Yu Xinyue, Zhan Peida, Chen Qipeng
School of Psychology, Zhejiang Normal University, Jinhua, China, Jinhua, China.
Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Jinhua, China.
Front Psychol. 2023 Feb 9;14:1112463. doi: 10.3389/fpsyg.2023.1112463. eCollection 2023.
Previous longitudinal assessment experiences for multidimensional continuous latent constructs suggested that the set of anchor items should be proportionally representative of the total test forms in content and statistical characteristics and that they should be loaded on every domain in multidimensional tests. In such cases, the set of items containing the unit Q-matrix, which is the smallest unit representing the whole test, seems to be the natural choice for anchor items. Two simulation studies were conducted to verify the applicability of these existing insights to longitudinal learning diagnostic assessments (LDAs). The results mainly indicated that there is no effect on the classification accuracy regardless of the unit Q-matrix in the anchor items, and even not including the anchor items has no impact on the classification accuracy. The findings of this brief study may ease practitioners' worries regarding anchor-item settings in the practice application of longitudinal LDAs.
先前针对多维连续潜在结构的纵向评估经验表明，锚定项目集在内容和统计特征上应与总测试形式成比例地具有代表性，并且应加载到多维测试的每个领域中。在这种情况下，包含单元Q矩阵（即代表整个测试的最小单元）的项目集似乎是锚定项目的自然选择。进行了两项模拟研究，以验证这些现有见解对纵向学习诊断评估（LDA）的适用性。结果主要表明，无论锚定项目中的单元Q矩阵如何，对分类准确性均无影响，甚至不包括锚定项目对分类准确性也没有影响。这项简短研究的结果可能会减轻从业者在纵向LDA的实践应用中对锚定项目设置的担忧。
