Embodied metaphors in tangible interaction design
Bibliographic record
Abstract
For centuries, learning and development has been supported by physical activity and manipulating physical objects. With the introduction of embedded technologies, opportunities for employing tangible or embodied interaction for learning and development have emerged. As a result of previous research, we have seen that interaction models based on embodied knowledge (through embodied metaphors) can support children’s learning in abstract domains. Although metaphorical mappings are promoted in tangible and embodied interaction research, little is known about how to identify embodied metaphors, or how to implement them effectively into interaction models. In this paper, we introduce a people-centered, iterative approach to the design of tangible learning systems with embodied metaphor-based mappings. As a design case, we implemented our approach to the design of Moving Sounds (MoSo) Tangibles; a tangible system for learning abstract sound concepts. The system consists of a set of interactive tangibles with which children can manipulate pitch, volume, and tempo of ongoing tones. In a user study with 39 participants, we found that all children were able to reproduce sound samples with MoSo Tangibles.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".