Science and engineering practices in science curricula: A comparative analysis of Thai, Vietnamese, Indonesian and Scottish curricula
Bibliographic record
Abstract
Science and engineering practices (SEPs) are one of the key learning goals of Science, Technology, Engineering, and Mathematics (STEM) education. There are few studies that compare similar SEPs in the science curricula of different countries. This study aims to compare SEPs in the science curricula of four countries (Indonesia, Scotland, Thailand, and Vietnam) in order to ascertain common knowledge and skills. Content analysis was used to analyse learning outcomes for grades seven to nine. The results showed that 1) desired learning outcomes in all four countries were consistent with science practices rather than with engineering practices and that they did not cover a number of SEPs. "Constructing scientific explanations" was found to have the highest frequency of the SEPs addressed in the curricula of the four countries, while "asking questions and defining problems" had the lowest overall average frequency. "Developing a model" was found more frequently in the Thai curriculum than in the Indonesian, Scottish, or Vietnamese curricula. The results of this study suggest that curriculum developers interested in broadening practices associated with science might revisit learning outcomes for the science curriculum in the areas of modelling and asking questions. Further research into the science curriculum could compare science or mathematics learning outcomes with the core disciplinary ideas, crosscutting concepts and the nature of each discipline, that are foundational in STEM education. Moreover, it would be worthwhile to investigate curriculum implementation of these practices by assessing teachers' instruction and students' STEM literacy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".