Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

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 pr...

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Bibliographic Details
Published inJournal of Information Processing Vol. 26; pp. 170 - 176
Main Authors Yang, Stephen J.H., Lu, Owen H.T., Huang, Anna Y.Q., Huang, Jeff C.H., Ogata, Hiroaki, Lin, Albert J.Q.
Format Journal Article
LanguageEnglish
Published Tokyo Information Processing Society of Japan 01.01.2018
Japan Science and Technology Agency
Subjects
Academic achievement
Accuracy
Analytics
Data management
Error analysis
learning analytics
Mathematical analysis
Model accuracy
multiple linear regression
Performance prediction
principal component analysis
Principal components analysis
Regression analysis
Regression models
Students
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Summary: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.
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ISSN:1882-6652
1882-6652
DOI:10.2197/ipsjjip.26.170