Research trends in science education from 2013 to 2017: a systematic content analysis of publications in selected journals
Bibliographic record
Abstract
Following a series of reviews every 5 years since 1998, this fourth study presents the research trends in science education based on 1,088 research articles published in Science Education, Journal of Research in Science Teaching, and International Journal of Science Education from 2013 to 2017. The top three research topics, that is, the context of students’ learning, science teaching, and students’ conceptual learning were still emphasized by researchers in the period of 2013–2017. It is also evident that researchers have undoubtedly changed their preferences of research topics in the three journals within the 2 decades. For example, the topic concerning conceptual understanding, alternative conceptions, and conceptual change (Learning-Conceptions) was in continuous decline from 2003 to 2017, although it ranked as the top topic in the 1998–2002 period. The research topic of Teaching continuously ranked second in the 2008–2012 as well as in the 2013–2017 periods. Yet, the declining trend of Goals, Policy, and Curriculum reported in the last review was not observed in the latest period. The analysis of the top 10 most-cited papers unveiled that the issues such as inequality in science education, STEM education, and undergraduate research experiences were gradually highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.105 | 0.093 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| 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".