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Record W1820815689 · doi:10.22329/celt.v8i0.4242

The Interplay of Space, Place and Identity: Transforming Our Learning Experiences in an Outdoor Setting

2015· article· en· W1820815689 on OpenAlexaffvenueabout
Alice Cassidy, Alan N. Wright, William B. Strean, Gavan Watson

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2015
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversity of AlbertaWestern UniversityUniversity of WindsorUniversity of British Columbia
Fundersnot available
KeywordsPedagogySpace (punctuation)Identity (music)Value (mathematics)Higher educationSociologyOutdoor educationLearning environmentNatural (archaeology)PsychologySession (web analytics)Point (geometry)Mathematics educationPolitical scienceGeography

Abstract

fetched live from OpenAlex

In this paper, we use a day-long professional development workshop for higher education faculty conducted in an outdoor setting as the starting point for an examination of the value of such activities. We explore the potential benefits, in terms of learning and holistic well-being, of educational activities designed to provide participants with sessions either in the natural environment or the built (urban) environment beyond the four walls of the traditional classroom. Drawing on the literature of ‘place-based learning’, the well-established traditions of some conference organizations, the emerging trend to mount such pre-conference workshops in the Society for Teaching and Learning in Higher Education (STLHE: Canada) and the feedback of past participants, we explore the nature of these experiences and the various outcomes, grappling with the challenge of identifying tangible ‘takeaways’ at the individual and community levels. We conclude with directions for further analysis of the role of this type of session in terms of conference pedagogy and means of measuring impact on the well-being, outlook, and practices of instructors in higher education.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.121
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.361
Teacher spread0.345 · 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 teacher head, 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

Citations4
Published2015
Admission routes3
Has abstractyes

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