Aslan, G., Küçüker, E. (2023). Rethinking the academic 

achievement (failure) of vocational high school 

students: student (family) or school, what is 

decisive? International Online Journal of Education 

and Teaching (IOJET), 10(2). 1061-1091.  

Received  : 11.12.2022 

Revised version received : 28.02.2023 

Accepted  : 01.03.2023 

 

 

RETHINKING THE ACADEMIC ACHIEVEMENT (FAILURE) OF VOCATIONAL 

HIGH SCHOOL STUDENTS: STUDENT (FAMILY) OR SCHOOL, WHAT IS 

DECISIVE? 

 (Research article)  

 

Gülay Aslan  (0000-0002-4067-0223) 

Tokat Gaziosmanpaşa University, Turkey 

gulay.aslan@gop.edu.tr  

 

Erdal Küçüker  (0000-0001-6040-0848) 

Tokat Gaziosmanpaşa University, Turkey 

erdal.kucuker@gop.edu.tr  

 

 

Biodata(s): 

Dr. Gülay Aslan is a Professor at Tokat Gaziosmanpaşa University, Faculty of Education, 

Department of Educational Sciences, Tokat, Turkey. 

 

Dr. Erdal Küçüker is an associate professor at Tokat Gaziosmanpaşa University, Faculty of 

Education, Department of Educational Sciences, Tokat, Turkey. 

 

 

Copyright © 2014 by International Online Journal of Education and Teaching (IOJET). ISSN: 2148-225X.  

Material published and so copyrighted may not be published elsewhere without written permission of IOJET.  

mailto:gulay.aslan@gop.edu.tr
mailto:erdal.kucuker@gop.edu.tr


Aslan& Küçüker 

1062 

  
  

RETHINKING THE ACADEMIC ACHIEVEMENT (FAILURE) OF 

VOCATIONAL HIGH SCHOOL STUDENTS: STUDENT (FAMILY) OR 

SCHOOL, WHAT IS DECISIVE? 

 

Gülay Aslan 
gulay.aslan@gop.edu.tr   

 
Erdal Küçüker 

erdal.kucuker@gop.edu.tr  

 

Abstract 

The purpose of this study is to analyze the academic achievement of vocational high school 

students through student level and school level variables. Student level variables were 

demographic data, socioeconomic and socio-cultural characteristics of the family, the 

educational environment and opportunities provided to the student, as well as attitude scores 

determined by the Attitude towards School Scale. School level variables were the total number 

of students and teachers in the schools. Hierarchical Linear Model (HLM) was used to analyze 

student and school level variables. Vocational high school students come mainly from the 

lower socioeconomic strata of society. Academic achievements of students differ according to 

schools. The school level variable that predicts academic achievement is the total student 

number in the school. The more the number increases, the academic achievement of the 

students decreases. Variables that determine academic achievement in vocational education is 

predominantly (63% probability) related to socioeconomic and socio-cultural variables at the 

student level.  

Keywords: Academic achievement, vocational high school, HLM, secondary education, 

Turkey 

 

 

 

1. Introduction 

There is almost a social consensus regarding the low academic achievement of vocational 

high school students in Turkey. All parties of this social consensus, especially the politicians 

or bureaucrats who direct the education system, generally mention the negativity of the social 

perception of these high schools as a priori regarding the reasons for the academic failure of 

vocational high school students; however, they do not provide sufficient analytical data 

(Ministry of National Education [MoNE], 2018a; 2018b). A few studies on the subject draw 

attention to some out-of-school or systemic variables (Aedo, Naqvi, & Cahu, 2013; Education 

Reform Initiative [ERG], 2009; 2012; Oral & Mcgivney, 2014; Turkish Education Association 

[TED, 2010]). The situation of vocational high schools in Turkey is becoming increasingly 

familiar; the demand for these high schools is doomed to mandatory placements based on 

address. Thus, vocational high schools turn into a type in which students who have not settled 

in any of their preferences are in the majority. This situation presents an oxymoron appearance. 

On the one hand, it is emphasized that vocational education is very important, on the other 

hand, it is difficult to find students for these high schools; families do not send their children 

mailto:gulay.aslan@gop.edu.tr
mailto:erdal.kucuker@gop.edu.tr


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1063 

 

to these schools unless they have to. Thus, children who do not get enough points in the high 

school transition exam are necessarily clustered in these high schools. So why do the most 

"unsuccessful" children cluster in angel high schools? Are there any common characteristics 

that define these children? Is this clustering a result of children's low ability and effort; or does 

it indicate a systemic disintegration? 

Academic achievement is one of the important quantitative indicators used to evaluate the 

efficiency of education systems. Countries, regardless of their type and level, make an effort 

to increase academic success in the education system. In fact, in determining the national level 

of academic achievement, it is seen that central exams that allow international comparisons 

have increased and countries have reorganized their education systems and policies depending 

on the results of these exams. Of course, this is a meaningful effort for countries. Because every 

country wants the children to whom it will entrust its future to be more successful and desires 

to develop the human potential in the best way. At the same time, the way to be ahead in 

international competition and to increase national welfare is to train high-qualified manpower 

within the framework of a developmental paradigm (Âdem, 1993, 2008; Hesapçıoğlu, 1984, 

1994; Karakütük, 2012; Kavak, 1990; Serin, 1979). Raising high-qualified manpower has a 

cost for every country. Therefore, countries also strive for efficient and effective functioning 

of their education systems.  

