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Record W4382466970 · doi:10.1007/978-3-031-31678-4_5

Effective Teaching: Linking Outcomes of Active Citizenship to Learning Environments

2023· book-chapter· en· W4382466970 on OpenAlexaffabout
Gordon Sturrock, David B. Zandvliet

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsSimon Fraser UniversityBritish Columbia Institute of TechnologyDouglas College
Fundersnot available
KeywordsCitizenshipExperiential learningLearning environmentActive citizenshipPerceptionActive learning (machine learning)PsychologyComputer scienceMathematics educationPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This chapter discusses the use of a learning environment instrument, the Place-Based Learning and Constructivist Environment Survey (PLACES) in an environmental studies program that operated out of British Columbia, Canada. In order to access information about students’ perceptions, the instrument was implemented in an Integrated Environmental Studies program called Experiential Studies 10 (ES 10) as part of a range of evaluation methods. The study was retrospective in nature utilizing a mixed method approach to determine the long-term effects of the program on participants’ citizenship activities. Our findings demonstrate that learning environment and citizenship outcomes were linked, and key learning environment features were identified as being important for long term outcomes of active citizenship. This chapter will provide a brief overview of the study and shed light on how paying close attention to the learning environment created within environmental education programming can contribute to long-term outcomes of active citizenship.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.036
GPT teacher head0.338
Teacher spread0.302 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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 routes2
Has abstractyes

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