Evaluating Playful Creative Problem Solving in Kyiv and Ukrainian Refugees in France
Bibliographic record
Abstract
Creativity in playful creative problem-solving (CPS) is influenced by the emotional state of the learner-player. In this study, we evaluated how stressful situations (such as conditions of war) can impact divergent thinking (DT) in CPS. To evaluate divergent thinking, the main components of which are fluency, flexibility, and originality, creativity researchers have developed the Alternative Uses Test (AUT), where participants are asked to write about different uses of familiar objects in a limited amount of time. In educational robotics, DT has been operationalized with the same components of fluency, flexibility, and originality based on the diverse configurations made by the learner-players. In the present research, we engaged participants in a playful activity with modular robotics to evaluate divergent thinking. Participants engaged in the CreaCube task are Ukrainians. We recruited two groups of participants: Ukrainian refugees living in France and participants staying in Kyiv (Ukraine). These participants are experiencing a stressful situation due to the daily war events in Kyiv during the development of this study (missile attacks, power and heating restrictions, air sirens, and limited communication with partners on the frontlines). By comparing these two groups of learner-players in the CreaCube task, we aim to identify how stressful situations affect divergent thinking. The preliminary results showed that flexibility and originality as DT components of the participants from Kyiv in the first experiment are significantly higher than those of the refugee participants based in France. This may indicate that the participants from Kyiv, having been in stressful situations (war conditions) for almost a year, have developed more original ideas (originality component) and different ideas (flexibility component), which could be explained by the daily problem-solving activity under war constraints. Playful situations under war conditions permitted the Kyiv participants to distract themselves from the current situation and show originality in thinking better than participants playing in France who were not in stressful situations. This may indicate that wartime constraints have engaged the participants in better strategies for generating novel ideas, but it also indicates that the interest in game activities has a positive effect on DT even in stressful situations for the participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".