Pedagogy and Subject Matter Knowledge in Early Childhood Teacher Education: Dewey, Kilpatrick, and Bestor
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".