Design of embodied interfaces for engaging spatial cognition
Bibliographic record
Abstract
Aspects of spatial cognition, specifically spatial skills, are strongly correlated with interest and success in STEM courses and STEM-related professions. Because growth in STEM-related industries is expected to continue for the foreseeable future, it is important to develop evidence-based and theoretically grounded methods and interventions that can help train relevant spatial skills. In this article, we discuss research showing that aspects of spatial cognition are embodied and how these findings and theoretical developments can be used to influence the design of tangible and embodied interfaces (TEIs). TEIs seek to bring interaction with digital content off the screen and into the physical environment. By incorporating physical movement and tangible feedback in digital systems, TEIs can leverage the relationship between the body and spatial cognition to engage, support, or improve spatial skills. We use this knowledge to define a design space for TEIs that engage spatial cognition and illustrate how TEIs that are designed and evaluated from a spatial cognition perspective can expand the design space in ways that contribute to the fields of cognitive science and human computer interaction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".