MétaCan
Menu
← Back to cohort
Record W4238921726 · doi:10.24124/2016/bpgub1183

Phonological awareness interventions in French immersion classrooms

2016· dissertation· en· W4238921726 on OpenAlexaff
Sonja Gowda

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of AlbertaUniversity of Northern British Columbia
Fundersnot available
KeywordsPhonological awarenessFluencyLiteracyPsychologyPsychological interventionVocabularySentenceComprehensionPedagogyMathematics educationComputer scienceLinguistics

Abstract

fetched live from OpenAlex

In this research project, I examined phonological awareness interventions (PAIs) in primary French Immersion classrooms. In detail, I discussed current research on the importance of phonological awareness (PA) as a foundation for literacy skills and the transferability of these pre-literacy skills across languages. In a focus group setting, I invited primary French Immersion teachers to share their knowledge and understanding of PA as well as share effective interventions to teach this pre-literacy skill to second language learners. Subsequently, I delivered a questionnaire to teachers asking them to rate the listed interventions for effectiveness and frequency of application. Results indicated that teachers had a varying degree of knowledge of PA, and therefore, PAIs. PAIs identified in this study match the research of current PAI and addressed the "big ideas" of literacy such as phonemic awareness, fluency, comprehension, vocabulary and the alphabetic principle. PAIs that were highest rated in frequency and effectiveness included building words on mini-chalkboards, cutting sentence strips, and identifying rhymes in stories and poems. --Leaf ii.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.375
Teacher spread0.331 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicReading and Literacy Development→French-language works237,207→