Various methods used in the evaluation of the efficiency of the education system are 

mentioned in the education economics literature. One of them is the internal productivity rate, 

which allows the evaluation of the internal functioning of the system, and the other is the 

external productivity rate, which evaluates the suitability of the grown manpower for economic 

and social life (Âdem, 1993; Bülbül, 1988; Hesapçıoğlu, 1994; Serin, 1979; Ünal, 1996). The 

internal productivity ratio, calculated based on students' academic achievement, is a 

quantitative indicator of a student's progress in the education system. With internal efficiency 

indicators; it is possible to determine the classes, levels, and types of education where the 

efficiency has decreased, and even the classes with low academic success and the classes or 

levels with a high probability of dropout and dropout are possible. Therefore, any policy to 

increase academic achievement or reduce dropout directly means increasing the internal 

efficiency of the system. The external efficiency of the education system is related to the 

conformity of the educational qualifications gained through education to the society (Bülbül, 

1988; Ünal, 1996). Calculating the internal efficiency of a system and evaluating its external 

efficiency provide important data in terms of education policies and planning studies. However, 

it is necessary to know the variables that affect the academic achievement of students in order 

to increase the internal efficiency of the system and to develop policies at macro and micro 

level regarding external efficiency. For this reason, academic achievement is not only an 

indicator of the results of individuals' efforts for countries. When considered from a 

technocratic point of view, it is also an indicator of whether the resources allocated to education 

are used efficiently and effectively and that the outputs of the education system are suitable for 

the society. 

In the literature, the variables that affect the academic success of students seem to be 

numerous, complex and intertwined. It is seen that in studies centered on variables originating 

from students, academic achievement differences are mostly tried to be explained by 

differences in children. In such studies, gender (Demir, Kılıç & Ünal, 2010; Halpern, 2007; 

Keskin & Sezgin, 2009; Kılıç & Karadeniz, 2004), intelligence and talent (Yıldırım, 2000), 

willingness (Zasacka & Bulkowski, 2017), study habits (Smith & Niemi, 2001), self-efficacy 

and motivation level (Doğru & Ünlü, 2012; Sarıer, 2016; Pajares, 1996), personality traits 

(Sığrı & Gürbüz, 2011), communication styles with friends, teachers or parents (Harding, 2003; 

Huang, 2008; Yılmaz, 2000), anxiety levels in the face of any lesson or exam (Akın, 2008; 



Aslan& Küçüker 

1064 

  
  

Austin & Partridge, 1985; Benjamin, 1991; Birenbaum & Nasser, 1994; Cassady, 2004; Culler 

& Holahan, 1980; Hancock, 2001) and academic achievement are tried to be explained. In the 

literature, it is seen that studies to make sense of academic success through such individual 

variables are dominant and success is generally considered as an individual result depending 

on the child's interest, ability, effort and/or psychological state. There is a lack of connection 

between academic achievement and the education system in which the child is or more 

generally the political and economic system can be seen as a kind of limitation in these studies. 

However, in the studies conducted in recent years, it has begun to be determined in the national 

literature that it is insufficient to explain the academic achievement of students only with their 

individual characteristics (Aslan, 2017; Bozkurt et al., 2021; Bülbül, 2021; Dinçer & Kolaşin, 

2009; Fındık & Kavak, 2013; Köse, 2007; Ünal et al., 2010).  

On the other hand, there are also studies that directly or indirectly associate school variables 

or institutional variables with academic success. An important part of the research in this group 

is about improving the environment and facilities of schools or reducing inequalities between 

schools. It is stated that these studies bring suggestions for making the education system more 

effective over school variables or for the education system at the institutional level; it is seen 

that they focus on access to education and equality of opportunity and opportunity in education 

(Aslan, 2015; Dinçer & Kolaşin, 2009; ERG, 2009; Oral & Mcgivney, 2014; Yolcu & Kurul, 

2009). Therefore, it is possible to consider them as academic achievement studies. Because 

every regulation and improvement related to the school directly or indirectly contributes to the 

better realization of the school's goals and to the increase of academic success. The most 

important contribution of equality of opportunity in education or effective school research to 

the academic achievement literature is the findings that institutional variables related to school 

are more important than individual variables in the success of the child, especially in 

developing countries (Balcı, 2014; Heyneman & Loxley, 1983). Today, school development 

studies, which mainly focus on institutional variables, have been added to these studies. While 

the initial studies that deal with academic achievement in the context of school variables focus 

on the infrastructure opportunities of schools (Lei & Zhao, 2007), today, school improvement 

research has diversified; In addition to the infrastructure facilities of the school, self or 

collective teacher competencies (Bandura, 1993; Shahzad & Naureen, 2017), school culture 

(Bozkurt et al., 2021; Şahin, 2011), climate (Chen & Weikart, 2008), size (Aksu, Güzeller & 

Eser, 2017; Erdoğan & Acar Güvendir, 2019), the leadership characteristics of teachers and 

school administrators (Bozkurt et al., 2021; Fink & Resnick, 2001; Özdemir, 2019) or the 

leadership capacity of the school (Aslan, 2020) is seen to expand to topics such as. However, 

it is seen that studies on how educational policies or institutional arrangements regarding the 

education system affect the academic success of students are insufficient in the national 

literature and analytical studies are needed on this subject.  

