WooGu: Exploring an Embodied Tangible User Interface for Supporting Children to Learn Farm-to-Table Food Knowledge
Bibliographic record
Abstract
Food is essential for human health, growth, and development. However, children need more learning materials and motivation to receive food literacy education or know the fundamental food processes from farm to table. In this work, we explored the design of a prototype named WooGu with tangible user interfaces (TUI) and embodied interactions, which aims to improve young children’s food literacy. WooGu presents three design features: a cube displaying user interfaces, step-by-step tasks guiding children to learn food from farm to table, and hands-on props made by cardboard empowering embodied interactions. We evaluated WooGu with two families in a pilot test, and the findings suggested that WooGu provides children with the embodied experience of food production, improving their food literacy, logical thinking, and practical ability. This research contributed to the human-food interaction area and provided a novel way of learning food literacy for children through embodied interactions with WooGu.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".