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Record W3158665475

Pedagogy and Subject Matter Knowledge in Early Childhood Teacher Education: Dewey, Kilpatrick, and Bestor

2016· article· en· W3158665475 on OpenAlexvenueno aff
Jonghyun Lee

Bibliographic record

VenueEarly childhood education · 2016
Typearticle
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsTeacher educationEarly childhood educationSubject (documents)CurriculumPedagogyEarly childhoodSubject matterMathematics educationPsychologyDevelopmental psychologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Currently, most educators in Korea recognize that pedagogical content knowledge (PCK) is an important element of teachers` professionalism, and many studies of the factors that influence teachers` PCK have been conducted across education at all levels. PCK concerns how a teacher, based on his/her subject matter knowledge, transfers subject matter content to learners. In early childhood education in Korea, an integrated curriculum that combines two or more subjects is used. To implement this approach, most early childhood teachers seek to develop a range of pedagogical methods. However, it may be true that, in early childhood education (which emphasizes integrated, play-centered activities), teachers may have difficulty in acquiring sufficient depth of knowledge in each subject area. Therefore, one important task for early childhood teacher education is to help pre-service early childhood teachers establish the necessary foundation of knowledge. Early childhood teachers must have both subject matter knowledge and pedagogical knowledge in order to be successful. This article analyzes how three scholars, Dewey, Kilpatrick, and Bestor, addressed the issue of the relative roles of subject matter knowledge and pedagogical knowledge in teacher education. By analyzing the different positions of these three scholars, this article seeks to offer some insights that may help early childhood teacher education programs to prepare prospective early childhood teachers for success in their future professions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.472
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.012
GPT teacher head0.303
Teacher spread0.290 · 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 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

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