Within this wide spectrum of variables affecting academic success, Coleman's emphasis on 

socioeconomic variables through family history in the 1960s caused a new dimension to enter 

the literature. Coleman (1988) found that a child's family background is more crucial for 

academic achievement than individual characteristics. This finding constitutes a dimension 

frequently emphasized by thinkers who draw attention to the reproduction function of 

education. The most emphasized variable by researchers who draw attention to the 

reproductive function of education is the child's family background or social class. According 

to this point of view, the education system performs the elimination function by distributing 

individuals from various socioeconomic levels and social classes to the appropriate education 

types and levels for the social classes they come from. He does this through his diplomas. Thus, 

the education system reproduces the existing class structure (Althusser, 2006; Apple, 2004, 

2006; Aslan, 2017; Bourdieu & Passeron, 2018; 2019; Bowles & Gintis, 2002) and distributes 



International Online Journal of Education and Teaching (IOJET) 2023, 10(2), 1061-1091.  

1065 

 

children to occupations similar to their parents' occupations. In this process, the education 

system screens individuals not according to their natural abilities, but according to their 

abilities that can be developed later, and provides the distribution of individuals to certain 

organizational positions with the diplomas it gives (Ünal, 1996, 2005). As a matter of fact, 

Bourdieu and Passeron (2018; 2019) state that the academic success of the child is the product 

of the social class they come from rather than being the product of their abilities. He argues 

that individuals with strong cultural capital are more fortunate in schools. Because the school 

transmits the culture of the social upper class to a large extent; therefore, individuals who are 

prone to this culture are more likely to be successful. Moreover, individuals with strong cultural 

capital are also likely to have strong economic capital. Especially in academic success; It is 

seen that studies dealing with socio-cultural and socioeconomic variables reveal findings 

consistent with the arguments of Bourdieu and Passeron or Coleman (Giambona & Porcu, 

2015; Kalaycıoğlu, 2015; Kotte, Lietz & Lopez, 2005; Liu, Peng & Luo, 2020; Long & Pang, 

2016; Organization for Economic Co-operation and Development [OECD], 2010; Parcel & 

Dufur, 2001; Schiller, Khmelko & Wang, 2002; Sirin, 2005).  

All of these individual, institutional and social variables that affect academic success are 

also valid for vocational high schools. However, it can be said that variables related to family 

background or socio-cultural and socioeconomic characteristics of families are more decisive 

in vocational high schools. Because it turns out that the children of families with similar 
socio-cultural and socio-economic characteristics are clustered in these high schools. For 
example, in an analysis of 2012 PISA data, it has been determined that the children of families 

in the highest 20% socioeconomically in Turkey are mainly educated in Turkey's best high 

schools (such as Science or Anatolian High Schools). This rate is 51% for Science high schools 

and 42% for Anatolian high schools. On the other hand, 23% of vocational high school students 

come from the lowest 20% slice (Oral & Mcgivney, 2014). This data is collected by the 

education system through certain mechanisms, primarily central examinations, by selecting 

individuals according to their socio-economic and socio-cultural characteristics, and to certain 

institutions; Afterwards, he supports the thesis that he placed in jobs and professions within the 

employment structure. In addition, it is possible to talk about the variables specific to 

vocational high schools regarding academic achievement. Policies regulating the transition of 

vocational high schools to higher education have the potential to affect academic achievement 

through attitudes towards school to the extent that they reduce the demand for these high 

schools. The concentration of students with low academic success rates in these schools may 

adversely affect the perception of students, parents, teachers, school administrators and even 

society towards these schools. Moreover, this situation is not unique to Turkey; a similar 

perception is also mentioned in countries such as Japan, USA and Malaysia. It is stated that in 

the relevant countries, vocational high schools are perceived as secondary schools towards 

which students with low socioeconomic status and academic success tend to, as in Turkey 

(Affero & Razali, 2013; MoNE, 2018a; Tsukamoto, 2016). On the other hand, the 

concentration of students with low academic achievement in the same schools negatively 

affects the school climate, as well as depriving students of successful role models and peer 

learning. In particular, the findings of studies on level classes provide data supporting this 

argument (Aslan, Küçüker, & Gürbüzler, 2014; Palaniappan, 2004; Slavin, 1987; Zimmer, 

2003). 

As can be seen, academic success is a problem that has been examined by many researchers 

in the national or international literature. However, the fact that there are studies based on 

variables that are so different and often disjointed on the subject point out both a difficulty for 

research in this field, the limitations of each research carried out and the need for more research. 

