Research trends in science education from 2018 to 2022: a systematic content analysis of publications in selected journals
Bibliographic record
Abstract
This study uncovers research trends by analysing 1,142 papers published in Science Education, Journal of Research in Science Teaching, and International Journal of Science Education: Part A between 2018 and 2022, followed by a series of systematic reviews dating back to 1998. The main findings indicate that, during the period of 2018–2022, the three most studied research topics were associated with learner characteristics and classroom contexts (Learning-Context), teacher thinking/cognition and pedagogical issues (Teaching), and preservice education/in-service professional development (Teacher Education). An emerging interest in investigating the influence of cultural, social, and gender factors on science education (Culture, Social, and Gender) was observed. The analysis of the top 10 most-cited papers unveiled a notable focus on pertinent theoretical discussions and empirical research within the context of STEM/STEAM education. Besides, issues regarding engagement in and out of school settings, learners’ epistemologies, or sensemaking and science as practice were also highly cited. It is worth noting that there has been a rapid surge and new trend in research concerning science identity, garnering substantial attention by researchers as a meaningful lens for exploring relevant issues associated with participation and pipeline/career paths in STEM-related fields.
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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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.102 | 0.102 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".