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The Effect of Model Granularity on Student Performance Prediction Using Bayesian Networks

  • Zachary A. Pardos
  • Neil T. Heffernan
  • Brigham Anderson
  • Cristina L. Heffernan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)

Abstract

A standing question in the field of Intelligent Tutoring Systems and User Modeling in general is what is the appropriate level of model granularity (how many skills to model) and how is that granularity derived? In this paper we will explore models with varying levels of skill generality (1, 5, 39 and 106 skill models) and measure the accuracy of these models by predicting student performance within our tutoring system called ASSISTment as well as their performance on a state standardized test. We employ the use of Bayes nets to model user knowledge and to use for prediction of student responses. Our results show that the finer the granularity of the skill model, the better we can predict student performance for our online data. However, for the standardized test data we received, it was the 39 skill model that performed the best. We view this as support for fine-grained skill models despite the finest grain model not predicting the state test scores the best.

Keywords

Bayesian Network Skill Model Intelligent Tutor System Slip Parameter Model Granularity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zachary A. Pardos
    • 1
  • Neil T. Heffernan
    • 1
  • Brigham Anderson
    • 1
  • Cristina L. Heffernan
    • 1
  1. 1.Worcester Polytechnic Institute, Carnegie Mellon University, Worcester Public Schools 

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