A Multiple Linear Regression-Based Approach to Predict Student Performance

TitreA Multiple Linear Regression-Based Approach to Predict Student Performance
Publication TypeJournal Article
Year of Publication2020
AuthorsO. Aissaoui, E, Y. Madani, EAlami El, Oughdir, L, Dakkak, A, Y. Allioui, E
JournalAdvances in Intelligent Systems and Computing
Volume1102 AISC
Pagination9-23
Mots-clésData mining, Education computing, Educational data mining, Forecasting, Intelligent systems, K fold cross validations, Learning systems, Linear regression, Machine learning techniques, Multiple linear regressions, Multivariate adaptive regression splines, Performance prediction models, planning, Predictive analytics, Statistical analysis methods, Students, Supervised learning, Supervised machine learning, Sustainable development
Abstract

Predicting students’ academic outcome is useful for any educational institution that aims to ameliorate students’ performance. Based on the resulted predictions, educators can provide support to students at risk of failure. Data mining and machine learning techniques were widely used to predict students’ performance. This process called Educational data mining. In this work, we have proposed a methodology to build a student’ performance prediction model using a supervised machine learning technique which is the multiple linear regression (MLR). Our methodology consists of three major steps, the first step aims to analyze and preprocess the students’ attributes/variables using a set of statistical analysis methods, and then the second step consists in selecting the most important variables using different methods. The third step aims to construct different MLR models based on the selected variables and compare their performance using the k-fold cross-validation technique. The obtained results show that the model built using the variables selected from the Multivariate Adaptive Regression Splines method (MARS), outperforms the other constructed models. © Springer Nature Switzerland AG 2020.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078413089&doi=10.1007%2f978-3-030-36653-7_2&partnerID=40&md5=893772562739d5d20e6b6ae21ebae325
DOI10.1007/978-3-030-36653-7_2
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