Journal on Policy & Complex Systems Volume 3, Issue 2 | Page 80

Education System Intervention Modeling Framework
Figure 1 . Agent network for SLIDER case study .
in terms of having a “ control ” variable , a common fallacy is to design an intervention only within this type of setting , only to have it fail in settings that are more complex . In terms of scalability , it is important to design and refine interventions in schools with more constraints . The other two schools exhibited much more variation and change during the course of the intervention . Through collaboration with the SLIDER team at Georgia Tech ( GT ) ( educational researchers and practitioners ) and public policy researchers , we apply the ESIM framework to the SLIDER case study .
3.1 Model Definition
Figure 1 shows the agent network for SLIDER for two scales of analysis simultaneously . At the macroscale , GT is interacting with three schools and allocating resources dynamically . Within each school , the agent networks contain teachers , students , and administrators . The blue arrows represent relationships , and the green arrows represent resource flows . The resource flows for SLIDER include
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PD ( time ) and supplies from GT . The attributes of the agents and school identified as potentially relevant by the SLIDER development and research team are shown in Table 1 .
External trends are changes occurring in schools that are independent of the intervention . These trends are modeled as external forces on the school that are not impacted by the intervention . For SLIDER , there were two primary external forces considered : student – teacher ratio and test score trends . While the student – teacher ratio at Rural School was constant , the student – teacher ratio at Suburban School increased significantly after the start of the intervention , likely due to the economic recession and layoffs occurring in 2008 – 2010 . The eighth grade science test scores at the two schools were examined for trends and were compared with district performance to assess external pressures on the schools . For Rural School , test scores were comparable with overall district performance , but at Suburban School , the school average was significantly below the district average . Because of this gap between school and