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Record W3211163549 · doi:10.20360/langandlit29510

The Name Jar Project: Supporting Preservice Teachers in Working with English Language Learners

2021· article· en· W3211163549 on OpenAlexaffvenue
Theodora Kapoyannis

Bibliographic record

VenueLanguage and Literacy · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEllLiteracyPedagogyMathematics educationTeacher educationFeelingPsychologyFocus groupTeaching methodSociologyVocabulary development

Abstract

fetched live from OpenAlex

Classrooms are becoming more linguistically and culturally diverse and many educators are feeling unprepared to meet the varied needs of English language learners (ELLs). Through a larger design-based research doctoral study, I collaborated with 11 preservice teachers and 28 ELLs in Grades 2 and 3 to design and implement a literacy intervention that focused on cultivating literacy engagement to foster English language development. This paper documents the positive impact the implementation of the literacy intervention, also known as the Name Jar Project, had on supporting the preservice teachers’ emerging practice. Analysis of focus group data, preservice teachers’ written reflections, and field notes revealed that (a) the preservice teachers, through their informal learning experiences, were able to empathize with the ELLs’ strengths and challenges of learning English; (b) the service learning model provided a safe learning environment for preservice teachers to gain practical experience working with ELLs; and (c) through the research design, preservice teachers connected practice and theory to inform their future teaching 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)0.000
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 score0.141
Threshold uncertainty score0.772

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 studies0.0000.000
Scholarly communication0.0010.000
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 head0.016
GPT teacher head0.266
Teacher spread0.251 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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