DIAGNOSING COGNITIVE ERRORS: STATISTICAL PATTERN CLASSIFICATION BASED ON ITEM RESPONSE THEORY

Abstract

This paper introduces a probabilistic model that is capable of diagnosing and classifying cognitive errors in a general problem-solving domain. The model is different from the usual deterministic strategies common in the area of artificial intelligence because item response theory is utilized to handle the variability of response errors. As for illustrating the model, the dataset obtained from a 38-item fraction addition test is used, and the students' responses are classified into 34 groups of misconceptions. These groups are predetermined by the result of an error analysis previously done, and validated with the error diagnostic program written by a typical formal logic approach.