An Agent-Based Simulation Environment for Predictive Longitudinal Modeling of High School Math Performance (Collaborative Research: Resta)

This project will test the efficacy of using agent-based simulation and visualization models to identify the factors that predict mathematics achievement for students from the 8th grade to the 12th grade and beyond. The team is using data that includes 14 years of data on student grade reports, coursework, demographics, teacher variables such as years of service, professional development courses taken, years of service, and other artifacts.

Lead Organization(s): 
Award Number: 
1119312
Funding Period: 
Tue, 11/15/2011 - Wed, 10/31/2012
Full Description: 

This collaborative proof-of-concept study involves The University of California at Santa Cruz, The University of Texas at Austin, and the Los Alamos National Laboratory. The PIs will test the efficacy of using agent-based simulation and visualization models to identify the factors that predict mathematics achievement for students from the 8th grade to the 12th grade and beyond. The team are using a data set that includes 14 years of data on student grade reports, coursework, demographics, teacher variables such as years of service, professional development courses taken, years of service, and other artifacts. The investigators hypothesize that agent-based modeling can be used to improve mathematics education. The research questions is What are the predictors of success in mathematics of public school 8th grade students and beyond as measured by a) mathematics performance (test scores) broken down by different mathematical skills? b) enrollment in algebra class (8th grade and high school)? and c) algebra and mathematics grades in 8th grade and high school? This exploratory study will analyze data using three tasks. The first task involves data assessment. The first task will involve discovering distributional information in general. They will explore visual and analytical processes of different variables so that different synthetic data can be simulated. The second task involves collaborating with a statistical science team to incorporate distributional information so that multivariate samples can be generated to form synthetic populations to use to build the agent-based model. The third task involves using the actual data from two large school districts to understand and quantify variability in the data.

Education systems do not have a valid way to measure progressions of mathematics education to evaluate outcomes associated with mathematics learning outcomes. This project will provide a baseline understanding of student's progression in mathematics achievement that is critical in helping educators and policy makers set goals and standards for mathematics education within the United States.

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