The selection of cognitive diagnostic models for a reading comprehension test
The selection of cognitive diagnostic models for a reading comprehension test
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DOI：
10.1177/0265532215590848
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发表时间：
2016-07-01
期刊：
影响因子：
4.1
通讯作者：
Lei, Pui-Wa
中科院分区：
文献类型：
--
作者：
Li, Hongli;Hunter, C. Vincent;Lei, Pui-Wa
认知诊断模型在提供诊断信息以辅助学习和教学方面具有很大的潜力，已有大量的认知诊断模型被提出。然而，不同的跨文化交际模式的假设和表现以及它们在阅读理解测试中的应用还没有被完全理解。在本研究中，我们比较了饱和模式(G-DINA)、两种补偿模式(DINO，ACDM)和两种非补偿模式(DINA，RRUM)与密歇根英语语言评估组合(MELAB)阅读测试的成绩。与饱和G-DINA模型相比，ACDM模型具有相似的模型拟合度和相似的技能分类结果。在模型拟合和分类结果方面，RRUM略逊于ACDM和G-DINA，而限制性更强的DINA和DINO则比其他三个模型差得多。这项研究的结果强调了CDM在阅读测试中应用的模式选择的过程和考虑因素。
Cognitive diagnostic models (CDMs) have great promise for providing diagnostic information to aid learning and instruction, and a large number of CDMs have been proposed. However, the assumptions and performances of different CDMs and their applications in regard to reading comprehension tests are not fully understood. In the present study, we compared the performance of a saturated model (G-DINA), two compensatory models (DINO, ACDM), and two non-compensatory models (DINA, RRUM) with the Michigan English Language Assessment Battery (MELAB) reading test. Compared to the saturated G-DINA model, the ACDM showed comparable model fit and similar skill classification results. The RRUM was slightly worse than the ACDM and G-DINA in terms of model fit and classification results, whereas the more restrictive DINA and DINO performed much worse than the other three models. The findings of this study highlighted the process and considerations pertinent to model selection in applications of CDMs with reading tests.