Research trends in international science, technology, engineering, and mathematics education conference series: An analysis of a decade of proceedings
Bibliographic record
Abstract
This paper reports on the results of a descriptive analysis of 675 publications included in the proceedings of five editions of the International Science , Technology , Engineering , and Mathematics in Education Conferences held in Australia, Canada, and China over 9 years between 2012 and 2021. Peer-reviewed academic journals are traditional sources of high quality and established research. Yet these papers might take months and sometimes years to get published. On the other hand, an analysis of research foci represented at international STEM conferences can be used as a source of the most recent trends in the field, and the developments of STEM education research across time and by national origins. In this analysis, 675 abstracts from five editions of the International STEM in Education Conference were coded into eight topics, followed by a descriptive analysis of the results. Four countries were consistently the top four contributors: Australia, Canada, China, and the United States. Thus, the study conducted a descriptive analysis and discussion of the trends among these countries. The results revealed trends in STEM education research, among which policy, curriculum, assessment; K-12 teaching and learning, are dominant topics of research investigation, while postsecondary STEM teaching and learning are less investigated. The results also demonstrated the dominance of some topics over others among the countries that most contributed to the conferences. For instance, Canada dominated in the studies of culture, gender, and social issues and in postsecondary STEM education. China paid much less attention to these topics, while putting more emphasis on historical and epistemological perspectives on STEM, policy and curriculum, and K-12 teaching and learning. Australian research focused on policy and curriculum; K-12 teaching and learning; and industry. Meanwhile, the United States showed the least difference in percentages between topics. Overall the analysis indicates a relatively low interest in postsecondary STEM education research, despite the growing demand for STEM professionals. This highlights the need for STEM education research focused on institutions of higher education. This analysis interprets the trend results in light of the issues in STEM education confronting various national jurisdictions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".