Does School Related Factors Affect Students’ PISA 2015 Science Achievement Test Score in Turkey?
Author(s):
Mehmet İkbal Yetişir (presenting / submitting) Kaan Batı
Conference:
ECER 2017
Network:
11. Educational Improvement and Quality Assurance
Format:
Paper

Session Information

11 SES 05, What Can We Learn from Standardized Tests?

Paper Session

Time:
2017-08-23
13:30-15:00
Room:
W2.10
Chair:
Ineta Luka

Contribution

PISA (The Program for International Student Assessment) is a research project by the OECD that evaluates the level of science, mathematics and reading literacy of 15-year-old children in participating countries. The project also makes it possible to compare countries. PISA results allow students to determine the factors affecting their knowledge, skills and attitudes, as well as their level of literacy in each three domain. (Bybee, Fensham, and Laurie, 2009). When the literature is examined, it is seen that the socio-economic level is the most important factor determining the literacy and literacy levels of the students (Perry, 2010). According to Perry (2010), not only the socio-economic level of pupils but also the socio-economic level of the school is at the top of the factors directly affecting science achievement. Stacy (2010) has shown that children in low socioeconomic status have lower academic achievement in the science field, as well as lower risk of counterfeiting than students in the higher socioeconomic level. In addition, Switzerland, Belgium, Ireland and France are among the finds that investigate whether this effect is stronger. Sun, Bradley, and Kathryn (2012) found that not only the socio-economic level but also the value of the parents of the students and the attitudes they showed affects the positive attitude of the students positively. It may not be possible to explain this effect directly at the socio-economic level, but the supporting work can be explained by the findings of the research conducted by Hong and Huang (2012). Lin, Hong and Huang found that emotional factors such as interest, enjoyment, etc., significantly influenced students' literacy levels. Undoubtedly, pupils can gain positive emotions that their parents have, and in this way they can develop a positive attitude towards the pupil.

The results of the survey on the Turkish sample show similar results with other OECD countries. It has been determined in many studies that family characteristics such as parents 'education level and the number of books in the home have a significant effect on the students' science achievement (Erbaş, 2005; Karabay, 2013, Karabay, 2012). Is Peki a consistent variable in terms of science achievement and literacy levels of socioeconomic level students in Turkey? According to Chechen (2015), the answer is yes. As a result of Chechen analysis, the results of PISA for 2003, 2006, 2009 and 2012 revealed that the socio-economic levels of students are a good predictor of academic achievement and literacy levels of students.

Of course, the only factor that determines science achievement and literacy is not socio-economic level. Studies have shown that external factors such as the adequacy of educational materials, the nature of teachers and school characteristics affect the academic achievement and literacy levels (Karabay, 2013, Çelebi 2010). In addition, it was determined that students' self-sufficiency directly affected the levels of science literacy (Usta, 2009). There is no direct relationship between science and science literacy in Turkey. However, as the value given to science increases indirectly, the importance given to scientific inquiry increases, and as the importance given to interrogation increases, an increase in science performance is observed.

Research questions

The study aims to answer the following questions:

1. Does science achievement vary significantly among eighth graders in Turkey?

2. Do ESCS and gender affect science achievement in Turkey?

3. Are school related some factors (school resources, school climate and teaching staff) associated with science achievement in Turkey?

Method

Data Source As this research was concerned with Turkish 15 –year –old students, the data collected from school principals and the 15 –year-old students by PISA 2015 were used in this study. The 5895 students’ and 187 school principals’ responses were analyzed in an attempt to answer the research questions. (OECD, 2016). Variables The dependent variable in this study is the science achievement of students. All science plausible values were used in the analysis. Gender and ESCS (index of economic, social and cultural status) were employed at student level. School resources, school climate (reflected student behavior and teacher behavior hindering learning) and teaching staff were selected to be used in school level. Analytical Models Because of in the PISA data were collected both student level and school level, the structure of PISA data set is nested. Neglecting this feature of the data set and using ordinary least squares regression of analyzing data from such nested structures will cause the loss characteristic dependencies. Consequently, applying an ordinary least squares regression analysis to the nested structure fails and the Type I error is likely to be inflated (Raudenbush & Bryk, 2002). Using HLM in nested data structure will overcomes these limitations by modelling all levels of data. HLM simultaneously investigates relationships within and between hierarchical levels of grouped data, thereby making it more efficient at accounting for variance among variables at different levels than other existing analyses (Woltman et al., 2012). In order to avoid errors mentioned above, HLM analysis was conducted. HLM analysis was conducted in three steps. As the first step (fully unconditional), variance was partitioned in science achievement into its between-school and within-school components. This preliminary model is equivalent to a one-way ANOVA with random effects (Saed & Hammouri, 2010). As an indicator of inequality between schools in the country intra-class correlation (ICC), was calculated. Additionally, in this step the reliability of science achievement was estimated. Second step is random coefficient model (Partially conditional). This model tests the relationship between the student level predictor variable and the outcome variable (science achievement) and the relative strength of the effects of student level variables (Woltman et al., 2012; Raudenbush & Bryk, 2002). Fully conditional model, as the third model, examines whether level-2 factors affect the students’ average science achievement within the same school, and how much variance in science achievement among schools could be explained by these level-2 factors.

Expected Outcomes

It is predicted that science achievement will vary significantly among students and that these differences will vary depending on the ESCS variable, not on the gender variable. Because studies on PISA results did not reveal concrete evidence that the gender factor could affect academic achievement about Turkish students. Likewise, the school variables such as school resources, school climate and teaching staff are thought to not significantly affect the science achievement of students in Turkey. However, findings from the literature show that the socio-economic level has a significant effect on student achievement. It is thought that school-based variables will be indirectly affected by the socio-economic level. Therefore, there is little doubt that the school type variable can be indirectly influenced by the student's success. As a result of the analyzes to be done, it will be determined how the school-related factors explain the variance of the student's science achievement. Additionally findings will be discussed and interpreted in recent years with improvements in school resources in Turkey.

References

Bybee, R., fensham, P. J., Laurie, R. (2009). Scientific Literacy and Contexts in PISA 2006 Science, JOURNAL OF RESEARCH IN SCIENCE TEACHING, VOL. 46, NO. 8, PP. 862–864 Laura Perry (2010). Does the SES of the school matter? Teachers College Record Volume, 112 (4), pp. 1137–1162 Stacey, K. (2010). Mathematical and Scientific Literacy around the World. Journal of Science and Mathematics Education in Southeast Asia, 33(1), 1-16. Huann-shyang Lin, Zuway-R. Hong & Tai-Chu Huang (2012) The Role of Emotional Factors in Building Public Scientific Literacy and Engagement with Science, International Journal of Science Education, 34:1, 25-42, DOI: 10.1080/09500693.2010.551430 Letao Sun , Kelly D. Bradley & Kathryn Akers (2012) A Multilevel Modelling Approach to Investigating Factors Impacting Science Achievement for Secondary School Students: PISA Hong Kong Sample, International Journal of Science Education, 34:14, 2107-2125, DOI: 10.1080/09500693.2012.708063 Karabay, Ersoy 2013 Investıgatıon Of The Predıctıve Power Of Famıly And School Characterıstıcs For Pısa Readıng Skılls, Mathematıcs And Scıence Lıteracy By Years Gazi Üniversitesi Eğitim Bilimleri Enstitüsü Master’s Degree, Department Of Educational Administration And Supervision Karabay, Ersoy 2012 Examınatıon Of The Predıctıve Powers Of Socıo-Cultural Varıables For Pısa Scıence Lıteracy By Years Ankara Üniversitesi Eğitim Bilimleri Enstitüsü Master’s Degree, Division Of Educational Measurement And Evaluation Hatice Gonca Usta 2009 The Factors That Effect Students’ Scıentıfıc Lıteracy Acordıng To Pısa 2006 In Turkey Ankara Üniversitesi Eğitim Bilimleri Enstitüsü Graduate Program For Educational Measurement And Evaluation Erbaş, K. C. (2005). Factors affecting scientific literacy of students in turkey in programme for international student assessment (PISA). Master Thesis of Middle East Technical University, Department of Secondary Science and Mathematics Education. Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage. Saed, S., & Hammouri, H. (2010). Does subject matter matter? Estimating the impact of instructional practices and resources on student achievement in science and mathematics: Findings from TIMSS 2007. Evaluation & Research in Education, 23(4), 287-299. Özgür Çelebi 2010 A Cross-Cultural Comparıson Of The Effect Of Human And Physıcal Resources On Students’ Scıentıfıc Lıteracy Skılls In The Programme For Internatıonal Student Assessment (Pısa) 2006 A Thesıs Submıtted To The Graduate School Of Natural And Applıed Scıences Of Mıddle East Technıcal Unıversıty Yeliz Çeçen 2015 Examination Of The Predictive Powers Of Socıocultural And Socıo-Economıc Varıables For Pısa Scıence Lıteracy By Years. Master Thesis of İstanbul Aydın University Social Sciences Enstitute.

Author Information

Mehmet İkbal Yetişir (presenting / submitting)
Ankara University
Faculty of Educational Sciences
Ankara
Kaan Batı
Hacettepe University, Turkey

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