Explorando uma Aplicação m-learning para Ensino de Vetores na Física do Ensino Médio
Bibliographic record
Abstract
The knowledge and understanding of vectors and vector quantities is extremely important to learn the motion of objects in physics at high school.Considering that vectors and vector quantities are a physics topic in which many students have difficulties, it is important to explore new ways to present them.In this vein, this study aims to explore a new m-learning application named Lab-Vetor.The application is designed for teaching and learning vectors on mobile devices.LabVetor can either be used by teachers in the classroom, through interactive whiteboards, and by the students at home as an m-learning training application.The LabVetor was evaluated by a group of students and showed to be a useful tool to assist the comprehension and reflection of the content learned in classroom.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".