Towards Optimization of Macrocognitive Processes: Automating Analysis of the Emergence of Leadership in Ad Hoc Teams

Abstract

An important focus for technical research related to machine learning has been to address not only generalization across sub-community structures within a hierarchical dataset, but also accommodating changes over time in a longitudinal dataset using evolving behavior models. We have two prototype models built and working and are extending that work to make it more scalable to larger datasets. We completed a highly scalable model, being able to be applied to networks with millions of users. We validated the model on 3 different data sets from Massive Open Online Courses and found that the sub-community structure identified by the algorithm was predictive of differences in dropout rate between subsets of students.

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Document Details

Document Type
Technical Report
Publication Date
Apr 21, 2014
Accession Number
ADA606908

Entities

People

  • Andrew Duchon
  • Carolyn P. Rose
  • Emily Patterson
  • Gerry Stahl
  • John Carroll
  • Marcela Borge
  • Sean Goggins

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Cognitive Systems Engineering
  • Computational Linguistics
  • Computational Science
  • Data Analysis
  • Data Sets
  • Databases
  • Distance Learning
  • Education
  • Generative Models
  • Information Science
  • Information Systems
  • Machine Learning
  • Natural Language Processing
  • Network Science
  • Psychology
  • Social Media
  • Students

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks