Transdisciplinarity in STEM education: a critical review
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.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".