Table_4_Research on the Cognitive Diagnosis of Chinese Listening Comprehension Ability Based on the G-DINA Model.DOCX

As a new generation of measurement theory, cognitive diagnosis theory shows significant potential and advantages in educational evaluation in that it combines a cognitive process and a measurement method. The application of the theory not only reveals the potential characteristics of learners in cognitive processing, but also provides targeted remedies and strategic guidance for individuals. Given the difficulties of traditional assessment models in providing an insightful and fine-grained account for individualized and procedural learning, providing personalized learning strategies for learners of Chinese as a second language has been a new goal of teaching and measurement in Chinese listening. This study constructs a cognitive diagnosis model of Chinese listening comprehension for Chinese-as-a-second-language learners through theoretical exploration, model hypotheses, repeated verification, and model modification. The results show that the Q-matrix (Q3) constructed by the experts within modification has the highest fitting degree with the empirical data. The parameter recovery rate, the accuracy of the tested attribute or mode, and the relative fitting index obtained from the simulation study are consistent with the information extracted from the empirical data. The diagnostic reliability and effectiveness of generalized deterministic inputs, noise “and” gate (G-DINA) are higher than those of DINA, deterministic inputs, noisy “or” gate (DINO), and reduced reparametrized unified model (RRUM). In the estimation of the item and subject parameters, the G-DINA model shows good convergence, and the average classification accuracy rate based on attribute level is 0.861.

作为测量理论的新一代，认知诊断理论在教育教学评估中展现出巨大的潜力和优势，其特色在于将认知过程与测量方法相结合。该理论的应用不仅揭示了学习者在认知处理过程中的潜在特征，而且还为个体提供了针对性的治疗策略和战略指导。鉴于传统评估模型在为个性化及程序性学习提供深入且细致的描述方面的困难，为作为第二语言学习的汉语学习者提供个性化的学习策略，已成为汉语听力的教学与测量领域的新目标。本研究通过理论探索、模型假设、重复验证和模型修改，构建了针对汉语作为第二语言学习者的听力理解认知诊断模型。结果表明，在修改过程中由专家构建的Q矩阵（Q3）与经验数据具有最高的拟合度。模拟研究中获得的参数恢复率、测试属性或模式的准确性以及相对拟合指数与从经验数据中提取的信息相一致。广义确定性输入、噪声“与”门（G-DINA）的诊断可靠性和有效性高于DINA、确定性输入、噪声“或”门（DINO）和缩减重参数化统一模型（RRUM）。在项目参数和个体参数的估计中，G-DINA模型表现出良好的收敛性，基于属性水平的平均分类准确率为0.861。