Learning with Stitch Samplers: Exploring Stitch Samplers as Contextual Instructions for E-textile Tutorials
Bibliographic record
Abstract
The field of textile fabrication has a strong pattern-making culture that enables individuals to reproduce items at home. Electronic textile (e-textile) researchers within HCI are increasingly exploring how computing can leverage these textile pattern-making practices, accessible fabrication tools, and do-it-yourself (DIY) maker cultures to enable individuals to make technologies for themselves with soft form factors that further blend computing into our everyday environments. In this paper we focus on the pattern-sharing artifact of stitch samplers, which are used for sharing, teaching, and learning stitching techniques, and explore how the design decisions around them should be adapted for practicing e-textile exercises. To do so, we conducted three studies: (1) preliminary interviews with five modern stitch sampler designers to understand what stitch samplers are used for, (2) a think-aloud user study of our initial e-textile sampler with ten beginners, and (3) interviews with five e-textile educators to reflect on applications and to better understand the opportunities and limitations of using samplers for distance learning. This paper contributes a better understanding of how HCI researchers can incorporate craft pattern practices for learning hybrid craft techniques.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".