An Approach to On-Line Assessment and Diagnosis of Student Troubleshooting Knowledge

Abstract

Intelligent tutors have the potential to enhance training in avionics troubleshooting by giving students more experience with specific problems. Part of the success of intelligent tutors will be associated with their ability to assess and diagnose the student's knowledge in order to direct pedagogical interventions. The goal of the research program described here is to develop a methodology for assessment and diagnosis of student knowledge of fault diagnosis in complex systems. Along with this broad goal, the methodology should: (1) target system knowledge, (2) provide rich representations of this knowledge useful for diagnosis, (3) be appropriate for real-world complex domains like avionics troubleshooting, and (4) enable assessment and diagnosis to be carried out on-line. In order to meet these requirements a general plan for mapping student actions onto system knowledge is proposed and research from one part of this plan is presented. Results from a Pathfinder analysis on action sequences indicate that action patterns can be meaningfully distinguished for high and low performers and that the patterns reveal specific targets for intervention. Short- and long-term contributions of this work are also discussed.... Artificial intelligence, Intelligent tutoring, Diagnostic assessment, Student modelling.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1993
Accession Number
ADA262289

Entities

People

  • Anna L. Rowe
  • Nancy J Cooke

Organizations

  • Rice University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Facilities
  • Artificial Intelligence
  • Avionics
  • Complex Systems
  • Computer Programming
  • Computers
  • Electronic Equipment
  • Human Resources
  • Intervention
  • Psychology
  • Sequences
  • Students
  • Task Performance And Analysis
  • Training
  • Troubleshooting

Readers

  • Instructional Design and Training Evaluation.
  • Neural Network Machine Learning.
  • STEM Education

Technology Areas

  • AI & ML