Exploring school factors of academic achievement in Macau: The application of educational data mining in Progress in International Reading Literacy Study (PIRLS) 2016 assessment

Abstract

International large-scale assessments, provide structured and static data, and due to their extensive databases, they can be considered as a valuable resource for Big Data in Education. In this paper, we propose an educational data mining approach to detect and analyze factors linked to academic performance in Macau schools using data from the Progress in International Reading Literacy Study (PIRLS) 2016. We conducted a secondary data analysis based on a set of socioeconomic, process, and outcome variables from PIRLS and other sources, and built decision trees to obtain a predictive model of school performance. By doing so, we were able to identify the school and student-level variables that are most significant in predicting student performance in Macau. These findings will be useful for informing educational policy decisions and shedding light on the causes of poor performance in Macau schools. Overall, our study highlights the potential of educational data mining approaches in analyzing large-scale assessment data and generating insights for educational research and practice.