Editorial: Special Issue “Promoting STEAM in Education”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lately STEAM (science, technology, engineering, art/aesthetics/architecture/all, mathematics) education has become a common notion. Yet, the theoretical and practical perspectives on STEAM, from its nature to classroom applications and its implementation in teacher education have unexamined potential. This special issue grew out of the International LUMAT Research Symposium “Promoting STEAM in Education” that took place at the University of Helsinki, Finland in June of 2020. With the challenges of organizing an online symposium in the midst of the COVID-19 pandemic, its online nature had significant advantages. The symposium drew international scholars inviting a multitude of prospective on STEAM education, while uncovering the challenges faced by educators. The issue aims at examining these challenges through a collection of papers. In this editorial, we introduce some key notions, discourses, and challenges of STEAM education, as a relatively novel concept and briefly discuss the history of STEAM and its evolution over the last decades. We also problematize STEAM and its roots through asking a question: What is “A” in STEAM representing? Then we introduce the three articles in this special issue: “Full STEAM ahead, but who has the map? – A PRISMA systematic review on the incorporation of interdisciplinary learning into schools”; Promoting STEAM learning in the early years: ‘Pequeños Científicos’ Program”; and “Promoting student interest in science: The impact of a science theatre project”. These articles challenge us to rethink STEAM education, reveal the potential of STEAM, and offer ideas for future research.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it