STEAM – Arts Integration Frameworks for Transdisciplinarity
Bibliographic record
Abstract
Whether for 21st century skills development such as creativity, communication, and collaboration orfor transdisciplinary knowledge creation leading to innovation, the integration of Arts with STEM (Science,Technology, Engineering, Mathematics) fields is gaining popularity in higher education. However, a comprehensive survey of proven methodologies to integrate Arts with STEM disciplines (referred to as STEAM) currently does not exist. This paper presents the preliminary results of asystematized literature review done to characterize the integration of arts with STEM disciplines in higher education. It also uses these findings to analyze the most recent STEAM initiative of the Faculty of Engineering at the University of Ottawa. This research finds three main rationales to integrate arts with STEM and presents the frameworks discussed in the literature to do this integration. It also examines how creativity is assessed and developed within STEAM higher education contexts. This research contributes a reference of validated arts integration and creativity frameworks which can be used to setup STEAM projects or evaluate them in relation to proven methodologies. The frameworks presented in this research can be used in classrooms and professional environments.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".