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Record W7061625436

The quality of the educational environment relating to emergent literacy practices: links to children’s engagement in preschool and kindergarten

2022· other· en· W7061625436 on OpenAlexaboutno aff

Bibliographic record

VenueArchipelago (University of Quebec in Montreal) · 2022
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracyReading (process)Quality (philosophy)Emergent literacyLearning environmentDimension (graph theory)
DOInot available

Abstract

fetched live from OpenAlex

The main objective of this study is to examine the relationships between the quality of the educational environment and the child's engagement in developmental and learning situations relating to emergent literacy in preschool and kindergarten classes in Québec (Canada). Oral language, reading, and writing are generally considered to be interrelated processes that correspond to emergent literacy in children ages 0 to 6 (Morrow et al., 2019). Through quality educational practices, the educational environment of the classroom supports the child's engagement in emergent literacy development and learning situations (Baroody & Diamond, 2016). The educational environment includes the physical dimension (classroom layout, resources, books, writing materials) as well as the interactive dimension (teacher and child interactions) (Guo et al., 2012). Child engagement refers to interactions with the teacher, peers and his or her involvement within tasks and activities (Bohlmann et al., 2019; Downer et al., 2010). \n \nThe quality of these interactions plays an essential role in the development of 4 and 5-year-olds' emergent literacy and in their current and later educational success (Sabol et al., 2018). As part of the \nimplementation of the new integrated preschool and kindergarten educational program in Québec (MEQ, 2021), it is important to develop knowledge regarding the quality of the educational environment and the child's interactions within this environment considering the importance of language \ndevelopment in preventing later reading and writing difficulties. This research project has 3 objectives: \n \n1) To measure the quality of the physical environment and the interactive environment relating to emergent literacy in preschool and kindergarten classrooms; 2) To measure children's level of engagement in their emergent literacy development and learning situations; 3) To analyze the \nrelationships between the quality of the educational environment relating to emergent literacy and children's level of engagement in these emergent literacy development and learning situations. \n \nThe sample will consist of 30 preschool and kindergarten teachers and children in their class (N=150). The classroom observations will allow us to assess the overall educational environment supporting emergent \nliteracy using the ELLCO Pre-K observation tool (Smith et al., 2008). On the other hand, the inCLASS observation tool (Downer et al., 2010) will measure children's level of engagement with the teacher, peers and tasks in emergent literacy development and learning situations. The results will contribute to a better understanding of the relationships between the quality of the educational environment (physical and interactive) and children's engagement in their emergent literacy experiences. This new \nknowledge will support research-based educational practices that promote children's educational success (MEES, 2018). In addition, the results can be incorporated into pre-service and in-service teacher training as the new preschool and kindergarten curriculum is implemented.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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
Published2022
Admission routes1
Has abstractyes

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