Diagnosing Cognitive Errors: Statistical Pattern Classification and Recognition Approach

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1985
Accession Number
ADA158108

Entities

People

  • Kikumi K. Tatsuoka

Organizations

  • University of Illinois Urbana–Champaign

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence
  • California
  • Computer Programs
  • Educational Psychology
  • Human Resources
  • Illinois
  • Military Research
  • New England
  • New York
  • Operations Research
  • Personnel Management
  • Probabilistic Models
  • Psychology
  • Security
  • Students
  • Teaching Methods
  • United States

Fields of Study

  • Education

Readers

  • Artificial Intelligence
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference