E-textile Learning Scaffolds: Supports for Novices Learning E-textile Concepts and Techniques
Bibliographic record
Abstract
Electronic textiles (e-textiles) combine the conductive properties of metallic threads with increasingly small computers and microcontrollers to create textiles that are interactive. E-textiles enable new opportunities such as devices that are strong yet flexible, and the ability to use more accessible crafting tools and materials. They also allow technology to blend into the textiles we have in our lives, such as those on our bodies and in our homes. In education, e-textiles are incorporated into curriculums for how they can increase participation in physical computing, while also enabling new creative and expressive applications. Yet blending the fields of physical computing and textiles is not simple, since each has its own culture of terminology, design and prototyping practices, tools, techniques, and methods of documentation. The goal of this thesis is to support beginners in learning the hybrid craft of e-textiles with e-textile learning scaffolds. Rather than building e-textiles from scratch, e-textile learning scaffolds are activities for supporting novices as they learn e-textile concepts and practice the tacit aspects of the craft. One central research question runs throughout the projects in this thesis: "How can we scaffold e-textile ideation and experiential learning with tangible objects and activities?" This thesis contributes five studies, each providing a tangible scaffold or activity, or scaffolding recommendations, for teaching e-textiles in courses or workshops. The design of these learning scaffolds focused on how they could be accessible to educators by prioritizing reproducibility, re-use, and low-cost.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".