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A Multi-Agent School Simulation Based on Hierarchical Social Networks

机译：基于分层社交网络的多Agent学校模拟

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The quality of K-12 education has been a very big concern for years. Previous methods studied only one or two factors, such as school choice, or teacher quality, on school performance. Therefore the results they provide can be limited. We propose a multi-agent approach to integrate multiple actors in a school system. These actors include teachers, students, supporting staffs and administrators. The interactions among these actors compose a hierarchical school social network. We first detect the hierarchical community structure in this school network by using an agglomerative hierarchical algorithm. Existing agglomerative hierarchical algorithms usually calculate similarity or dissimilarity between two clusters by using some measure of distance between pairs of observations. We, however, develop a method that calculates similarity based on social interactions between interactions is essential in multi-agent systems. Our algorithm is applied to 15 school districts in Bexar County, Texas, and it provides satisfying results on generating the hierarchical structure of all school districts. We then use the detected structure of the social network to evaluate the school system’s organization performance. We design and implement a funding evaluation model to decompose the funding policy task into subtasks and then evaluate these subtasks by using funding distribution policies from past years and looking for possible relationships between student performances and funding policies. Experiments in the 15 school districts in Bexar County show no significant correlation between student performance and the amount of the funding a school district received.
机译：多年来，K-12教育的质量一直是一个非常重要的问题。先前的方法仅研究一个或两个因素对学校成绩的影响，例如学校选择或教师素质。因此，它们提供的结果可能会受到限制。我们提出了一种多主体方法，将多个参与者整合到学校系统中。这些参与者包括教师，学生，辅助人员和管理人员。这些参与者之间的互动构成了一个分层的学校社交网络。我们首先通过使用聚集分层算法来检测该学校网络中的分层社区结构。现有的聚集层次算法通常通过使用观测值对之间的某种距离度量来计算两个聚类之间的相似性或相异性。但是，我们开发了一种基于交互之间的社交交互来计算相似性的方法，这在多主体系统中至关重要。我们的算法已应用于德克萨斯州Bexar县的15个学区，在生成所有学区的层次结构方面提供了令人满意的结果。然后，我们使用检测到的社交网络结构来评估学校系统的组织绩效。我们设计并实施了一项资金评估模型，以将资金政策任务分解为子任务，然后通过使用过去几年的资金分配政策并寻找学生表现与资金政策之间的可能关系来评估这些子任务。在Bexar县的15个学区进行的实验表明，学生的表现与学区获得的资助金额之间没有显着相关性。

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