The science education research trends (SERT) in Indonesian secondary schools: a systematic review and bibliometrics study
Bibliographic record
Abstract
Indonesian researchers have published a substantial number of research articles on science education. However, there is no overarching sense of the science education research landscape in Indonesia. The purpose of this study was to provide such an overarching sense with respect to science education research focused on Indonesian secondary schools between 2000 and 2020. Systematic review and bibliometrics methods were used to analyse 287 papers retrieved from Scopus. The study found that the publications have drastically increased since 2017 with only a few of them published in leading science education journals. International collaborations among Indonesian science educators have included many countries, such as Malaysia, Japan, South Korea, the United Kingdom, Australia, Thailand, and Canada. The most common research topics are critical thinking skills, problem-based learning, cooperative learning, HOTS, learning tools, blended learning, creative thinking skills, project-based learning, misconceptions, and lesson study, mostly researched through quantitative rather than qualitative methods. These findings are important for Indonesian science educators to assess their progress and identify areas for improvement to have a greater impact on the community. In the international context, these findings provide critical knowledge for global academics as they initiate and build international networks and collaborations to advance science education globally.
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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 | 0.020 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.081 | 0.079 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".