MétaCan
Menu
Back to cohort
Record W2617147240 · doi:10.12973/eurasia.2017.00833a

Teaching and Learning Science Outdoors in Schools’ Immediate Surroundings at K-12 Levels: A Meta-Synthesis

2017· article· en· W2617147240 on OpenAlexaff
Jean‐Philippe Ayotte‐Beaudet, Patrice Potvin, Hugo G. Lapierre, Melissa Glackin

Bibliographic record

VenueEurasia Journal of Mathematics Science and Technology Education · 2017
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsScience learningOutdoor educationMathematics educationScience educationPsychologyEnvironmental educationLearning sciencesPerceptionExperiential learningPedagogy

Abstract

fetched live from OpenAlex

This literature review synthesizes empirical data of 18 articles published between 2000 and 2015 about teaching and learning science outdoors from kindergarten to secondary levels (K–12). We asked four questions: (1) What are the general characteristics of the corpus of studies on teaching and learning science outdoors in schools’ immediate surroundings at K–12 levels? (2) What are the authors’ aims for conducting studies about teaching and learning science outdoors? (3) What are the main outcomes related to teaching and learning science outdoors in schools’ immediate surroundings? (4) What further studies should, according to the selected articles, be conducted in the future? We identified three categories of authors’ aims: environmental education, science education, and outdoor education. The main outcomes are classified into four categories: 1) learning, 2) student attitude or interest, 3) other students’ perceptions, and 4) challenges to outdoor science teaching. Finally, in light of the review, we discuss how further studies should consider learning outcomes, students’ attitudes, challenges, and methodological guidelines.

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)0.015
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.062
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.008
Bibliometrics0.0180.017
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.053
GPT teacher head0.374
Teacher spread0.321 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations69
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueEurasia Journal of Mathematics Science and Technology EducationSame topicOutdoor and Experiential EducationFrench-language works237,207