In order to overcome this gap in the literature, albeit to a certain extent, in accordance with the 



Aslan& Küçüker 

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analysis method used in the current study, the variables that are assumed to affect academic 

achievement are discussed in two categories: student level and school level. In the study, school 

level variables, the number of students and teachers of the school; student level variables, on 

the other hand, from the child's gender to study habits; it includes many variables from the 

socio-cultural and socioeconomic characteristics of the parents to the opportunities provided 

to the child or the child's attitude towards school. In the study, student level variables include 

not only the individual characteristics of the child, but also socio-cultural and socioeconomic 

variables related to the family. On the other hand, it does not seem possible to address all 

dimensions of academic achievement, which has the potential to be affected by many variables, 

in one study. As a matter of fact, the mentioned limitation is partially valid for this study as 

well. However, one of the features that make the research different from most studies in the 

national and international literature is its method. Especially in the majority of studies in the 

national literature on academic achievement, the variables that predict academic achievement 

were evaluated with classical regression analysis. In classical regression analyzes, since the 

assumption that the observations are independent from each other is violated, the standard 

errors are calculated smaller than they should be. However, since intergroup independence is 

taken into account for Level 1 (student) and Level 2 (school) variables in hierarchical models, 

the probability of erroneous estimation is minimized compared to single-level models 

(Raudenbush & Byrk, 2002 as cited in Atar & Atar, 2012). However, the second difference is 

that there is no study that directly addresses the academic success of students studying at 

vocational high schools in Turkey, except for national or international central exams. On the 

other hand, it is essential to examine the process of transformation from vocational high schools 

that admitted students through exams in the early 1990s to high schools that are now left at the 

mercy of forced address-based placements, from various perspectives, especially academic 

success. It is hoped that the research will fill the gap in the national literature on vocational 

high schools and guide policy makers or education administrators with analytical data, beyond 

the widely accepted assessments of increasing the academic success rates of these high schools.  

1.1. Purpose of the Research 

The aim of this study is to analyze the academic achievements of vocational high school 

students through student level and school level variables. For this purpose, answers to the 

following questions were sought: 

1. Are there any significant differences between schools in terms of the academic 

achievement of students? 

2. What are the student-level variables that show a significant relationship with students' 

academic success? 

3. What are the school-level variables that show a significant relationship with students' 

academic achievement? 

4. What are the school-level variables associated with students' academic achievement 

when the student-level variables that show a significant relationship with the students' 

academic achievement are added to the model? 

2. Method 

2.1. Research Model 

The research is in the relational screening model as it aims to predict the academic 

achievement of vocational high school students on a two-level variable set, which are student 

at the first level and school at the second level. Studies designed with the relational survey 



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1067 

 

model aim to describe the relationships between two or more variables as they are (Balcı, 2005; 

Karasar, 2005). 

2.2. The Study Group 

The research was carried out with 778 twelfth grade students studying at Anatolian 

Vocational High Schools in five different types (Health Profession, Industry Vocational, Girl 

Vocational, Imam-Hatip and Trade Vocational) in a city center in the Black Sea Region. There 

are two reasons why twelfth grade students are preferred. First, it is desired to use graduation 

data in the evaluation of students' academic success. The second is that all of the twelfth grade 

students have been placed through the Basic Education to Secondary Education (BESE), which 

is a central exam. In the 2017-2018 academic year, the BESE exam was abolished, and both 

central and address-based placement applications were introduced. In the study, students who 

were placed according to similar criteria were evaluated. While determining the schools, five 

different types of vocational high schools, one from each of the vocational high schools in the 

central district, were included in the research. In high school types with more than one school, 

the selection was made randomly. In the study, all of the twelfth grade students in the schools 

that were not sampled, who were at the school on the days of the application and agreed to 

answer the scale, were included in the study. Since the aim of this study is to determine the 

relationships between the variables, it is not aimed to generalize the results to a target 

population. Therefore, the research is an “internal validity” study (Balcı, 2005, p. 79). 

During the data collection process, scales and questionnaires were distributed to 1000 

students; 792 of these scales and questionnaires were returned, but some of them were not 

included in the analysis because they were not filled in properly, and some of them were not 

included in the analysis due to the extreme value examination made before the analysis. The 

research was carried out on the data of 778 participants. 

 

Table 1. Personal characteristics of students and parents 

Personal 

characteristics 

f % Personal 

characteristics 

f % 

Student's Gender   Number of siblings   

Female 429 55,1 Singleton 23 3,0 

Male 349 44,9 2-3 Siblings 484 62,2 

   4-5 Siblings 216 27,8 

   6-10 Siblings 55 7,1 

Mother Education   Father Education   

Illiterate 48 6,2 Illiterate 25 3,2 

Literate 26 3,3 Literate 8 1,0 

Primary school 

graduate 
423 54,4 

Primary school 

graduate 259 33,3 

Secondary school 

graduate 
185 23,8 

Secondary school 

graduate 204 26,2 

High school graduate 81 10,4 High school graduate 215 27,6 

College-faculty 

graduate 
14 1,8 

College-faculty 

graduate 62 8,0 

Master's-PhD graduate 1 0,1 Master's-PhD graduate 5 0,6 

Mom working   Dad working   

Working 105 13,5 Working 641 82,4 

Not working 673 86,5 Not working 137 17,6 

 

 

   

 

  



Aslan& Küçüker 

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Personal 

characteristics 

f % Personal 

characteristics 

f % 

Mother Occupation Father occupation 

Professional 

occupations 4 0,5 

Professional 

occupations 17 2,2 

Officer 20 2,6 Officer 91 11,7 

Worker+Farmer 62 8,0 Worker+Farmer 419 53,9 

Tradesman 11 1,4 Tradesman 189 24,3 

No job 679 87,3 No job 43 5,5 

Other 2 0,3 Other 19 2,4 

Household income   Annual Education 

Expenditure for Child   

2000 TL and below 356 45,8 1000 TL and below 337 43,3 

Between 2001-4000 322 41,4 

Between 1001-2000 

TL 266 34,2 

Between 4001-6000 81 10,4 

Between 2001-3000 

TL 100 12,9 

6001+ 19 2,4 3001+ 75 9,6 

 

2.3. Data Collection 

In the study, two data collection tools were used for the variables related to the student level. 

