Investigating the Factors Affecting Turkish Students PISA 2012 Mathematics Achievement Using Hierarchical Linear Modeling

Abstract

When Turkey's mathematical literacy performance in PISA 2003 and 2012 is examined, it is seen that it has an increase of approximately 25 points between the two assessments. Despite this increase, the fact that the mathematics performance is below the OECD average leads to the need to determine what factors affect the mathematics performance of Turkish students. This study is aimed to investigate the factors affecting mathematics performance of Turkish students at school and school level. Initially Turkish sample consisted a total of 4848 students from 170 schools. As a result of examining the assumptions of the two-level hierarchical linear model; the data of 4236 students from 128 schools were included in the analysis. The results indicate that 64 % of the variability in mathematics achievement was found between schools. Variables associated with attitudes towards mathematics have significant effects on mathematics achievement. Mathematics self-efficacy has the most significant impacts on mathematics achievement after controlling remaining variables. For the school-level variables, proportion of mathematics teachers was found to be a strong predictor of a school's average mathematics achievement. However, it's been found that student-teacher ratio was the only negative predictor of mathematics achievement at school-level. School-level variables explained 44.1% of the variance in the between school difference in mean mathematics achievement.

Prediction of academic achievements of vocational and technical high school (VTS) students in science courses through artificial neural networks (comparison of Turkey and Malaysia)

“…For example, Ong et al (2010) have also reported that the characteristics of the families is the most important factor affecting academic failure of primary school students in Malaysia. One of the most important factors affecting failure in science courses was the lack of private bookshelves, and this result is in line with the results of the studies conducted by Erbaş (2005), Özer and Anıl (2011) and Saşmazer (2006); in addition, the lack of computers and Internet was in line with the results of the studies by Özer and Anıl (2011) and Christman and Badgett (1999). This may be indicative that the use of technology in science education in Malaysia is particularly effective.…”

Examination of Turkey’s PISA 2018 reading literacy scores within student-level and school-level variables

“…This finding constituted a reflection of the literature. The studies focusing on PISA results showed that school-level variables were more dominant than student-level variables in terms of variance in outcome variable (Alacacı & Erbaş, 2010;Astrom & Karlsson, 2007;Özberk, Atalay-Kabasakal, & Boztunç-Öztürk, 2017;Üstün, Özdemir, Cansız, & Cansız, 2019).…”

Determining the Relationships between Selected Variables and Latent Classes in Students’ PISA Achievement

“…In these studies, different models have been developed based on the latest technologies and building on previous work to statistically minimise errors. Multilevel approaches adopted in these models can be grouped into predictive regression (Anıl, 2009;Gülleroğlu, Bilican Demir, and Demirtaşlı, 2014;Thorpe, 2006;Tomul and Çelik, 2009;Xu, 2006;Yıldırım, 2009), structural equation (Akyüz and Pala, 2010;Anıl, 2008;Özer and Anıl, 2011;Usta and Çıkrıkçı-Demirtaşlı, 2014), and hierarchical linear models (Akyüz, 2014;Atar, 2014;Atar and Atar, 2012;Geske et al, 2006;Tavşancıl and Yalçın, 2015). However, in the literature, there are only a limited number of studies which have evaluated student achievement as a latent variable and analysed it based on patterns identified in variables by creating homogenous sub-classes (Finch and Marchant, 2013;Lin and Tai, 2015).…”