Supervised diagnostic classification of cognitive attributes using data augmentation.

ABSTRACT: Over recent decades, machine learning, an integral subfield of artificial intelligence, has revolutionized diverse sectors, enabling data-driven decisions with minimal human intervention. In particular, the field of educational assessment emerges as a promising area for machine learning applications, where students can be classified and diagnosed using their performance data. The objectives of Diagnostic Classification Models (DCMs), which provide a suite of methods for diagnosing students' cognitive states in relation to the mastery of necessary cognitive attributes for solving problems in a test, can be effectively addressed through machine learning techniques. However, the challenge lies in the latent nature of cognitive status, which makes it difficult to obtain labels for the training

SUBMITTER: Yoon JY 

PROVIDER: S-EPMC10769086 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature