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

机译：图采样方法在智能补习系统中学生模型初始化中的应用

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摘要

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.
机译：目的-为了在智能补习系统中初始化学生模型，应向学生提供某种形式的初始知识测试。由于作者不能在该初始测试中包括所有领域知识，因此应选择领域知识子集。本文旨在讨论这个问题。设计/方法/方法-为了生成代表特定领域知识的知识样本，作者可以使用采样算法。在本文中，作者提出了五种采样算法（随机游走，Metropolis-Hastings随机游走，森林火灾，雪球和表示算法），并研究了进行采样过程后保留领域知识样本的哪些结构特性。结果-比较了作者使用这些算法得到的样本，并比较了它们在采样图中的累积节点度分布，聚类系数和最短路径的长度，以便找到最佳样本。原创性/价值-这种方法是原创的，因为作者找不到使用图抽样方法进行学生建模的任何类似作品。

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