Balancing disciplinary and integrated learning: How exemplary <scp>STEM</scp> teachers negotiate tensions of practice
Bibliographic record
Abstract
Abstract Integrated STEM education within North America has become a popular pedagogy; however, teachers identify challenges that arise when planning for and implementing integrated STEM education. These challenges may threaten STEM teachers' capacity to balance disciplinary and integrated learning, a core feature of effective STEM education. The purpose of this study was to investigate how exemplary STEM teachers navigate tensions of practice to balance disciplinary and integrated learning. Through an in‐depth qualitative methodology, drawing on interview and artifact data from 14 purposefully selected exemplary secondary and elementary integrated STEM teachers, this study identified tensions that teachers faced as they navigated planning for and implementing integrated STEM education: (a) curriculum content versus skills; (b) guided instruction versus inquiry and play; (c) process versus task completion; and (d) collaboration versus individual needs. In line with a Worldly Perspective (Rennie et al., 2020), balancing these tensions leads to enhanced integration.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".