Tracking performance and forming study groups for prep courses using probabilistic graphical models

Yoram Bachrach, Yoad Lewenberg, Jeffrey S. Rosenschein, Yair Zick

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Efficient tracking of class performance across topics is an important aspect of classroom teaching; this is especially true for psychometric general intelligence exams, which test a varied range of abilities. We develop a framework that uncovers a hidden thematic structure underlying student responses to a large pool of questions, using a probabilistic graphical model.

Original languageEnglish
Title of host publicationAAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems
Pages1359-1360
Number of pages2
ISBN (Electronic)9781450342391
StatePublished - 2016
Event15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016 - Singapore, Singapore
Duration: 9 May 201613 May 2016

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

Conference

Conference15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016
Country/TerritorySingapore
CitySingapore
Period9/05/1613/05/16

Keywords

  • Bayesian PCA
  • Education
  • Probabilistic graphical models

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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