STEAM education: student learning and transferable skills
Bibliographic record
Abstract
Purpose Globally, interdisciplinary and transdisciplinary learning in schools has become an increasingly popular and growing area of interest for educational reform. This prompts discussions about Science, Technology, Engineering, Arts and Mathematics (STEAM), which is shifting educational paradigms toward art integration in science, technology, engineering and mathematics (STEM) subjects. Authentic tasks (i.e. real-world problems) address complex or multistep questions and offer opportunities to integrate disciplines across science and arts, such as in STEAM. The main purpose of this study is to better understand the STEAM instructional programs and student learning offered by nonprofit organizations and by publicly funded schools in Ontario, Canada. Design/methodology/approach This study addresses the following research question: what interdisciplinary and transdisciplinary skills do students learn through different models of STEAM education in nonprofit and in-school contexts? We carried out a qualitative case study in which we conducted interviews, observations and data analysis of curriculum documents. A total of 103 participants (19 adults – director and instructors/teachers – and 84 students) participated in the study. The four STEAM programs comparatively taught both discipline specific and beyond discipline character-building skills. The skills taught included: critical thinking and problem solving; collaboration and communication; and creativity and innovation. Findings The main findings on student learning focused on students developing perseverance and adaptability, and them learning transferable skills. Originality/value In contrast to other research on STEAM, this study identifies both the enablers and the tensions. Also, we stress ongoing engagement with stakeholders (focus group), which has the potential to impact change in teaching and teacher development, as well as in related policies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".