Abstract
Public school officials are charged with ensuring that students receive a strong fundamental education. One tool used to test school efficacy is the standardized test. In this paper, we build a predictive model as an early warning system for schools that may fall below the state average in building level average proficiency in the Michigan Educational Assessment Program (MEAP). We utilize data mining techniques to develop various decision tree models and logistic regression models, and found that the decision tree model with entropy impurity measure accurately predicts school performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ryan S.J.d. Baker. Data mining for education. International encyclopedia of education, 7:112–118, 2010.
Alejandro Peña-Ayala. Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications, 41(4):1432–1462, 2014.
Ryan Shaun Baker, Albert T Corbett, and Kenneth R Koedinger. Detecting student misuse of intelligent tutoring systems. In International Conference on Intelligent Tutoring Systems, pages 531–540. Springer, 2004.
Joseph E Beck. Engagement tracing: using response times to model student disengagement. In Proceedings of the 12th International Conference on Artificial Intelligence in Education, pages 88–95, 2005.
R Charles Murray and Kurt VanLehn. Effects of dissuading unnecessary help requests while providing proactive help. In Proceedings of the 12th International Conference on Artificial Intelligence in Education, pages 887–889, 2005.
Alex J Bowers, Ryan Sprott, and Sherry A Taff. Do we know who will drop out?: A review of the predictors of dropping out of high school: Precision, sensitivity, and specificity. The High School Journal, 96(2):77–100, 2013.
Bradley Carl, Jed T Richardson, Emily Cheng, HeeJin Kim, and Robert H Meyer. Theory and application of early warning systems for high school and beyond. Journal of Education for Students Placed at Risk (JESPAR), 18(1):29–49, 2013.
Brijesh Kumar Baradwaj and Saurabh Pal. Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2(6):63–69, 2011.
Jared E Knowles. Of needles and haystacks: Building an accurate statewide dropout early warning system in Wisconsin. Journal of Educational Data Mining, 7(3):18–67, 2015.
Center for Educational Performance and Information. Michigan School Data. http://www.michigan.gov/cepi.
Michigan Department of Education. Michigan School Data. https://www.mischooldata.org.
Federal Bureau of Investigation. Uniform Crime Report. https://www.fbi.gov/about-us/cjis/ucr/ucr.
United States Census Bureau. American Community Survey (ACS). https://www.census.gov/programs-surveys/acs/data.html.
Theodore G Chiricos. Rates of crime and unemployment: An analysis of aggregate research evidence. Social problems, 34(2):187–212, 1987.
J. Ross Quinlan. Induction of decision trees. Machine learning, 1(1):81–106, 1986.
Frank Harrell. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer, 2015.
Caroline M Hoxby. The effects of class size and composition on student achievement: new evidence from natural population variation. Technical report, National Bureau of Economic Research, 1998.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Sullivan, W., Marr, J., Hu, G. (2017). A Predictive Model for Standardized Test Performance in Michigan Schools. In: Lee, R. (eds) Applied Computing and Information Technology. Studies in Computational Intelligence, vol 695. Springer, Cham. https://doi.org/10.1007/978-3-319-51472-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-51472-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51471-0
Online ISBN: 978-3-319-51472-7
eBook Packages: EngineeringEngineering (R0)