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      TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net

      Frontiers in Psychology

      Frontiers Media S.A.

      machine learning, elastic net, regularization, penalized regression, TIMSS, mathematics achievement

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          Abstract

          A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective.

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          Most cited references 22

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          Ridge Regression: Biased Estimation for Nonorthogonal Problems

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            Regression shrinkage and selection via the lasso

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              A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics.

              Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity. Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                15 March 2018
                2018
                : 9
                Affiliations
                Department of Education, Korea National University of Education , Cheongju, South Korea
                Author notes

                Edited by: Jason C. Immekus, University of Louisville, United States

                Reviewed by: Jung Yeon Park, Teachers College, Columbia University, United States; Eun Hye Ham, Kongju National University, South Korea

                *Correspondence: Jin Eun Yoo jineun.yoo@ 123456gmail.com

                This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2018.00317
                5862814
                Copyright © 2018 Yoo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 2, Tables: 5, Equations: 6, References: 38, Pages: 10, Words: 7709
                Categories
                Psychology
                Original Research

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