Applying graph sampling methods on student model initialization in intelligent tutoring systems (CROSBI ID 231005)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Vištica, Marija ; Grubišić, Ani ; Žitko, Branko
engleski
Applying graph sampling methods on student model initialization in intelligent tutoring systems
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.
computer-based learning ; domain knowledge ; domain knowledge graph ; graph sampling ; intelligent tutoring systems ; knowledge management
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Podaci o izdanju
33 (4)
2016.
202-218
objavljeno
2056-4880
2056-4899
10.1108/IJILT-03-2016-0011
Povezanost rada
Računarstvo