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Neural network prediction of math and reading proficiency as reported in the Educational Longitudinal Study 2002 based on non-curricular variables [electronic resource] / by Jason D. Brown
Manuscript | 2007.
Available at Gumberg AV Materials - 1st Floor (MF 805 REEL 1925)
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Imprint
2007.
Descript
xii, 136 p. : ill. (some col.)
Note
Title from document title page.
Abstract included in electronic submission form.
Neural network.
Student achievement.
Linear regression.
Prediction.
Back propagation.
Thesis (Ed.D.)--Duquesne University, 2007.
Bibliog.
Includes bibliographical references (p. 69-76).
Summary
Predicting student achievement is often the goal of many studies, and a frequently employed tool for constructing predictive models is multiple linear regression. This research sought to compare the performance of a three-layer back propagation neural network to that of traditional multiple linear regression in predicting math and reading proficiency from 103 non-curricular variables collected in the National Center for Educational Statistics.
Note
System requirements: PC, World Wide Web browser and PDF reader.
Mode of access: World Wide Web.
Faculty advisor: Connie Moss.
Subject
Add Title
Educational longitudinal study 2002
MARC
OCoLC
20080918111526.0
m d
cr mn|nnnancun
080918s2007 xx a s 000 0 eng dntmIa
DUQ DUQ
DUQQ
Computer data (1 file)
Access for Duquesne University Authorized Users http://digital.library.duq.edu/u?/etd,29025
C0 DUQ
Access for Duquesne University Authorized Users http://digital.library.duq.edu/u?/etd,29025
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