Assessment of Creativity in K‐12 Education: A Scoping Review
Bibliographic record
Abstract
Over the past two decades, creativity has emerged as one of the core 21 st century learning objectives within K‐12 education systems around the world. While some literature has demonised assessment as something that inhibits creativity, a growing body of research supports feedback‐driven teaching — also known as formative assessment or assessment for learning —as an effective pedagogical approach across contexts and content areas. Given this empirical foundation, we propose that assessment for learning holds powerful potential for helping students to learn about being creative. To examine intersections of creativity and assessment in K‐12 educational contexts, we carried out the scoping review study reported here, with the aim of advancing understanding of how assessment can support and promote creativity in classroom contexts. Fifty‐one research articles were selected for review, based on inclusion criteria which required that articles (a) reported the collection and analysis of quantitative or qualitative data, (b) addressed K‐12 classroom or extra‐curricular contexts, (c) addressed the formative or summative assessment of creativity for pedagogical intent, (d) were peer‐reviewed, and (e) were published in English. Analysis of the research revealed two dominant and consistent themes. Firstly, multiple studies indicated the importance of defined criteria for effective and useful creativity assessment within K‐12 classroom contexts. Secondly, a number of studies identified the particular value of self‐assessment and/or reflection in supporting creativity. We discuss implications of these findings in relation to educational policies and practices that seek to promote creativity, and areas for future research.
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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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".