Platform for in-person or remote teaching and learning of concepts and practices in the Internet of Things.
Bibliographic record
Abstract
A crescente complexidade do mercado tecnológico e a necessidade de profissionais aptos a resolver problemas reais motivam alterações nos processos de ensino e aprendizagem em Engenharia. Esta pesquisa busca contribuir para preencher a lacuna entre teoria e prática, propondo uma plataforma educacional que utiliza a placa Labrador para ensino e aprendizado de conceitos de Internet das Coisas (IoT), em modalidades presencial e remota. O método Design Based Research foi empregado para desenvolver a plataforma como um ambiente de laboratório para uso presencial ou remoto, onde alunos podem interagir com elementos e dispositivos físicos IoT, por meio de programação em Python e/ou blocos visuais. A plataforma inclui um banco inicial com sugestões de atividades, que progridem em complexidade e, ao mesmo tempo, incentivam a autonomia dos estudantes, permitindo que avancem da prática orientada pelo professor para a elaboração mais autônoma de soluções para problemas. A plataforma apresenta vantagens em termos de acessibilidade e custo. Testes preliminares com docentes e alunos de Engenharia, apontam potencial para facilitar e ampliar o acesso `a aprendizagem prática de IoT, tanto em ambientes presenciais quanto à distância.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.040 |
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".