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Record W2800824085

Learning Together about Culturally Relevant Science Teacher Education: Indigenizing a Science Methods Course

2018· article· en· W2800824085 on OpenAlexaffabout
Saiqa Azam, Karen Goodnough

Bibliographic record

VenueInternational Journal of Innovation in Science and Mathematics Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBachelorContext (archaeology)Teacher educationPedagogyScience educationCurriculumMathematics educationSociologyNarrativePsychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This paper captures our co-learning, two science teacher educators, about indigenizing a science methods course in Canada. A self-study was conducted in the context of a pilot bachelor of education program (IBED) for a group of Indigenous students, to engage ourselves in reflective conversations about transforming the curriculum of a science methods course and making it culturally relevant for pre-service science teachers. The purpose was to determine our tacit and personal knowledge, as it contributes to our understanding of inclusive science education practices. In particular, we focused our conversations on written reflection about the perceived effectiveness of pedagogies used by Saiqa, the first author, who was the course instructor. Karen, the second author and critical friend, carefully examined these reflective narratives and provided comments, which were then considered in the context of other course materials, to initiate an ongoing dialogue about culturally relevant teaching (CRT) as it relates to science teacher education. The findings are framed using an art- based concept, a circle, to present our co-learning journey. This allowed us to connect our personal histories to our role as inclusive science teacher educators in the present, and to consider our future aspirations to indigenize our science methods courses.

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)0.023
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0020.004
Scholarly communication0.0010.003
Open science0.0010.000
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 head0.029
GPT teacher head0.439
Teacher spread0.410 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2018
Admission routes2
Has abstractyes

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