Observation: unlocking, assessing, and nurturing creative problem solving
Bibliographic record
Abstract
The purpose of this article was to advocate and provide methods for observation of behavior as a method for assessing and developing creativity during problem solving. To accomplish this purpose, the authors (a) outlined the advantages of observing people's actions as a method for unlocking, assessing, and nurturing creative problem solving; (b) explained briefly the conceptual framework for the research underlying these recommendations; (c) presented results of research on observable behaviors that are indicators of abilities in creative problem solving in diverse domains of talent; and (d) described activities, experiences, and materials that have been used to unlock, assess, and nurture creative problem-solving capability and skills. Research on the Discovering Intellectual Skills and capability while Observing Varied Ethnic Responses (DISCOVER) in which performance-and play-based assessments of children and young adults (ages 3 to adult) were designed, field-tested, and validated over a 37-year period were the basis for making recommendations for behaviors to observe and methods for eliciting them. Research has been conducted in several countries (e.g., Australia, Bahrain, Canada, Chile, France, Hong Kong, Lebanon, Mexico, Taiwan, Thailand, and United Arab Emirates) and languages (e.g., Arabic, Chinese, English, French, Navajo, Spanish, Thai, and Taiwanese).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".