Easier Said than Done: STEM Subject Integration Through Engineering Design in Swedish Upper Secondary School
Bibliographic record
Abstract
Abstract Engineering design projects can enhance authenticity, increase relevance, and integrate STEM subjects without compromising their individual integrity. Nevertheless, the literature also warns that few STEM subject integration projects acknowledge students’ contexts and everyday problems. This study explores how teachers prepare for STEM subject integration in an engineering design project in a Swedish upper secondary school Technology programme and examines the process and outcomes of project implementation from both teachers’ and students’ viewpoints. The design project induces students’ solutions for bettering their everyday physical school context regarding well-being, feasibility, and sustainability. Collection of data employed participatory observations, and interviews with teachers and students. Results are presented as four themes: (1) integration and collaboration can be encouraged through project organization; (2) the engineering design process is the centrepiece of the integrated project; (3) models and modelling are primarily used for communication of design ideas; and (4) integration of STEM content and methods seldomly draws on more than two disciplines. Findings show that utilizing the school’s technology profile provides an accessible pathway to promote integrated STEM. However, although several teachers demonstrate enthusiasm for the real-world relevance of design projects, integration remains challenging. Since the project was mostly viewed as being technology and engineering-based, science and mathematics were present to a lesser degree, which made integration of all STEM subjects demanding. Nevertheless, the project could be seen as responding to an “extended STEM problem” in that components from health sciences were also incorporated.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".