Using Written and Behavioral Data to Detect Evidence of Continuous Learning

Abstract

We describe a lifelong learner modeling project that focuses on the use of written and behavioral data to detect patterns of learning over time. Related work in essay analysis and machine learning is discussed. Although primarily focused on isolated learning experiences, we argue there is promise for scaling these techniques up to the lifelong learner modeling problem.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2009
Accession Number
ADA596001

Entities

People

  • Dave Gomboc
  • H. Clifford Lane
  • John Hart
  • Mark Core
  • Mike Birch
  • Milton Rosenberg

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Army
  • Artificial Intelligence Computing
  • Bayesian Networks
  • Computational Science
  • Computers
  • Detectors
  • Engineering
  • Instructions
  • Learning
  • Machine Learning
  • Models
  • Neural Networks
  • Simulations
  • Standards
  • Students
  • Training

Readers

  • Computational Modeling and Simulation
  • Instructional Design and Training Evaluation.
  • Oncology

Technology Areas

  • AI & ML