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Record W4399324886 · doi:10.22329/jtl.v18i1.8056

‘My Most Tricky Pickle!’ Balancing Reading Instruction in Play-Based Kindergarten: Educator Self-Efficacy Beliefs and Pedagogical Content Knowledge Needs

2024· article· en· W4399324886 on OpenAlexaffvenue
Yvonne Messenger, Tiffany L. Gallagher

Bibliographic record

VenueJournal of Teaching and Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsBrock University
Fundersnot available
KeywordsReading (process)Computer scienceContent (measure theory)Mathematics educationPsychologyMultimediaPedagogyLinguistics

Abstract

fetched live from OpenAlex

Many kindergarten educators grapple with how best to teach reading in play-based kindergarten classrooms. The purpose of this mixed-methods study was to ascertain the instructional strengths and needs of kindergarten educators as they teach reading in play-based programs. Fifteen kindergarten teachers participated in an online questionnaire and focus group conversations that explored their concepts of self-efficacy and professional content knowledge to gain an understanding of the tensions these educators expressed, and to compare and confirm these with existing literature. Educators felt quite confident that they were effectively weaving foundational reading skills with learning opportunities into authentic experiences throughout the day. They indicated that balancing competing priorities within their programs was a challenge, and that supporting multilinguals and deepening their understanding of how to effectively build oral language and phonological awareness in their students were areas where they wanted to build their professional content knowledge.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.388
Teacher spread0.310 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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