Research Trends and Hotspots in the Integrated Science Curriculum (1947–2024): A CiteSpace Analysis
Bibliographic record
Abstract
The integrated science curriculum has become a central theme in global education reforms, yet its research development remains fragmented.This study employs CiteSpace 6.3 to conduct a scientometric analysis of 350 publications retrieved from Web of Science, Scopus Abstract and Citation Database, and China National Knowledge Infrastructure .Publication trends reveal three phases: marginal development (1947-1995), gradual growth (1996-2005), and rapid expansion linked to STEM initiatives and the NGSS (2005-2019), followed by a decline after 2020 due to the COVID-19 pandemic.The United States, China, and Canada dominate contributions, with the Texas A&M University System and the Purdue University System identified as leading institutions.Author and institutional networks highlight active but regionally clustered collaborations.Keyword co-occurrence indicates that curriculum design, student learning, and teaching practices remain consistent research themes.Keyword clustering demonstrates interdisciplinary expansion into sustainability, computer science, and nanoeducation, reflecting broader societal and technological agendas.Keyword burst detection identifies recent surges in "science curriculum" and "students" (2021-2024), signaling growing emphasis on curriculum innovation and learner engagement.These findings provide a systematic visualization of integrated science curriculum research hotspots, offering valuable insights for both future scholarship and educational policy.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.094 | 0.137 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".