A pedagogical model for STEAM education
Bibliographic record
Abstract
Purpose Certain researchers have expressed concerns about inequitable discipline representations in an integrated STEM/STEAM (science, technology, engineering, arts and mathematics) unit that may limit what students gain in terms of depth of knowledge and understanding. To address this concern, the authors investigate the stages of integrated teaching units to explore the ways in which STEAM programs can provide students with a deeper learning experience in mathematics. This paper addresses the following question: what learning stages promote a deeper understanding and more meaningful learning experience of mathematics in the context of STEAM education? Design/methodology/approach The authors carried out a qualitative case study and collected the following data: interviews, lesson observations and analyses of curriculum documents. The authors took a sample of four different STEAM programs in Ontario, Canada: two at nonprofit organizations and two at in-school research sites. Findings The findings contribute to a curriculum and instructional model which ensures that mathematics curriculum expectations are more explicit and targeted, in both the learning expectations and assessment criteria, and essential to the STEAM learning tasks. The findings have implications for planning and teaching STEAM programs. Originality/value The authors derived four stages of the STEAM Maker unit or lesson from the analysis of data collected from the four sites, which the authors present in this paper. These four stages offer a model for a more robust integrated curriculum focusing on a deeper understanding of mathematics curriculum content.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".