Designing children’s media: taxonomies as a scaffold for learning and attention
Bibliographic record
Abstract
The goal of this study was to develop an educational video that emphasized taxonomic relationships as a means of supporting attention and learning for preschoolers. We used a design study method to iteratively design, develop, implement and evaluate a video. In each iteration we evaluated the success of the video based on children’s attention, as measured through eye-tracking, their recognition of target vocabulary words introduced in the video, and the relationship between attention and vocabulary recognition. In the first iteration we tested 56 children, who were assigned to view the taxonomic video or no video. Children who viewed the video had high levels of attention but were only marginally more likely to identify low-frequency vocabulary words. In the second iteration we altered the content of the video, and tested 88 students, who viewed the taxonomic video, no video, or a thematically-organized video. We found that children in the taxonomic group showed a similarly high level of attention as iteration one but were better able to identify low-frequency vocabulary words. Children’s attention to the video significantly predicted their recognition of vocabulary words. These results suggested that taxonomically-organized videos may have potential as a source of knowledge for young children.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".