LAUSR

In any society, education plays a critical role because it determines the perspectives of individuals’ life. Academic performance is highly correlated with the individuals’ future career and occupational choices. To assess academic performance over time, there are studies such as Programme for International Student Assessment (PISA) organized by the Organization Economic for Co-Operation and Development (OECD), Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS) organized by International Association for the Evaluation of Educational Achievement (IEA). More specifically, science achievement is about understanding and applying the fundamental knowledge of science, drawing conclusions based on data and evidence and developing the significance of science and technology in daily lives (OECD, 2017). In the literature, empirical research have been made to specify the underlying factors of students’ achievement (Contini, Di Tommaso, & Mendolia, 2017; Sheldrake, Mujtaba, & Reiss, 2017; Kılıç Depren, Aşkın, & Öz, 2017; Kılıç Depren, 2018). Zhang, Khan, and Tahirsylaj (2015) have used regression methods to measure the factors affecting students’ performance using the dataset of PISA 2009 participating countries. The research of Delprato and Chudgar (2018) has found out the systemic school factors on the privatepublic performance gap using the Teaching and Learning International Survey (TALIS)-PISA dataset in three countries. Based on a multilevel model, Giambona and Porcu (2018) have identified the most important factor on students’ achievement was school size. In this research, it was assumed that the students’ science achievement scores were reflecting a mixture distribution that is a sample of students representing various backgrounds, with some or all backgrounds associated with different distributions and mean scores. Thus, Quantile Regression Mixture Model (QRMIX), which is a unique approach in the education literature, was used to determine the factors affecting students’ science achievement in Turkey and Singapore. Abstract. In the last decade, the usage of advanced statistical models is growing rapidly in many different disciplines. However, the Quantile Regression Mixture Model (QRMIX), which is a developed approach of the Finite Mixture Model (FMM), is an applicable new method in the educational literature. The aim of the proposed study was to determine factors affecting students’ science achievement using the QRMIX approach. To reach this aim, data of the Programme for International Student Assessment (PISA) survey, which has been conducted by the Organization Economic for Co-Operation and Development (OECD) every 3 years, was used. Dataset used in the research is composed of 6,115 students from Singapore, which is the top-performer country among the participant countries, and 5,895 students from Turkey. The results showed that the factors affecting students’ science achievement and its importance on the achievement differentiated according to the achievement levels of the students. In conclusion, it was revealed that Turkish students with the lowest science achievement level should be supported with home possessions, perceived feedback, and environmental awareness and Singaporean students with the lowest achievement level should be supported with perceived feedback, enjoyment of science, and epistemological beliefs.