Applying graph sampling methods on student model initialization in intelligent tutoring systems. Issue 4 (1st August 2016)

• Record Type: Journal Article
• Authors: Vištica, Marija; Grubišic, Ani; Žitko, Branko
• Is Part Of: International journal of information and learning technology. Volume 33:Issue 4(2016)
• Page Start: 202
• Page End: 218
• Publication Date: 2016-08-01
• Subjects: Computer-based learning -- Knowledge management -- Domain knowledge -- Domain knowledge graph -- Graph sampling -- Intelligent tutoring systems; Education, Higher -- Data processing -- Periodicals; Education, Higher -- Computer networks -- Periodicals; Education, Higher -- Information technology -- Periodicals; 378.00285
• DOI: 10.1108/IJILT-03-2016-0011
• Languages: English
• ISSNs: 2056-4880

Abstract

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.