Plataforma para uso presencial ou remoto voltada ao ensino e aprendizagem de conceitos e práticas em Internet das Coisas.
Bibliographic record
Abstract
The increasing complexity of the technological market and the need for professionals capable of solving real-world problems motivate changes in Engineering teaching and learning processes.This research aims to contribute in bridging the gap between theory and practice by proposing an educational platform that uses the Labrador board to teach and learn Internet of Things (IoT) concepts, both in-person and remote settings.The Design Based Research method was employed to develop a laboratory environment for in-person or remote use, where students can interact with physical IoT elements and devices, through Python programming and/or visual blocks.The platform includes an initial dataset with suggested activities that progress in complexity and, at the same time, encourage students autonomy, allowing them to advance from teacher-guided practice to more autonomous elaboration of solutions to problems.The platform has advantages in terms of accessibility and low-cost benefits.Preliminary tests with teachers, students and Engineering Professionals point to the potential to facilitate and expand access to practical IoT learning, both in in-person and remote environments.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".