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Different Worlds Same Province: Blended Learning Design to Promote Transcultural Understanding in Teacher Education

2020· article· en· W3119817216 on OpenAlexaffvenueabout
Kathy Snow

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsTransformative learningMainstreamIndigenousPedagogyThematic analysisBachelorSociologyContext (archaeology)PsychologyQualitative researchPolitical scienceSocial science

Abstract

fetched live from OpenAlex

The history of Canada’s educational offerings for Indigenous students was based in colonial and assimilative practice. As such culturally responsive pre-service teacher education needs to respond not only by moving away from historical practice but with a moral and social imperative through programming that aids reconciliation. Current literature outlines the challenges that both Indigenous and mainstream teacher candidates have in developing efficacy towards transcultural skills development. In an effort to respond to both types of students, during the design and development of a 16-month community-based Bachelor of Education program, that was offered in parallel to a campus based program, a model of blended-education for cultural understanding was developed. Using the “elementary science methods” course as design case, this paper will outline the development of the blended model which paired campus- and community-based students. The challenges and successes of the design were determined through a thematic analysis of instructor observations of the pilot during the 2014-15 academic year. Four key themes emerged as important in fostering transcultural understandings within blended learning practice: student efficacy, relationship building, recognition of cultural bias, and legitimizing traditional ecological knowledge. Each of these will be discussed in the context of the course as well as transformative post-secondary educational experiences.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.005
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 head0.124
GPT teacher head0.336
Teacher spread0.212 · 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 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
Published2020
Admission routes3
Has abstractyes

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