Efforts to Increase Creativity through the Creation of Woven Patchwork Cloth for Children Aged 5-6 Years
Bibliographic record
Abstract
This study aims to enhance the creativity of 5-6-year-old children through the creation of woven patchwork fabric at TK IT Auladi Palembang. The research method used is Classroom Action Research (CAR) with the Kemmis and McTaggart model, which consists of planning, implementation, observation, and reflection across two cycles. The subjects of this study were 20 children from Group B, selected based on their developmental stage and involvement in early childhood education, ensuring they were appropriate for assessing creativity development through hands-on activities like woven patchwork creation. Data was collected through observation and documentation, with observation focusing on children’s engagement, participation, and creativity during the activity, while documentation provided additional insights into the learning process and outcomes. The indicators for observation and documentation included children’s ability to follow the weaving process, creativity in the design of the patchwork, and overall engagement in the task. Data analysis was both qualitative and quantitative. Quantitative analysis calculated the percentage of children who demonstrated specific levels of creativity across each cycle, showing 66% of children starting to develop in Cycle I and 83% reaching a “very well developed” category in Cycle II. Qualitative analysis focused on field notes and observations to gain deeper insights into children’s development. The results showed a significant increase in creativity after implementing woven patchwork activities, proving the method’s effectiveness in fostering creativity in early childhood education.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".