Using data analytics to predict students score

• Main Authors: MA, Nang Laik, CHUA, Gim Hong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: STEM; education; machine learning; inter-relationship; social; economics; predictive models; Singapore; MITB student; Asian Studies; Data Science; Educational Assessment, Evaluation, and Research; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/5982; https://ink.library.smu.edu.sg/context/sis_research/article/6985/viewcontent/Ma__N._L.____Chua__G._H.__2020_._Using_Data_Analytics_to_predict_students_score.pdf
• Institution: Singapore Management University

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.