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ERIC Number: EJ1075800
Record Type: Journal
Publication Date: 2014
Pages: 9
Abstractor: As Provided
ISSN: ISSN-1927-5250
Predicting Student Performance in Statewide High-Stakes Tests for Middle School Mathematics Using the Results from Third Party Testing Instruments
Meylani, Rusen; Bitter, Gary G.; Castaneda, Rene
Journal of Education and Learning, v3 n3 p135-143 2014
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.
Canadian Center of Science and Education. 1120 Finch Avenue West Suite 701-309, Toronto, OH M3J 3H7, Canada. Tel: 416-642-2606; Fax: 416-642-2608; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Middle Schools; Secondary Education; Junior High Schools; Grade 6; Intermediate Grades; Elementary Education; Grade 7; Grade 8
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Arizona
Grant or Contract Numbers: N/A