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Record W4214716155 · doi:10.3389/feduc.2022.733047

Community-Anchored Assessment of Indigenous Second Language Learning in K-12 Schools

2022· article· en· W4214716155 on OpenAlexaffabout
Shelley Tulloch, Sylvia Moore, Jodie Lane, Sarah Townley, Joan Dicker, Doris Boase, Ellen Adams

Bibliographic record

VenueFrontiers in Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsInuit Tapiriit KanatamiGovernment of NunavutMemorial University of NewfoundlandUniversity of Winnipeg
Fundersnot available
KeywordsIndigenousIndigenous languageCurriculumIndigenous educationPedagogyLearning communitySociologyMathematics educationPsychology

Abstract

fetched live from OpenAlex

Indigenous second language programs in K-12 schools contribute to culturally nourishing education and to the revitalization of Indigenous languages. Assessing Indigenous second language learning presents particular opportunities and challenges based on the linguistic, historical, political, cultural, and social contexts in and for which the Indigenous language is being taught and learned. The self-governing Inuit region of Nunatsiavut is concerned with developing effective and appropriate tools for assessing students’ Inuttitut in order to evaluate how well K-12 programs are working so far, identify the basis on which future K-12 Inuttitut curriculum may be developed, and support ongoing assessment of learning and for learning in Inuttitut classrooms. This article discusses ways in which Inuit teachers in Nunatsiavut and a curriculum evaluation team have developed and implemented assessment tools and practices to evaluate Inuttitut learning in Nunatsiavut area K-12 schools. We discuss how Indigenous language learning and assessment, even when it occurs as part of an official school program, can be anchored in families and community. Families and communities need to be part of establishing language learning goals. Inuit teachers are drawing in full community resources and building a community of practice including Elders, other language speakers, leaders, principals, and teachers, to support and create contexts for community-anchored Inuttitut learning and assessment.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.206
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.428
Teacher spread0.403 · 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.

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

Citations5
Published2022
Admission routes2
Has abstractyes

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