A Multi-Agent School Simulation Based on Hierarchical Social Networks

DOI: 10.4236/iim.2014.64020   PDF   HTML     2,911 Downloads   3,737 Views   Citations


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.

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Zhang, Y. , Wu, J. and Ma, L. (2014) A Multi-Agent School Simulation Based on Hierarchical Social Networks. Intelligent Information Management, 6, 196-210. doi: 10.4236/iim.2014.64020.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Andrighetto, G., Boella, G., Sichman, G. and Verhagen, H. (2009) Social Networks and Multi-Agent Systems Symposium (SNAMAS-09) Introduction. Proceedings of the SNAMAS Symposium, Edinburgh, 6-9 April 2009, 1-3.
[2] Newman, M.E. (2003) The Structure and Function of Complex Networks. SIAM Review, 45, 167-256.
[3] Guessoum, Z. (2003) Dynamic and Adaptive Replication for Large-Scale Reliable Multi-Agent Systems. In: Garcia, A., Lucena, C., Zambonelli, F., Omicini, A. and Castro, J., Eds., Software Engineering for Large-Scale Multi-Agent Systems, Springer, Berlin, 182-198.
[4] Grant, T. (2009) Modeling Network-Enabled C2 Using Multiple Agents and Social Networks. Social Networks and Multi-Agent Systems Symposium (SNAMAS), 6-9 April 2009, Edinburgh, 30-35.
[5] Ma, J., Guo, D.W., Wang, K.P., Liu, M. and Chen, S. (2009) Colony Evolution in Social Networks Based on Multi-Agent Systems. IEEE International Conference on Natural Computation, 4, 594-597.
[6] Bettinger, E. (2005) The Effect of Charter Schools on Charter Students and Public Schools. Economics of Education Review, 24, 133-147.
[7] Lubienski, C. and Lubienski, S.T. (2006) Charter, Private, Public Schools and Academic Achievement: New Evidence from NAEP Mathematics Data. Occasional Paper, A12.
[8] Slate, J.R. and Jones, C.H. (2005) Effects of School Size: A Review of the Literature with Recommendations. Essays in Education, 13, 1-22.
[9] Schaeffer, S.E. (2007) Graph Clustering. Computer Science Review, 1, 27-64.
[10] Harris, D.N. and Sass, T.R. (2011) Teacher Training, Teacher Quality and Student Achievement. Journal of Public Economics, 95, 798-812.
[11] Clark, D., Martorell, P. and Rockoff, J. (2010) School Principals and School Performance. National Center for Analysis of Longitudinal Data in Education Research, Technical Report, Columbia University, New York.
[12] Meier, K.J., O’Toole Jr., L.J. and Goerdel, H.T. (2003) School Superintendents and School Performance: Quality Matters. Texas Educational Excellence Project, Technical Report.
[13] Anderson, N. (2011) Per Pupil Spending: How Much Difference Does a Dollar Make? All Graduate Plan B and other Reports, Paper 20.
[14] Crampton, F.E. (2009) Spending on School Infrastructure: Does Money Matter? Journal of Educational Administration, 47, 305-322.
[15] Fortunato, S. (2010) Community Detection in Graphs. Physics Reports, 486, 75-174.
[16] Wakita, K. and Tsurumi, T. (2007) Finding Community Structure in Mega-Scale Social Networks. Proceedings of the 16th International Conference on World Wide Web, Banff, 8-12 May 2007, 1275-1276.
[17] Dietterich, T.G. (2000) An Overview of MAXQ Hierarchical Reinforcement Learning. In: Koenig, S. and Holte, R., Eds., Abstraction, Reformulation, and Approximation Anonymous, Springer, Berlin, 26-44.
[18] Kenyon, D.A. (2007) The Property Tax-School Funding Dilemma. Lincoln Institute of Land Policy, Cambridge.
[19] Waters, J.T. and Marzano, R.J. (2006) School District Leadership That Works: The Effect of Superintendent Leadership on Student Achievement. Mid-Continent Research for Education and Learning.
[20] Dhuey, E. and Smith, J. (2011) How Important Are School Principals in the Production of Student Achievement? Working Paper, University of Toronto, Toronto.
[21] Rockoff, J.E. (2004) The Impact of Individual Teachers on Student Achievement: Evidence from Panel Data. American Economic Review, 94, 247-252.
[22] Wong, K.K. (1989) Fiscal Support for Education in American States: The “Parity-to-Dominance” View Examined. American Journal of Education, 97, 329-357.
[23] Zhang, D. (2008) The Effects of Teacher Education Level, Teaching Experience, and Teaching Behaviors on Student Science Achievement. Graduate Thesis, Utah State University, Logan.
[24] Hanushek, E. and Rivkin, S. (2007) Pay, Working Conditions, and Teacher Quality. The Future of Children, 17, 69-86.
[25] Croenweth, S. (2012) Does Teacher Performance Pay Improve Student Achievement?
[26] Bohte, J. (2002) School Bureaucracy and Student Performance at the Local Level. Public Administration Review, 61, 92-99.
[27] Epple, D. and Romano, R.E. (1998) Competition between Private and Public Schools, Vouchers, and Peer-Group Effects. The American Economic Review, 88, 33-62.
[28] Henderson, A. (2002) A New Wave of Evidence: The Impact of School, Family, and Community Connections on Student Achievement. Southwest Educational Development Laboratory, Annual Synthesis.
[29] Ferreyra, M. and Liang, P. (2011) Information Asymmetry and Equilibrium Monitoring in Education. Journal of Public Economics, 96, 237-254.
[30] Chaney, B., Burgdorf, K. and Atash, N. (1997) Influencing Achievement Through high School Graduation Requirements. Educational Evaluation and Policy Analysis, 19, 229-244.
[31] Marchiori, M. and Latora, V. (2000) Harmony in the Small-World. Physica A: Statistical Mechanics and Its Applications, 285, 539-546.
[32] Znaniecki, F. (1986) Social Relations and Social Roles. Irvington Publishing, Manchester.
[33] Müllner, D. (2011) Modern Hierarchical, Agglomerative Clustering Algorithms. ArXiv Organization, Technical Report.
[34] Barto, A. and Mahadevan, S. (2003) Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems, 13, 341-379.
[35] Mirzazadeh, F., Behsaz, B. and Beigy, H. (2007) A New Learning Algorithm for the maxq Hierarchical Reinforcement Learning Method. International Conference on Information and Communication Technology (ICICT’07), Dhaka, 7-9 March 2007, 105-108.
[36] Pinkus, L. (2009) Moving Beyond AYP: High School Performance Indicators. Alliance for Excellent Education. Policy Brief, Washington DC.

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