Transdisciplinarity in STEM education: a critical review
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
Science, Technology, Engineering and Mathematics (STEM) education garnered significant attention in recent years and has emerged as a key field of research globally. The goal of this article is to offer a critical review of how STEM education and its transdisciplinarity were defined and/or positioned in empirical studies published during the early formulation of the field. In particular, we sought to identify how these studies conceptualise learners and learning and portray the underlying assumptions in light of the macrosystemic discourses that often serve as ideological forces in shaping research and practice of STEM education. We examined 154 peer-reviewed articles published between January 2007 and March 2018 and analysed them along several emergent dimensions: their geospatial focus, focal disciplinary areas, methodological and theoretical assumptions, and major findings. Grounded in a critical transdisciplinary perspective, we used critical discourse analysis to identify how macrosystemic and institutionalised forces – overtly and implicitly – shape what counts as STEM education research, including its goals and conceptualisations of learners and learning. Our analysis highlights the need for aesthetic expansion and diversification of STEM education research by challenging the disciplinary hegemonies and calls for reorienting the focus away from human capital discourse.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.017 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it