Using data analytics to predict students score

Author

Nang Laik MA, Singapore University of Social Sciences
Gim Hong CHUA, Singapore Management UniversityFollow

Publication Type

Working Paper

Version

publishedVersion

Publication Date

11-2020

Abstract

Education is very important to Singapore, and the government has continued to invest heavily in our education system to become one of the world-class systems today. A strong foundation of Science, Technology, Engineering, and Mathematics (STEM) was what underpinned Singapore's development over the past 50 years. PISA is a triennial international survey that evaluates education systems worldwide by testing the skills and knowledge of 15-year-old students who are nearing the end of compulsory education. In this paper, the authors used the PISA data from 2012 and 2015 and developed machine learning techniques to predictive the students' scores and understand the inter-relationships among social, economic, and education factors. The insights gained would be useful to have fresh perspectives on education, useful for policy formulation.

Keywords

STEM, education, machine learning, inter-relationship, social, economics, predictive models, Singapore, MITB student

Discipline

Asian Studies | Data Science | Educational Assessment, Evaluation, and Research | Numerical Analysis and Scientific Computing

First Page

1

Last Page

10

Embargo Period

6-3-2021

Citation

MA, Nang Laik and CHUA, Gim Hong. Using data analytics to predict students score. (2020). 1-10.
Available at: https://ink.library.smu.edu.sg/sis_research/5982

Copyright Owner and License

Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://arxiv.org/abs/2012.00105