Mineração de dados educacionais e Mundos Virtuais: um estudo exploratório no OpenSim
Bibliographic record
Abstract
O uso dos mundos virtuais vem se expandindo no cenário educacional, assim como a gama de possibilidades de pesquisas relacionadas a estes. Este artigo buscou explorar a viabilidade da aplicação de mineração de dados educacionais (MDE) nos mundos virtuais, para identificar possíveis padrões dos usuários e permitir alterações no planejamento pedagógico. Um estudo de caso foi realizado com um laboratório virtual de química no OpenSim, em que foram simuladas interações com dados sintéticos e analisados por meio da tarefa de regras de associação com o uso do algoritmo Apriori. Os resultados demonstraram a viabilidade da proposta, sendo possível o uso de MDE dentro dos mundos virtuais para identificar padrões de comportamento dos usuários.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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".