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Applying graph sampling methods on student model initialization in intelligent tutoring systems

Applying graph sampling methods on student model initialization in intelligent tutoring systems PurposeIn order to initialize a student model in intelligent tutoring systems, some form of initial knowledge test should be given to a student. Since we cannot include all domain knowledge in that initial test, a domain knowledge subset should be selected.Design/methodology/approachIn order to generate a knowledge sample that represents truly a certain domain knowledge, we can use sampling algorithms. In this paper, we present five sampling algorithms (Random Walk, Metropolis-Hastings Random Walk, Forest Fire, Snowball and Represent algorithm) and investigate which structural properties of the domain knowledge sample are preserved after sampling process is conducted.FindingsThe samples that we got using these algorithms are compared and we have compared their cumulative node degree distributions, clustering coefficients and the length of the shortest paths in a sampled graph in order to conclude about the best one. Originality/valueThis approach is original as we could not find any similar work that uses graph sampling methods for student modeling. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Information and Learning Technology Emerald Publishing

Applying graph sampling methods on student model initialization in intelligent tutoring systems

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2056-4880
DOI
10.1108/IJILT-03-2016-0011
Publisher site
See Article on Publisher Site

Abstract

PurposeIn order to initialize a student model in intelligent tutoring systems, some form of initial knowledge test should be given to a student. Since we cannot include all domain knowledge in that initial test, a domain knowledge subset should be selected.Design/methodology/approachIn order to generate a knowledge sample that represents truly a certain domain knowledge, we can use sampling algorithms. In this paper, we present five sampling algorithms (Random Walk, Metropolis-Hastings Random Walk, Forest Fire, Snowball and Represent algorithm) and investigate which structural properties of the domain knowledge sample are preserved after sampling process is conducted.FindingsThe samples that we got using these algorithms are compared and we have compared their cumulative node degree distributions, clustering coefficients and the length of the shortest paths in a sampled graph in order to conclude about the best one. Originality/valueThis approach is original as we could not find any similar work that uses graph sampling methods for student modeling.

Journal

The International Journal of Information and Learning TechnologyEmerald Publishing

Published: Aug 1, 2016

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