An Endorsement-Based Approach to Student Modeling for Planner-Controlled Intelligent Tutoring Systems

Abstract

This report describes an approach to student modeling for intelligent tutoring systems based on an explicit representation of the tutor's beliefs about the student and the arguments for and against those beliefs (called endorsements). A lexicographic comparison of arguments, sorted according to evidence reliability, provides a principled means of determining those beliefs that are considered true, false, or uncertain. Each of these beliefs is ultimately justified by underlying assessment data. The endorsement-based approach to student modeling is particularly appropriate for tutors controlled by instructional planners. These tutors place greater demands on a student model than opportunistic tutors. Numeric calculi approaches are less well-suited because it is difficult to correctly assign numbers for evidence reliability and rule plausibility. It may also be difficult to interpret final results and provide suitable combining functions. When numeric measures of uncertainty are used, arbitrary numeric thresholds are often required for planning decisions. Such an approach is inappropriate when robust context-sensitive planning decisions must be made. Instead, the ability to examine beliefs and justifications is required. This report presents a truth maintenance system (TMS)-based implementation of the endorsement-based approach to student modeling, compares this approach to alternatives, and provides a project history describing the evolution of this approach.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1991
Accession Number
ADA242514

Entities

People

  • William R. Murray

Organizations

  • FMC Corporation

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Classification
  • Computational Science
  • Computers
  • Education
  • Human Resources
  • Hydraulic Valves
  • Information Systems
  • Instructions
  • Learning
  • Machine Learning
  • Maintenance
  • Reliability
  • Students
  • Training
  • Uncertainty

Readers

  • Artificial Intelligence