Using machine learning to predict student achievement on the state of Texas Assessment of Academic Readiness examination in charter schools

Author

Christopher D. Gonzalez, The University of Texas Rio Grande Valley

Date of Award

12-2016

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Emmett Tomai

Second Advisor

Dr. Richard Fowler

Third Advisor

Dr. Wendy Lawrence-Fowler

Abstract

The purpose of this study was to research and develop a way to use machine learning algorithms (MLAs) to predict student achievement on the State of Texas Assessment of Academic Readiness (STAAR), specifically in the charter school setting. Charter schools have the disadvantage of a constant influx in students, so providing historical student data in order to analyze trends proves difficult. This study expands on previous research done on students in secondary and post-secondary school and determining features that indicate success in these settings. The data used is from the district of IDEA Public Schools who focuses on providing education to low income and minority populations. This study uses data that was readily available to IDEA Public Schools and MLAs provided by MATLAB to create models in order to predict if a student is going to meet the standard on the STAAR test at the end of the year.

Comments

Copyright 2016 Christopher D. Gonzalez. All Rights Reserved.

https://www.proquest.com/dissertations-theses/using-machine-learning-predict-student/docview/1878202159/se-2?accountid=7119

Recommended Citation

Gonzalez, Christopher D., "Using machine learning to predict student achievement on the state of Texas Assessment of Academic Readiness examination in charter schools" (2016). Theses and Dissertations. 210.
https://scholarworks.utrgv.edu/etd/210