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Record W3008194495 · doi:10.3102/0002831220908800

A Model for Assessment in Play-Based Kindergarten Education

2020· article· en· W3008194495 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Educational Research Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Methods and Practices
Canadian institutionsQueen's UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyAuthentic assessmentNegotiationAlternative assessmentPedagogyMathematics educationAssessment for learningEarly childhood educationFormative assessmentSociologyCurriculum

Abstract

fetched live from OpenAlex

Kindergarten teachers face the challenge of integrating contemporary assessment practices with play-based pedagogy. The current study addresses this challenge by presenting a kindergarten assessment framework rooted in theory and current classroom practices, based on teacher interview and observational data collected in 20 kindergarten classrooms. Ten teachers subsequently participated in extended observations and video elicitation interviews. Results uncovered seven different assessment pathways by which teachers mobilized learning goals through play pedagogies and assessment. Based on these pathways, a comprehensive assessment framework was developed underscoring the cyclical relation between student learning goals, types of play, and assessment contexts and practices. This framework supports teachers’ negotiation and integration of assessment practices with play-based pedagogies to promote both academic and developmental learning goals.

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.

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)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.462
GPT teacher head0.637
Teacher spread0.176 · 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