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Title: Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis.
Authors: Yang, Stephen J.H.
Lu, Owen H.T.
Huang, Anna Y.Q.
Huang, Jeff C.H.
Ogata, Hiroaki  kyouindb  KAKEN_id
Lin, Albert J.Q.
Author's alias: 緒方, 広明
Keywords: learning analytics
multiple linear regression
principal component analysis
Issue Date: Feb-2018
Publisher: Information Processing Society of Japan
Journal title: Journal of Information Processing
Volume: 26
Start page: 170
End page: 176
Abstract: With the rise of big data analytics, learning analytics has become a major trend for improving the quality of education. Learning analytics is a methodology for helping students to succeed in the classroom; the principle is to predict student's academic performance at an early stage and thus provide them with timely assistance. Accordingly, this study used multiple linear regression (MLR), a popular method of predicting students' academic performance, to establish a prediction model. Moreover, we combined MLR with principal component analysis (PCA) to improve the predictive accuracy of the model. Traditional MLR has certain drawbacks; specifically, the coefficient of determination (R²) and mean square error (MSE) measures and the quantile-quantile plot (Q-Q plot) technique cannot evaluate the predictive performance and accuracy of MLR. Therefore, we propose predictive MSE (pMSE) and predictive mean absolute percentage correction (pMAPC) measures for determining the predictive performance and accuracy of the regression model, respectively. Analysis results revealed that the proposed model for predicting students' academic performance could obtain optimal pMSE and pMAPC values by using six components obtained from PCA.
Rights: ここに掲載した著作物の利用に関する注意 本著作物の著作権は情報処理学会に帰属します。本著作物は著作権者である情報処理学会の許可のもとに掲載するものです。ご利用に当たっては「著作権法」ならびに「情報処理学会倫理綱領」に従うことをお願いいたします。Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPSJ. Please be complied with Copyright Law of Japan and the Code of Ethics of the IPSJ if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof. All Rights Reserved, Copyright (C) Information Processing Society of Japan. Comments are welcome. Mail to address, please.
DOI(Published Version): 10.2197/ipsjjip.26.170
Appears in Collections:Journal Articles

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