New Vision and Challenges in Inquiry-Based Curriculum Change in Singapore
Bibliographic record
Abstract
A new primary science syllabus with strong inquiry focus has been implemented in Singapore since 2008. In this study, we attempted to understand how teachers experience the emphasis of inquiry-based curriculum under the current educational conditions that is routined and highly teacher fronted. We invited 50 pre-service and 41 in-service teachers to participate in survey questionnaires and narratives, reflective writings, and group discussions related to science inquiry which formed our data corpus. Data analysis in the form of thematic coding was carried out using NVivo8, with over 80% inter-rater coding agreement level. Three key aspects of teachers’ perceptions of science inquiry were revealed: (1) teachers’ responsibilities as facilitators, (2) privileging content knowledge rather than process skills, and (3) pressure of assessment systems in current educational contexts. These understandings bring out conflicts of inquiry teaching between teacher- and student-centredness, content and process, and curriculum and assessment. Based on these teachers’ perceptions and dilemmas of inquiry science teaching, the visions and challenges of inquiry science curriculum change against assessment requirements are discussed.
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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 | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 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".