The Development and Application of Computational Fairy Tales for Elementary Students
Bibliographic record
Abstract
In the field of K-12 education, the demand for effective coding education is gradually expanding with various coding tools such as Scratch being popularly used as an effective learning environment. However, an answer to the question of what constitutes appropriate computing concepts for children (e.g. elementary school students) has not been fully answered. In this regard, this study worked to develop computational fairy tales (CFT) for coding beginners and applied it to the elementary school classroom environment in Korea. This CFT was developed by extracting the concepts of computer science through literature analysis, developing a plot/episode and creating a story. The final CFT developed is composed of 15 core computational concepts. 152 elementary students participated in the experiment where students read the CFT and solved related problems over two weeks period. We analyzed the effects of the CFT on the acquisition of basic computing concepts and coding attitudes. The results of the study showed that both the score of computing concept comprehension and attitude was enhanced significantly (p<.001). This study demonstrates the positive educational effects of CFTs in fostering understanding of basic computing concepts before students begin to code with algorithms directly.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".