Percepção e Desafios na Avaliação de Jogos Digitais Educacionais
Bibliographic record
Abstract
Os jogos digitais estão se tornando mais presentes na vida das pessoas e isso acaba se refletindo nas diversas áreas, tais quais a cultura, saúde, computação, educação, entre outras. Entretanto, quando se pensa na aplicação desses jogos no contexto educacional há ainda desafios a serem enfrentados, a exemplo de: como o professor consegue identificar se um jogo pode, ou não, ser utilizado para fins educacionais? Diante desse cenário, este trabalho apresenta os desafios enfrentados pelo professor para selecionar jogos digitais educacionais que atendam ao seu propósito e possam ser utilizados em sala de aula. Como abordagem para esse desafio, apresentamos dois referenciais para apoiar o professor na avaliação e seleção dos jogos digitais educacionais.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".