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

Abstract

Purpose

In order to initialize a student model in intelligent tutoring systems, some form of initial knowledge test should be given to a student. Since the authors cannot include all domain knowledge in that initial test, a domain knowledge subset should be selected. The paper aims to discuss this issue.

Design/methodology/approach

In order to generate a knowledge sample that represents truly a certain domain knowledge, the authors can use sampling algorithms. In this paper, the authors 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.

Findings

The samples that the authors got using these algorithms are compared and the authors have compared their cumulative node degree distributions, clustering coefficients and the length of the shortest paths in a sampled graph in order to find the best one.

Originality/value

This approach is original as the authors could not find any similar work that uses graph sampling methods for student modeling.

Citation

Vištica, M., Grubišic, A. and Žitko, B. (2016), "Applying graph sampling methods on student model initialization in intelligent tutoring systems", International Journal of Information and Learning Technology, Vol. 33 No. 4, pp. 202-218. https://doi.org/10.1108/IJILT-03-2016-0011