The data of the study on student level variables were collected through a questionnaire 

consisting of 25 questions developed by the researchers. While developing the questionnaire, 

in addition to the literature review, interviews were conducted with school administrators and 

teachers regarding the variables that may affect students' academic achievement. A 

questionnaire consisting of 25 questions was developed based on the literature review and 

interviews. The developed questionnaire was presented to the expert opinion and some 

questions were arranged in line with the suggestions of the experts. The questionnaire includes 

questions about the students' personal information and study habits, as well as the parents, the 

educational environment and opportunities provided by the parents to the child. 

Students' attitudes towards school were collected using the "Attitudes Towards School Scale 

(ATSS)" developed by Alıcı (2013). ATSS is a 20-item, 5-point Likert-type scale developed 

to determine high school students' attitudes towards school, ranging from "strongly disagree" 

to "strongly agree". The scale shows a three-component structure with a single factor. The scale 

consists of the sub-factors "School as a Barrier to Personal Development" (eight items), 

"School as a Supporter of Personal Development" (eight items) and "School as a Missing 

Entity" (four items). The Cronbach's Alpha reliability for the whole scale was 0.907 and the 

alpha reliability for its sub-factors was 0.871, respectively; it was calculated as 0.813 and 

0.786. These values show that the scale as a whole and its sub-components are consistent within 

themselves. Fit indices of the scale, RMSEA= .056; CFI = .98; GFI = .92; AGFI = .90; RMR 

= .088 (Alıcı, 2013). Cronbach alpha values were calculated as .903 for the whole scale, and 

.846, .867 and .787 for subcomponents, respectively. The goodness of fit values calculated as 

a result of DFA are as follows; χ2= 373.06, sd = 157, χ2/sd= 2.37, RMSEA= .04, GFI= .95, 

AGFI=.94, CFI=.99, and RMR=.040. The data show that the scale is valid and reliable for this 

study as well. 

In the study, the school level variables were obtained through the MEIS forms provided by 

the school administrations. First of all, the school level variables that have the potential to affect 

the academic achievement of the students were examined by scanning the literature. In line 

with the literature, the total number of students and teachers of the school were used as school 

level variables. Graduation averages were included in the analysis as a measure of the academic 

achievement of the participants. Graduation averages of the participants were taken from the 



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1069 

 

schools they graduated from. The participants' mean graduation scores are the dependent 

variable of the study. 

2.4. Data Analysis 

The collected data were coded by the researchers and transferred to the computer 

environment. SPSS 22.0 package program and HLM 8.0 program for Hierarchical Linear 

Model (HLM) were used to organize or analyze the data. The variables in the study show a 

hierarchical structure at the student and school level. Each school and each student has 

characteristics inherent in them and these features may differ. However, students can also be 

affected by the characteristics of the schools they attend. Since students are also affected by 

school level variables in multilevel data, it is not appropriate to use single level models. 

According to Raudenbush and Byrk (2002 cited in Atar and Atar, 2012), analyzes are 

inadequate when linear regression analysis is used for hierarchically structured data because 

common variance is neglected in research. Because when classical linear regression analysis is 

applied to a hierarchical data set, the standard errors are calculated smaller than they should 

be, since the assumption that the observations are independent from each other is violated. This 

causes the estimated regression coefficients to be higher in importance (overestimation). 

However, hierarchical models are also based on regression analysis, but since intergroup 

independence is also taken into account for Level 1 (student) and Level 2 (school) variables in 

these models; compared to single-level models, the probability of erroneous estimation is 

minimized.  

The path followed in the analysis is as follows: 

1. In the study, before the data were analyzed, the data set was examined in terms of missing 

data and extreme values, and necessary corrections were made. 

2. For the purposes of this research, a different hierarchical model was used for each 

purpose. 

a. Accordingly, the One-Way ANOVA with Random Effects was used to test whether 

there is a significant difference between the schools in terms of the graduation scores of the 

students, which is the first sub-purpose of the research. The model is the simplest of the two-

level hierarchical linear models; it does not include predictive variables that explain the 

variance of graduation scores at both the first and second levels. Therefore, it is also called a 

fully unconditional model (Raudenbush & Bryk, 2002 as cited in Atar & Atar, 2012). This 

model estimates how much of the differences observed in graduation scores are due to students' 

individual differences and how much is due to the school environment. 

b. The Random-Coefficients Regression Model was used to determine the student level 

(Level-1) variables that significantly predict the graduation scores of the students, which is the 

second sub-purpose question of the research. In the random coefficients regression model, the 

first level of the model to explain the part of the differences in graduation achievement arising 

from individual differences between students is based on gender, mother's education, father's 

education, family's social security, pre-school education status of the child, daily study habits, 

average annual income of the family and annual children. The child's attitude towards school 

was added as well as the variables of education expenditure for among these variables, the 

continuously variable ones were included in the model by centered on their general mean 

(grand mean centering). 

c. The third sub-objective of the study is to determine the school-level variables (Level-

2) that significantly predict students' graduation scores. For this purpose, the Means as 

Outcomes Regression Model was used. As the school level variables, the total number of 

students and the total number of teachers were used. The reason why school level variables are 

limited is the low number of schools. 



