitative Study in Alberta

2023· other· en· W6884651361 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyExploratory researchVariety (cybernetics)Qualitative researchNatural (archaeology)Foundation (evidence)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.798

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0180.010
Scholarly communication0.0040.002
Open science0.0040.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.105
GPT teacher head0.435
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOpen MIND→French-language works237,207→