Exploring Children’s Online Summer Camp Adventures through Creativity and Problem Solving
Bibliographic record
Abstract
Summer camps can help children continue to learn beyond school, build knowledge, keep their learning skills sharp, and help them prepare for the following school year. This paper presents participants’, facilitators’, and researchers’ experiences in a “Problem Solvers Camp” held in the Maker Lab at an Ontario University. A total of 12 junior students participated in a one-week summer camp, during which the participants developed plausible solutions for mathematical and instant problems using their creativity while learning some mathematical concepts. The creative learning spiral (CLS) model was adopted while designing the learning activities. Throughout the camp, children had the opportunity to work with virtual tech tools to design, create, and play to complete their challenges. Afterward, they shared their work for feedback and generated new ideas to promote their creative learning. The data were collected through observations, participants’ work, and their portfolios to highlight the campers’ experiences throughout the camp. On the last day of the camp, the researchers also ran focus group interviews. Data analysis showed that CLS might offer engaging environments that enhance children’s creative and reflective thinking skills to solve real-life problems. This study enabled children to engage in all stages of the CLS during problem solving, encouraging the exchange of ideas and opinions. The implementation of the CLS model also has the potential to inspire creativity and enhance learners’ fluency and elaboration skills, especially when complemented by technological or coding tools.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".