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Record W2923271142

An analysis of curriculum and pedagogy through a consideration of outdoor learning perceptions and practices in teacher education courses: an update on a study in progress.

2019· article· en· W2923271142 on OpenAlexaffabout
Hartley Banack

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOutdoor educationCurriculumPedagogyService-learningExperiential learningPerceptionFocus groupTeacher educationEnvironmental educationActive learning (machine learning)PsychologyMathematics educationMedical educationSociologyMedicineComputer science
DOInot available

Abstract

fetched live from OpenAlex

Much literature indicates that time spent outdoors (TSO): 1) is healthy for us, 2) develops key environmental practices, and 3) enhances learning.  However, most learning experiences, in K-12 and post-secondary, predominantly take place indoors.  It is posited that for a shift to occur to increase TSO, teachers (pre- and in-service) require intentional induction to outdoor learning as a practice. This project considers shifts in pre-service teacher outdoor learning perceptions and practices through Teacher Education courses in a large Faculty of Education in Western Canada through an emphasis on outdoor learning. Participants: 1) track and reflect on where their learning experiences occur, 2) design outdoor lessons/unit plans, and 3) attend focus-group discussions around outdoor learning. The project aims to understand how outdoor teaching and learning experiences during teacher education courses impact pre-service teacher perceptions and practices of curriculum and pedagogy.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.433
Teacher spread0.391 · 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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venue2019 Conference of the Canadian Society for the Study of EducationSame topicOutdoor and Experiential EducationFrench-language works237,207