ror.org/002rjbv21
Résumés
Résumé
Malgré une importante demande de recevoir des informations diagnostiques sur les difficultés en lecture des élèves, il existe très peu d’outils d’évaluation conçus spécifiquement pour cet usage. Plusieurs recherches en approche diagnostique cognitive (ADC) utilisent donc les résultats d’épreuves à grande échelle pour fournir de la rétroaction diagnostique fine et fiable sur les forces et les faiblesses des élèves. Les modélisations de données permettent de s’éloigner des scores ou des rangs percentiles habituellement obtenus, et de fournir des pistes d’intervention appropriées. Cette étude vise à vérifier la faisabilité d’appliquer des modélisations à visée diagnostique aux résultats de 4762 élèves canadiens ayant fait le cahier 13 du test du PIRLS de 2011. Les résultats suggèrent un potentiel de recevoir de la rétroaction diagnostique détaillée de leurs forces et faiblesses sur les habiletés sous-jacentes du test.
Mots-clés :
- approche diagnostique cognitive (ADC),
- lecture,
- DINA,
- G-DINA,
- modèles de classification diagnostique (MCD),
- épreuves à grande échelle
Abstract
Despite the grand demand to receive diagnostic information about students’ difficulties in reading, there are very few tests specifically designed for diagnostic purposes. Therefore, many researches in cognitive diagnostic approach (CDA) use large-scale test results to provide fine and reliable diagnostic feedback on the strengths and weaknesses of students other than the total scores or percentiles ranks, which allow appropriate intervention. This study shows an example of the application of diagnostic modeling using data from 4,762 Canadian students who completed booklet 13 of the PIRLS test in 2011. The results highlight the potential for detailed diagnostic feedback of students’ strengths and weaknesses on the underlying skills identified in the test.
Keywords:
- cognitive diagnostic approach (CDA),
- reading,
- DINA,
- G-DINA,
- diagnostic classification models (DCM),
- large-scale tests
Resumo
Apesar da importante procura por informações diagnósticas sobre as dificuldades de leitura dos alunos, existem muito poucas ferramentas de avaliação concebidas especificamente para este uso. Diversas investigações em abordagem de diagnóstica cognitiva (ADC) utilizam, portanto, os resultados de testes em larga escala para fornecer feedback diagnóstico detalhado e fiável sobre os pontos fortes e fracos dos alunos. As modelizações de dados torna possível afastar-se das pontuações ou dos níveis percentuais normalmente obtidos e fornecer pistas de intervenção apropriadas. Este estudo tem como objetivo verificar a viabilidade da aplicação da modelização diagnóstica aos resultados de 4.762 alunos canadianos que realizaram o caderno 13 do teste do PIRLS de 2011. Os resultados realçam o potencial para um feedback diagnóstico detalhado dos pontos fortes e fracos dos alunos em relação às habilidades subjacentes ao teste.
Palavras chaves:
- abordagem diagnóstica cognitiva (ADC),
- leitura,
- DINA,
- G-DINA,
- modelos de classificação diagnóstica (MCD),
- testes em larga escala
Parties annexes
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