WinterLab: Developing a Low-Cost, Portable Experiment Platform to Encourage Engagement in the Clectronics Lab
Bibliographic record
Abstract
Encouraging student engagement is a key aim in any educational setting, and allowing students the freedom to pursue their own methods of solving problems through independent experimentation has been shown to markedly improve this. In many contexts, however, allowing students this flexibility in their learning is hampered by constraints of the material itself, such as in the electronics laboratory, where expensive and bulky equipment confines the learning environment to the laboratory room. Finding ourselves in the position of teaching one such laboratory course at the undergraduate level, we sought to encourage students to learn through independent investigation and the pursuit of personal projects, by providing a more flexible and inquiry-based learning environment and allowing them to take their measurement equipment—and their learning—beyond the laboratory itself. We present this project as a case of design both for and by students, with the lead designer undertaking the project after attending the course in question, and pursuing its development as a foundational step in their graduate career. We discuss the challenges and opportunities we encountered over the course of the design and development process, and the eventual key output of the project: a portable, low-cost, integrated electronics experimentation platform called the WinterLab board.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".