Lowering the Barriers to Designing and Creating Electronics-Based Tangibles for Learning Programming
Bibliographic record
Abstract
Tangible interfaces are a powerful medium for learning, yet their creation requires technical expertise in coding and electronics. My first research addressed this with TangiBooks, a system using sensor-augmented paper to simplify learning programming concepts. My subsequent research tried to lower the barrier to authoring tangible lessons using modular hardware and a web-based lesson editor, revealing a strong user demand for more flexible and capable tangible authoring tools, especially for creating novel interactions. This insight motivates my current research: investigate the tangible creation made radically simple by leveraging the opportunities that naturallanguage programming offers, especially for general audiences. Through an electronics toolkit with a natural language interface, users will be able to program interactive objects by describing their desired behavior effortlessly and learn from the generated code. The findings will contribute to understanding key HCI issues in lowering the barrier to tangibles for large audiences and let them create interactive experiences, free from technical constraints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".