Educommunication, Geography and Virtual Games: A Proposal to Encourage Scientific Literacy in Middle School
Bibliographic record
Abstract
This research describes a development process of an educommunicational project and its presentation by 7th year’s students of Middle School. Student’s part of this research was from a private school in a city part of Sao Paulo’s State. In fact, it aimed to verify possible contributions obtained by using a specific didactic sequence linked with Geography teaching. Thereunto, virtual games and educommunication tools were used to promote literacy and scientific dissemination through Digital Information Technologies and Communication. In terms of method utilized, a qualitative-descriptive method was used since it applied observation, analysis, and data comparison to obtain results. In addition, the methodology helped to solve problems throughout the project phase until it reached its final presentation. As a final result of this project, it was observed that students could develop not only active learning but also creative learning throughout the process. As assistance, students had teacher acting as a mediator in order to constantly guide and manage them in their activities. To sum up, documentary videos presented by students, combined with games and digital educational technologies are considered relevant forms of communication as well as effective learning resources because they favor a development of critical-scientific thinking. As a matter of fact, it is applicable when carrying out researches, experiencing challenges also building knowledge throughout student’s own experience processes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".