Predicting Student Performance in Statewide High-Stakes Tests for Middle School Mathematics Using the Results from Third Party Testing Instruments

Rusen Meylani, Gary G. Bitter, Rene Castaneda


In this study regression and neural networks based methods are used to predict statewide high-stakes test results for middle school mathematics using the scores obtained from third party tests throughout the school year. Such prediction is of utmost significance for school districts to live up to the state’s educational standards mandated by the No Child Left Behind Act by helping them take the necessary measures in a timely manner and avoid penalties such as decreased funding, salary cuts, job losses, the state taking over the school administration, etc. Although the predictive analyses were performed in the context of middle school mathematics, the suggested models can readily be applied to other grade levels and content areas as well.

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Journal of Education and Learning   ISSN 1927-5250 (Print)   ISSN 1927-5269 (Online)


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