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d. The fourth sub-purpose question of this study is “What are the school-level variables 

related to the academic achievement of students when the student-level variables that have a 

significant relationship with the academic achievement of the students are added to the model?” 

is the question. In order to answer this question, the Intercepts-and-Slopes-as Outcomes Model 

was used. This model, which includes student (Level-1) and school (Level-2) variables 

together, is called the full model (Raudenbush & Byrk, 2002 as cited in Atar & Atar, 2012). 

3. The assumptions about multiple connections and models were tested for the data set 

before the analyzes were performed, and it was determined that the assumptions were met. 

3. Results 

3.1. Findings Regarding the Determination of Differences Between Schools in terms of 

Graduation Scores of Students 

The first sub-purpose question of the research is about whether the academic achievement 

of students differs between schools. To answer this question, the One-Way ANOVA with 

Random Effects, one of the Hierarchical Linear Models, was used. In the equation in which 

the graduation success (M𝑖𝑗= graduation) of student i at school J is estimated, 𝛽0𝑗 is interpreted 
as the graduation success average of school j, and 𝑟𝑖𝑗 is interpreted as the difference between 
the graduation success of student i at school j from the graduation success average of school j. 

𝛽0𝑗, called the intersection coefficient, is the constant parameter in the model, and, the first-
order error term, is the random parameter in the model. It is assumed that this parameter has a 

normal distribution, its mean is 0 and its variance is σ2. In the equation where the intercept 

coefficient (β0j) at the first level of the model is considered as the dependent variable, γ00, is 

the general graduation success average, and u0j is interpreted as the difference between the 

graduation success average of school j and the general graduation average. It is assumed that 

the u0j parameter, also called the second-order error term, has a normal distribution, its mean 

is 0 and its variance is τ00 (Atar, 2014). Accordingly, the following models were established. 

The estimation of fixed effects as a result of One-Way ANOVA for the established model is 

given in Table 2.  

Level-1 Model  (Mij) = β0j + rij  

Level-2 Model  β0j = γ00 + u0j  

Mixed Model  (Mij) = γ00 + u0j + rij 

 

Table 2. The estimation of constant effects obtained from the random effect one-way ANOVA 

model 

Fixed Effect Constant SH t 
For Coefficient β0j, 

Average School Mean, γ00 
70.97 3.31 21.47* 

* p<.05  

When Table 2 is examined, the difference between school averages in terms of graduation 

averages differs significantly from zero. In other words, it is seen that the averages of the 

schools included in the analysis differ significantly from each other (t = 21.47, p <.05). 

Accordingly, when a 95% confidence interval is established around the overall graduation 

success average, it can be said that the real value of the overall graduation success average in 

the research province is in the range of 70.97 ± (1.96) (3.31) = 64.48 - 77.46 points with 95% 

probability. The estimation of the variance components of the Random Effect One Way 



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ANOVA Model, which was made in line with the first sub-purpose of the research, is given in 

Table 3. 

Table 3. The estimation of variance components of the random effect one-way ANOVA 

model 

 
Standard 

Deviation 

Variance 

components 
2 s.d. 

Explained 

% 

School average, u0j 7.36 54.22 531.558* 4 .51 

Level-1 Effect, rij 7.23 52.24    
* p<.05 

According to Table 3, the variance between the schools' graduation averages is 
estimated to be 54.22. At the student level, the variance of the graduation scores of the 
students within the framework of the school average was calculated as 52.24. The fact that the 

variability between schools is statistically significant (p<.05, sd=4) shows that there are 

significant differences between the average graduation achievements of vocational high 

schools in the research province. Possible value range for school averages is 70.97 ± (1.96) 

(7.36), in other words, school averages are between 56.54 and 85.40 points with 95% 

probability. According to this result, it can be said that the graduation scores among the schools 

in the sample are in a wide range. 

In order to determine how much of the variance in the graduation scores of the students is 

due to the difference between schools, the τ00 (variance component) estimates of the model 

for both levels are compared and the explained variance ratio index is obtained (Hox, as cited 

in 1995, Aksu, Güzeller & Eser, 2017). Accordingly, the explained variance value in β0j is 

calculated using the equation below. 

Considering the variance values in Table 2, the variance values explained for the graduation 

achievement indicator were obtained. 51% (54.22/54.22+52.24) of the differences observed in 

graduation scores are due to the difference in average graduation scores between schools. In 

other words, the differences in the graduation scores of vocational high school students are due 

to school variables with a probability of 51% and variables at the student level with a 

probability of 49%. This differentiation between schools in terms of graduation scores is 

coincidental (x2 =531.558, sd=4, p<.05). 

Another finding obtained from the analysis results of the one-way analysis of variance 

random effects model is the estimation of the graduation mean (𝛽0𝑗) reliability coefficient 
(r=0.99). These values show that the sample mean used in the analysis predicts the actual 

school averages very reliably. 

3.2.  Examination of Student Level Variables Related to Students' Academic 
Achievement 

The Random Coefficient Model was used in the study to determine the student level 

variables that affect the academic success of vocational high school students. The model is 

established with first-order variables and is considered as a simple linear regression model. 

