Driving programme quality with pedagogical leadership: A case study in Singapore
Bibliographic record
Abstract
The preschool education landscape in Singapore comprises diverse operators that offer various care and educational services for children from birth to six years of age. This diversity has resulted in varying levels of quality and standards in early childhood education. Since positive child outcomes are strongly linked to high quality early childhood educational programmes, this paper discusses how a competent preschool leader establishes quality programmes through pedagogical leadership. The authors posit pedagogical leadership as a key driver for programme quality. By examining the effectiveness of pedagogical leadership using a case study, this article offers valuable insights into the roles and practices of a childcare centre principal that contribute to good teaching and learning practices in her centre curriculum. The case study investigates the enactment of pedagogical leadership through multiple perspectives – the principal, a kindergarten one teacher, six kindergarten one children and eight parents. Employing purposeful sampling, an exemplary childcare centre with the Singapore Preschool Accreditation Framework (SPARK) Commendation Award was chosen for this study. Data collection included artefacts, classroom observations, questionnaires, interviews and a survey. Analytic induction, coding and qualitative content analysis were used for data analysis. Through detailed descriptions, the narrative account provides insights into how an effective pedagogical leader has advanced programme and centre quality. Overall, the findings illustrate how this pedagogical leader had led her centre to achieve the SPARK Commendation Award for teaching and learning, and provided high quality programmes for the children in the centre.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".