Sparse Factor Analysis for Information Extraction and Fusion

Abstract

Statistical models have been developed to incorporate preferences of each instructor in recommending personalized learning action. Such models can provide feedback to instructors to help them understand their preferences. In addition, novel methods have been proposed to use neural networks to design data-driven question generation models. A new novel criterion is also proposed to evaluate the performance and relevance of such models. Furthermore, we have proposed a method for automatic short answer grading. Finally, stronger generative models have been developed using DRM framework. Over the past year, we have made significant progress on education data processing in five directions: 1) Statistical models for instructor content preference analysis, 2) Data-driven question generation models, 3) Criteria for Neural Question Generation Models, 4) Meta-learning model for automatic short answer grading, 5) Generative models using deep learning

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Document Details

Document Type
Technical Report
Publication Date
May 21, 2019
Accession Number
AD1088887

Entities

People

  • Richard G. Baraniuk

Organizations

  • Rice University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Automatic
  • Data Processing
  • Deep Learning
  • Demographic Cohorts
  • Department Of Defense
  • Education
  • Factor Analysis
  • Generative Models
  • Information Science
  • Instructors
  • Learning
  • Machine Learning
  • Models
  • Neural Networks
  • Scientific Research

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Instructional Design and Training Evaluation.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks