Exploring how children use their hands to think: an embodied interactional analysis
Bibliographic record
Abstract
In order to better understand how to design hands-on child-computer interaction, we explore how different styles of interaction facilitate children's thinking while they use their hands to manipulate objects. We present an exploratory study of children solving a spatial puzzle task. We investigate how the affordances of physical, graphical and tangible interfaces may facilitate the development of thinking skills including mental visualisation, problem space exploration and collaboration. We utilise the theory of complementary actions taken from embodied cognition to develop a video coding methodology that allows us to classify behavioural activity and make inferences about thinking skills development. Our findings indicated that the combination of direct hands-on input style with audio-visual feedback facilitated by the tangible user interface enabled a dynamic task completion strategy, which supports the development of mental skills with a slight time cost. The mouse and graphical user interface supported a trial and error approach, which may limit skills development. The physical cardboard puzzle enabled effective task completion but provided less support for social interaction and problem space exploration. We conclude with design recommendations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".