Mastering Of Hypermedia Resources By Virtual Learning Communities: Possibilities And Constraints For Interaction, Communication And Construction Of Network Knowledge.
Bibliographic record
Abstract
The purpose of this paper is to present the procedures and some of the results from the investigation on the use of hypermedia resources for the construction of network knowledge. These resources, available in a distance learning support environment, are used for the process of interaction and production of knowledge through a virtual learning community (VLC) under formation. This VLC aims at providing a collaborative environment equipped with multimedia resources focused on information, communication and continuing education for community health agents[1], as well as nurses, physicians, teachers and other people who work in public establishments which offer access to Information and Communication Technologies in the city of Pedreira in the countryside of São Paulo. In this context, collaborative learning situations are shaped where users/learners master procedures, strategies and multimedia resources available and make “products” using audiovisual aids. The “products” are presented, discussed and revised by the group to be later distributed to and shared with the relevant community. Based on such activities, it was possible to understand that the use of audiovisual aids in virtual learning environments (i.e., tools which enable different forms of expression and the establishment of the distance communication among people on the Web through the joint use of visual and sound components) are able to boost the learning process by overcoming the training constraints typically found in traditional models and thus expanding the range of possibilities for the construction of knowledge.[1] In Brazil, a community health agent is a lay health care worker who is not certified to practice medicine or nursing, but has the primary task of gathering information on the health status of a small community by means of a close relationship with it.[pt]Este artigo tem como objetivo apresentar e discutir ideias, métodos, procedimentos e resultados relacionados à investigação sobre a utilização de recursos hipermidiáticos para a construção de conhecimentos em rede. Estes recursos, disponíveis em um ambiente de suporte de educação a distância são utilizados para o processo de interação e produção de conhecimentos por uma comunidade virtual de aprendizagem (CVA) em formação. Esta CVA pretende ser um espaço colaborativo, com recursos multimídia, voltada para a informação, comunicação e formação continuada de agentes comunitários de saúde, enfermeiros, médicos, professores e demais pessoas vinculadas aos espaços públicos de acesso as tecnologias de informação e comunicação do município de Pedreira-SP.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.008 |
| 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".