Identifying Cognitive Attributes in Highly Correlated Test Scenarios: A Machine Learning Perspective

Assessment based on fine-grained latent traits can provide more detailed information about the subjects. Cognitive diagnostic assessment (CDA) is a framework of education and psychological measurement that is grounded in the assessment of fine-grained latent traits. The Q -matrix, defining the relationship between items and attributes, is the basis for CDA. Data-driven Q -matrix estimation has become a research hotspot due to its high efficiency and objectivity. However, the existing method for Q -matrix estimation primarily focuses on scenarios with low or middle correlations between attributes (latent traits), and they point out that the accuracy of Q -matrix estimation significantly declines in situations with high attribute correlations. To address the limitations of existing methods in scenarios with high attribute correlation. This paper proposes a majority-class symmetric undersampling (MCSU) method tailored for CDA. To evaluate its performance, two simulation studies are conducted. The simulation results under a wide variety of conditions show that the MCSU can improve the estimation accuracy of attribute number and Q -matrix in highly correlated scenarios. Finally, a real dataset of the Examination for the Certificate of Proficiency in English is analyzed using the proposed method.