Accordingly, gender, number of siblings, number of siblings going to school, mother-father 

union, mother working, father working, mother education, father education, mother 

occupation, father occupation, getting preschool education, going to school course, going to 

study/classroom, studying. hours, extracurricular reading habits, leisure use, computer at home, 

internet at home, number of extracurricular books at home, presence of study room, ownership 

of the house, heating style of the house, household income, education expenditure for the child, 

social security, school adjustment variables Level. It is included in the model as -1 variables. 

The model established with these variables is given below. 



Aslan& Küçüker 

1072 

  
  

 

Level-1: 

(Yij|OPV) = β0j + β1j(Genderij) + β2j(Number of siblingsij) + β3j(Number of siblings 

going to schoolij) + β4j(Parent unionij) + β5j(Mom workingij) + β6j(Dad workingij) + 

β7j(Mom trainingij) + β8j(Dad trainingij) + β9j(Mother occupationij) + β10j(Father occupation 

ij) + β11j(Preschool educationij) + β12j(Go to school courseij) + β13j(Going to 

study/classroomij) + β14j(Study hoursij) + β15j(Extracurricular reading habitij) + β16j(Leisure 

useij) + β17j(Computer at homeij) + β18j(Internet at homeij) + β19j(Number of extracurricular 

books at homeij) + β20j(Presence of study roomij) + β21j(Ownership of the houseij) + 

β22j(How the house is heatedij) + β23j(Household incomeij) + β24j(Education expenditure for 

the childij) + β25j(Social securityij) + β26j(Attitude towards schoolij) + rij  

 

Level-2: 

β0j = γ00 + u0j 

β1j = γ10 + u1j 

β26j = γ260 + u26j 

 

Mixed Model:  

Yij|OPV) = γ00 + u0j + (γ10 + u1j)(Gender) + (γ20 + u2j)(Number of siblings) + (γ30 + 

u3j)(Number of siblings going to school) + (γ40 + u4j)(Parent union) + (γ50 + u5j)(Mom 

working) + (γ60 + u6j)(Dad working) + (γ70 + u7j)(Mom training) + (γ80 + u8j)(Dad training) 

+ (γ90 + u9j)(Mother occupation) + (γ100 + u10j)(Father occupation) + (γ110 + 

u11j)(Preschool education) + (γ120 + u12j)(Go to school course) + (γ130 + u13j)(Going to 

study/classroom) + (γ140 + u14j)(Study hours) + (γ150 + u15j)(Extracurricular reading habit) 

+ (γ160 + u16j)(Leisure use) + (γ170 + u17j)(Computer at home) + (γ180 + u18j)(Internet at 

home) + (γ190 + u19j)(Number of extracurricular books at home) + (γ200 + u20j)(Presence of 

study room) + (γ210 + u21j)(Ownership of the house) + (γ220 + u22j)(How the house is heated) 

+ (γ230 + u23j)(Household incomeij) + (γ240 + u24j)(Education expenditure for the child) + 

(γ250 + u25j)(Social security) + (γ260 + u26j)(Attitude towards school) + rij 

 

In order to examine the significance of the model established with the inclusion of Level-1 

variables, the method based on the test of the significance of the Deviance value was used (as 

cited in Karakoç Alatlı, 2020 from Garson, 2013). Accordingly, the Level-1 model was 

determined to be statistically significant ( 2=466.261, p<0.05). Table 4 presents the results of 

the analysis conducted to determine whether student variables related to graduation success are 

a significant predictor of graduation success. 

  



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Table 4. The estimation of constant effects related to the level-1 random coefficient model 

Cluster Constant effects Coefficient

s 

Standa

rd 

error 

t-ratio s.d. 

Demograph

ic features 

Overall achievement, γ00 66.49 3.11 21.38* 

747 

Gender, γ10 1.89 0.57 3.30* 

Number of siblings, γ20 -0.15 0.22 -0.68 

Number of siblings going to 

school, γ30 

0.52 0.26 2.01* 

Parent union, γ40 -0.56 0.78 -0.72 

Socio-

cultural 

level 

Mom working, γ50 -0.79 1.34 -0.59 

747 

Dad working, γ60 -1.26 0.71 -1.79 

Mom training, γ70 -0.04 0.10 -0.37 

Dad training, γ80 0.28 0.09 3.34* 

Mother occupation, γ90 0.42 1.35 0.31 

Father occupation, γ100 -0.27 0.78 -0.35 

Educational 

support 

Preschool education, γ110 0.14 0.52 0.26 

747 
Go to school course, γ120 2.76 0.63 4.41* 

Going to study/classroom, 

γ130 

2.53 0.61 4.16* 

Study habit Study hours, γ140 2.71 0.66 4.13* 

747 
Extracurricular reading 

habit, γ150 

-1.03 0.59 -1.74 

Leisure use, γ160 0.91 0.52 1.77 

Opportuniti

es for the 

child 

Computer at home, γ170 0.92 0.58 1.57 

747 

Internet at home, γ180 0.48 0.60 0.81 

Number of extracurricular 

books at home, γ190 

1.52 0.57 2.68* 

Presence of study room, γ200 -1.07 0.58 -1.8 

Economic 

indicators 

Ownership of the house, γ210 0.36 0.60 0.60 

747 

How the house is heated, γ220 -0.81 0.61 -1.34 

Household income,γ230 -0.82 0.58 -1.41 

Education expenditure for 

the child,γ240 

1.38 0.63 2.17* 

Social security,γ250 2.04 0.71 2.85* 

Attitude Attitude towards school, γ260 0.49 0.36 1.38 747 
* p<.05  

When Table 4 is examined, variables such as gender, number of siblings going to school, 

father's education level, going to school, going to study center/classroom, daily study hours, 

number of extracurricular books at home, education expenditure for the child and social 

security are significant predictors of graduation success was determined as (p<.05). 

