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.