Accession Number:

ADA158108

Title:

Diagnosing Cognitive Errors: Statistical Pattern Classification and Recognition Approach

Descriptive Note:

Research rept.

Corporate Author:

ILLINOIS UNIV AT URBANA COMPUTER-BASED EDUCATION RESEARCH LAB

Personal Author(s):

Report Date:

1985-01-01

Pagination or Media Count:

37.0

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 the item response theory is utilized for handling the variability of response errors. As for illustrating the model, the dataset obtained form 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. Keywords cognitive errors, item response theory, bugs, fractions, pattern classification, caution index.

Subject Categories:

  • Psychology
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE