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Record W2915436215 · doi:10.1002/trtr.1795

Play(ful) Pedagogical Practices for Creative Collaborative Literacy

2019· article· en· W2915436215 on OpenAlexfundno aff
Christine Portier, Nicola Friedrich, Shelley Stagg Peterson

Bibliographic record

VenueThe Reading Teacher · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAction researchLiteracyPedagogyMathematics educationCollaborative learningPsychologyAction (physics)

Abstract

fetched live from OpenAlex

Abstract With the goal of supporting students’ writing and content area learning using play as a pedagogical model, teachers’ action research projects involved kindergarten and grade 1 students collaborating to create texts for a range of purposes. The authors analyzed the project activities for their starting points or motivators, student and teacher roles, and artifacts. The activities took the form of small initiatives, a range of themes connecting curricular areas, and imaginative scenarios. Within each, teachers took on various roles to support student interactions and scaffold literacy learning, and students responded through collaboration, creative expression, and writing. Each activity addressed curricular objectives related to literacy while presenting students with opportunities to engage in collaborative, play(ful) learning with peers and the teacher and express their learning through creative means. These projects show that there does not have to be a disconnect between the achievement of curricular objectives and the implementation of play(ful) learning activities.

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)0.008
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.008
Scholarly communication0.0050.004
Open science0.0020.011
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.089
GPT teacher head0.422
Teacher spread0.333 · 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 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

Citations13
Published2019
Admission routes1
Has abstractyes

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