MétaCan
Menu
Back to cohort
Record W2022547046 · doi:10.1002/sce.20356

Learning on zoo field trips: The interaction of the agendas and practices of students, teachers, and zoo educators

2009· article· en· W2022547046 on OpenAlexaff
Susan Kay Davidson, Cynthia Passmore, David P. Anderson

Bibliographic record

VenueScience Education · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsField tripPerceptionTRIPS architecturePsychologyPedagogyMathematics educationClass (philosophy)Field (mathematics)Value (mathematics)Political science

Abstract

fetched live from OpenAlex

Abstract This paper reports on the findings of a case study that investigated the interaction of the agendas and practices of students, teachers, and zoo educators during a class field trip to a zoo. The study reports on findings of the analysis of two case classes of students and their perceptions of their learning experiences during the field trip. The goals, expectations, and perceived outcomes of the trip for students, their classroom teachers, and the zoo educators were elicited through interviews, surveys, student work, and observations. Both cases demonstrated how students placed high value and importance on social interactions with their peers. In addition, classroom teachers' pedagogical practices and the learning agendas they held for their students had a significant influence on students' subsequent learning and perceptions of the experience. This was in contrast to the zoo educators' practices and agendas that appeared not to be significant influences on student learning and perceptions. Implications for field trip planning and implementation are discussed. © 2009 Wiley Periodicals, Inc. Sci Ed 94:122–141, 2010

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)0.010
metaresearch head score (Gemma)0.017
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 score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0070.007
Scholarly communication0.0080.004
Open science0.0010.006
Research integrity0.0020.002
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 head0.040
GPT teacher head0.356
Teacher spread0.317 · 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

Citations85
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueScience EducationSame topicMuseums and Cultural HeritageFrench-language works237,207