Distance Education and the Open University of Brazil
Bibliographic record
Abstract
Correspondence courses have been offered in Brazil since the late 19th century; in the 20th century, instructional media such as radio and television were successfully used long before the introduction of the Internet. However, distance education (DE) was officially established in Brazil only in 1996 by the National Educational Law of Policies and Bases. Several censuses conducted by the Brazilian Ministry of Education and the Brazilian Association of Distance Education (ABED) collected statistics on the number of institutions and students involved in DE in Brazil. Although higher education DE has developed in the country since then, several attempts to create an Open University failed. The institution that is now The Open University of Brazil (UAB), created in 2005, focused mainly on teacher education. However, it is not a new institution (but rather a system of older institutions). It is neither a university (but rather a consortium of public federal, state, and municipal face-to-face educational institutions), nor open (candidates should have at least finished high school and are required to pass a rigorous entrance exam). Although UAB certainly contributed to the progress of DE in Brazil, it faces many challenges and problems, such as the continuously questioned quality of its learning support centers, labor relations, issues related to hiring face-to-face and online tutors, and the structure and organization of producing content for courses. This article presents a brief history and the main characteristics of DE in Brazil, details UAB’s structure, and discusses the challenges it faces.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".