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Record W2964497869 · doi:10.1130/abs/2019am-332303

MERGING EARTH SCIENCE WITH ENVIRONMENTAL EDUCATION FOR TEACHERS THROUGH INQUIRY, CONSTRUCTIVIST AND PLACE-BASED LEARNING

2019· article· en· W2964497869 on OpenAlexaff
Ankun Ma, Eileen van der Flier Keller, David B. Zandvliet, Kevin Cameron

Bibliographic record

VenueAbstracts with programs - Geological Society of America · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConstructivist teaching methodsMathematics educationComputer scienceEarth (classical element)Earth scienceAstrobiologyPsychologyTeaching methodGeologyPhysicsAstronomy

Abstract

fetched live from OpenAlex

Recognizing the importance of interdisciplinarity and effective pedagogical implementation, Earth science is incorporated into three of the six modules of an Environmental Education course (EDU452) at Simon Fraser University for teachers, through inquiry, constructivist and place-based learning. The case study research encompasses two pilots with 52 participants in summer 2018 and 2019. Field observations, interviews and pre- and post-course surveys (MESEES) were employed to evaluate the course effectiveness and pedagogies. EDU452 increased students’ recognition of Earth Science being fundamental and relevant. Inquiry learning promoted engagement, yet careful design of overarching questions and guidance adjusted according to individual’s pre-existing knowledge and the conceptual difficulty are recommended. Constructivist and place-based learning are widely accepted by the participants for offering personal ownership of learning, engaging experiences, impactful visuals, and local relevance of knowledge. Conceptual and experiential learners have different understandings of the role of Earth Science. An integrated teaching strategy is believed to enhance the congruency across subjects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.300
Teacher spread0.281 · 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 designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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