Um Modelo para Recuperação de Objetos de Aprendizagem no Padrão IEEE LOM Utilizando o Protocolo OAI-PMH e Repositórios de Objetos de Aprendizagem Públicos
Bibliographic record
Abstract
Trabalhos recentes mostram que estudantes tendem a ter melhores resultados no aprendizado quando o conteúdo lhe é apresentado de forma personalizada. Dessa forma, a adaptação automática de conteúdo em sistemas de ensino é uma área que vem sendo alvo de diversos estudos. Porém, é necessário ter uma quantidade suficiente de conteúdo a ser personalizado para que esta seja eficiente. Este trabalho apresenta uma análise de uma abordagem de recuperação de conteúdo da Web, que se baseia na utilização do protocolo OAI-PMH e no padrão IEEE LOM de metadados dos Objetos de Aprendizagem. Os resultados obtidos, apesar de preliminares, expandem as possibilidades de trabalhos futuros e apontam novas necessidades.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".