STEM vs. STEAM Education and Student Creativity: A Systematic Literature Review
Bibliographic record
Abstract
STEM (science, technology, engineering and mathematics) is an educational approach that is now accompanied by the STEAM (STEM + Arts) variant. Both educational approaches seek to renew the scientific literacy of younger generations, and, with the inclusion of the arts, student creativity is described as a key skill that must receive special attention. A review is therefore presented here of empirical STEM and STEAM-based educational interventions so as to determine their potential to develop student creativity. A systematic search of papers over one decade, 2010–2020, found 14 didactic interventions on the Web of Science and Scopus databases for analysis within the review process. The analysis suggested that: (1) the interventions based both on STEM and STEAM have multiple and even contradictory forms, both in theory and in practice; (2) there appears to be a preference among researchers for the Likert-type test to evaluate creativity; and (3) both educational approaches show evidence of positive effects on student creativity. In the light of the principal findings, it was concluded that arguing for the implementation of STEAM education over STEM education, with a view to developing or promoting student creativity, is not in agreement with the evidence from the empirical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".