Large Scale Deployment of Tablet Computers in High Schools in Brazil
Bibliographic record
Abstract
Abstract Recently different sectors of society of many countries have been demanding significant improvements in their education systems, and teaching and learning practices (Keller, 2008; Latchem & Hanna, 2001). The need for keeping up or developing competitiveness has been the main reason for these improvements. These countries has been faced with challenges in terms of lack of skilled workers, capacity of resilience from the labor market to deal with dramatic economic changes, and the pursuit for more productivity based on the use of technology. Brazil is a good example of one of these countries. It has been struggling to improve its public basic education in order to develop the workforce. One of the initiatives to improve the education system is by changing the education paradigm in high schools with the use of tablet computers in a large scale deployment. This paper describes the social scenario that led to this initiative and how it has been made in a large country, as well as the research that is being carried out to investigate the impact of such initiative in the learning outcomes in public high schools in Brazil.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".