Bridging knowledge and practice: evidence from pre-service early childhood teachers’ PCK implementation in their teaching practicum
Bibliographic record
Abstract
This study investigates the role of Pedagogical Content Knowledge (PCK) in equipping pre-service early childhood education teachers with essential skills for addressing practical classroom challenges. Using a mixed-methods approach, the research analyzed data from 45 female pre-service teachers across six educational institutions, combining quantitative surveys with in-depth qualitative interviews. Statistical analysis revealed strong intercorrelations among teaching competencies (ρ = 0.58–0.78, p < .01), with a single dominant factor explaining 65.3% of competency variance. Curriculum confidence (β = 0.72, p = .020) and teaching aids competence (β = 0.61, p = .035) emerged as significant predictors of PCK integration ability. Thematic analysis identified four key dimensions of PCK implementation: concept simplification and concretization, multimodal teaching strategies, resource adaptation, and assessment-driven modifications. Notably, experience with special needs students showed positive correlations with developmental understanding (ρ = 0.29, p = .036), teaching aids competence (ρ = 0.28, p = .048), and PCK integration ability (ρ = 0.31, p = .024), though 72% of participants reported no such experience. The findings suggest that enhancing pre-service teacher preparation requires coordinated efforts across institutional, curricular, and practical training domains, with particular emphasis on expanding exposure to diverse learner needs and strengthening technological integration with traditional pedagogical approaches. This study contributes to the theoretical understanding of PCK development while offering practical implications for teacher preparation programs and professional development initiatives.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".