Getting Down to Details: Using Theories of Cognition and Learning to Inform Tangible User Interface Design
Bibliographic record
Abstract
Many researchers have suggested that tangible user interfaces (TUIs) have potential for supporting learning. However, the theories used to explain possible effects are often invoked at a very broad level without explication of specific mechanisms by which the affordances of TUIs may be important for learning processes. Equally problematic, we lack theoretically grounded guidance for TUI designers as to what design choices might have significant impacts on learning and how to make informed choices in this regard. In this paper, we build on previous efforts to address the need for a structure to think about TUI design for learning by constructing the Tangible Learning Design Framework. We first compile a taxonomy of five elements for thinking about the relationships between TUI features, interactions and learning. We then briefly review cognitive, constructivist, embodied, distributed and social perspectives on cognition and learning and match specific theories to the key elements in the taxonomy to determine guidelines for design. In each case, we provide examples from previous work to explicate our guidelines; where empirical work is lacking, we suggest avenues for further research. Together, the taxonomy and guidelines constitute the Tangible Learning Design Framework. The framework advances thinking in the area by highlighting decisions in TUI design important for learning, providing initial guidance for thinking about these decisions through the lenses of theories of cognition and learning, and generating a blueprint for research on testable mechanisms of action by which TUI design can affect learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".