@article {841,
	title = {DETERMINATION OF THE FACTORS AFFECTING STUDENTS{\textquoteright} SCIENCE ACHIEVEMENT LEVEL IN TURKEY AND SINGAPORE: AN APPLICATION OF QUANTILE REGRESSION MIXTURE MODEL},
	journal = {Journal of Baltic Science Education},
	volume = {19},
	year = {2020},
	month = {April/2020},
	pages = {Continuous},
	type = {Original article},
	chapter = {247-260},
	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{\textquoteright} 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{\textquoteright} 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.},
	keywords = {finite mixture models, Programme for International Student Assessment, quantile regression mixture models, science performance},
	issn = {1648-3898},
	doi = {https://doi.org/10.33225/jbse/20.19.247},
	url = {http://oaji.net/articles/2020/987-1586941544.pdf},
	author = {Kilic Depren, S.}
}
@article {711,
	title = {PREDICTION OF STUDENTS{\textquoteright} SCIENCE ACHIEVEMENT: AN APPLICATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES AND REGRESSION TREES},
	journal = {Journal of Baltic Science Education},
	volume = {17},
	year = {2018},
	month = {October/2018},
	pages = {Continuous},
	type = {Original article},
	chapter = {887-903},
	abstract = {Turkey is ranked at the 54th out of 72 countries in terms of science achievement in the Programme for International Student Assessment (PISA) survey conducted in 2015, which is a very big disappointment for that country. The aim of this research was to determine factors affecting Turkish students{\textquoteright} science achievements in order to identify the improvement areas using PISA 2015 dataset. To achieve this aim, Multivariate Adaptive Regression Splines (MARS) and Classification and Regression Trees (CART) approaches were used and these approaches were compared in terms of model accuracy statistics. Since Singapore was the top performer country in terms of science achievement in PISA 2015 survey, the analysis results of Turkey and Singapore were compared to each other to understand the differences. The results showed that MARS outperforms the CART in terms of measuring the prediction of students{\textquoteright} science achievement. Furthermore, the most important factors affecting science achievements were environmental optimism, home possessions and science learning time (minutes per week) for Turkey, while the index of economic, social and cultural status, environmental awareness and enjoyment of science for Singapore.},
	keywords = {higher education, machine learning algorithms, PISA, Science Achievement},
	issn = {1648-3898},
	doi = {https://doi.org/10.33225/jbse/18.17.887 },
	url = {http://oaji.net/articles/2017/987-1539418761.pdf},
	author = {Kilic Depren, S.}
}
