BlinkBoard: Guiding and Monitoring Circuit Assembly for Synchronous and Remote Physical Computing Education
Bibliographic record
Abstract
Motivated by the necessity of guiding and monitoring students when assembling electronic circuits during in-class activities, we propose BlinkBoard , an augmented breadboard that enhances synchronous and remote physical computing classes. BlinkBoard uses LEDs placed on each row of the breadboard to guide, via four distinct blinking patterns, how to place and connect components and wires. It also uses a set of Input/Output pins to sense voltage levels or to generate voltage output at user-specified rows. Our hardware uses an open protocol of JSON commands and responses that can be used directly via a command-line interface to control the hardware. Alternatively, these commands can be issued within a front-end graphical application hosted on a computer for a more user-friendly interface, and to ensure bidirectional and real-time communication between the instructor’s guiding and monitoring panel, and all the students’ remote breadboards. The BlinkBoard hardware is affordable and simple, partially due to a customized circuit configured via a hardware description language that handles the LEDs’ patterns with minimal load on the Arduino microcontroller. Finally, we briefly show BlinkBoard in use during a workshop with high-school students and an undergraduate design course.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".