Análise métrica de questões componentes de testes de rendimento: mecanismo de feedback para aprimorar sua elaboração
Bibliographic record
Abstract
Resumo O texto aborda a releví¢ncia da atividade de análise métrica das questões ou dos itens componentes de testes de rendimento, enquanto procedimento essencial ao feedback que se deve proporcionar aos elaboradores das mesmas. Nesse í¢mbito, o objetivo do ensaio foi comparar os efeitos das características métricas dos itens sobre a estimativa do nível de habilidade do aluno (θ), utilizando-se, para tal, o modelo de três parí¢metros logísticos da Teoria da Resposta ao Item (TRI). Este trabalho mostra uma das inúmeras possibilidades resultantes do uso da TRI: análises estatísticas pertinentes para aferir se o instrumento e os itens ou questões que o compõem cumpriram suas funções pedagógicas, isto é, se estimaram de modo válido e confiável o aprendizado dos discentes. Palavras chave: avaliação educacional; avaliação da aprendizagem; Teoria da Resposta ao Item (TRI); testes de rendimento. Metric analysis of assessment test components: feedback mechanisms to improve design Abstract This paper overviews the relevance of item metric analysis of assessment tests as an essential feedback procedure which should be provided to those who design them. The essay intends to compare the effects of the item metric characteristics over the estimated skill level of students (θ), using the three logistic parameters of the Item Response Theory (IRT). This work shows one of the many possibilities resulting from the use of the IRT: relevant statistical analysis to verify whether or not the tool and its items meet the expected pedagogical functions, i.e.: if they can validly and reliably estimate the learning of students. Key words: Educational Assessment; Learning Assessment; Item Response Theory (IRT); Performance Tests.
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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 | 0.017 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".