Developing and Validating a Survey of Korean Early Childhood English Teachers’ Knowledge
Bibliographic record
Abstract
<p>The main purpose of this study is to develop and validate a valid measure of the early childhood (EC) English teacher knowledge. Through extensive literature review on second/foreign language (L2/FL) teacher knowledge, early childhood teacher knowledge and early childhood language teacher knowledge, and semi-structured interviews from current early childhood English teacher, the initial survey questionnaire was developed. Then, think-aloud interviews were conducted with samples from four groups with different teaching experience as they took the survey to see whether or not they understood the survey questions and whether or not the survey questions/statements represented their professional knowledge accurately. Lastly, a finalized survey was distributed to 40 current early childhood English teachers (K-2) in Korea. The theoretical framework of teacher knowledge was drawn upon Grossman’s (1999) “model of teacher knowledge” such as subject matter knowledge, general pedagogical knowledge, pedagogical content knowledge and knowledge of context. The data were analyzed by using descriptive statistics, Cronbach-alpha, and split-half. The findings will show subcomponent of teacher knowledge, characteristics of the participants and survey items’ reliability. The implications of analyzing teacher knowledge survey to gain insight into developing curriculum in early childhood English teacher education.</p>
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".