Accordingly, the graduation scores of female students are 1.89 points higher than male 

students. A one-unit increase in the number of siblings going to school is 0.52 in the academic 

success of the student; one-year increase in father's education level, 0.28; child attending a 

school course 2.76; Going to study center/classroom caused an increase of 2.53 points. On the 

other hand, the academic success of students who state that they study for one or more hours a 

day is 2.71 points higher than those who state that they do not study at all. At home, 

extracurricular novels, poems, stories, etc. The academic achievement of those who have 25 or 

more books is 1.52 points higher than the others. In the set of economic indicators, two 



Aslan& Küçüker 

1074 

  
  

variables were found to be significant. The annual education expenditure for the child is 1.38 

points above the academic achievement of those who are above the average of the research 

group; It is seen that children who have social security in their household due to their mother 

or father are 2.04 points higher than those who do not.  

On the other hand, one-unit increase in the number of siblings (-0.15 points), separation of 

parents (-0.56 points), mother being a housewife (-0.79 points), father not working (-1.26 

points), one-year increase in mother's education level (-0. ,04 points), the father being a farmer 

(-0.27 points), the child not having the habit of reading extracurricular books (-1.03 points), 

not having a study room (-1.07 points), heating the house with a stove (-0.81 points), minimum 

household income Although variables such as being below the wage (-0.82 points) were not 

significant at the p<.05 level, they caused a decrease in the academic achievement of the 

student. The mother has a profession (0.42 points), the child has pre-school education (0.14 

points), does not spend daily time on a computer/internet/tablet/smart phone or television for 

entertainment (0.91 points), has a computer at home (0.92 points), does not have internet access 

at home. Variables such as having a high school education (0.48 points), not renting a house 

(0.36 points) and having a high level of school adjustment (0.49 points) contributed to the 

increase in academic achievement, although they were not significant at the p<.05 level. The 

findings regarding the estimations of the variance components in the model established for the 

student-level variables that predict the graduation success of the students are given in Table 5.  

Table 5. The estimated variance components for level-1 random coefficient model 

Coincidental effects Standard 

Deviation 

Variance 

Component 

s.d. 2 

School average, u0j 6.16 37.94 
4 

466.26* 

Level-1 effect, rij 6.66 44.36  
* p<.05  

Table 5 presents the results of the test of the variance of graduation achievement scores as 

a result of adding Level-1 variables and the significance of the random effect of variance in 

terms of school level. Accordingly, the variance ratio index explained can be obtained by 

comparing the variance values estimated with the two models for graduation success. The 

variance rate explained at Level-1 (52.24-44.36 / 52.24) is 15.08%. Accordingly, 

approximately 15.08% of the student-level variance in graduation success is explained by the 

student-level variables given above. The reliability of the model established at the student level 

was estimated to be approximately 0.99. Accordingly, it can be said that the estimation is quite 

reliable. 

3.3. Examination of School Variables Related to Academic Achievement of Students 

The third sub-objective of the study is to determine the school-level variables (Level-2) that 

significantly predict students' graduation scores. For this purpose, the Means as Outcomes 

Regression Model was used. The school variables addressed for the graduation success of the 

students are "number of students in schools" and "number of teachers in schools". 

Level-1: 

(Yij|OPV) = β0j + rij  

Level-2: 

β0j = γ00 + γ01 (Number of students) + γ01 (Number of teachers) + u0j  

 



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Mixed Model:  

(Yij|OPV) = γ00 + γ01 (Number of students) + γ01 (Number of teachers) + u0j + rij 

The analysis results obtained from the model established for the school level are given in 

Table 6. 

Table 6. The estimation of constant effects for level-2 random coefficients model 

Constant effects Coefficients Standard 

Error 

t-ratio s.d. 

Overall achievement average, 

γ00 70.97 2.68 26.44* 
2 

Number of students, γ01 -0.03 0.02 -1.82* 

Number of teachers,γ01 0.32 0.33 0.99 
* p<.05  

According to Table 6, it was determined that among the school level variables, the number 

of students in schools significantly predicted the graduation success of students (𝑡=-1.82, 
p<.05). An increase of one standard deviation in the total number of students in the school 

causes a decrease of 0.03 points in the graduation scores of the students. On the other hand, it 

was determined that the number of teachers variable was not a significant predictor of 

graduation success (𝑡=0.99, p>.05). 

Table 7. Estimation of variance components for level-2 random coefficients model 

Coincidental effects Standard 

Deviation 

Variance 

Components 

s.d. 2 

School average, u0j 5.97 35.66 
2 171.91 

Level-1 average, rij 7.23 54.22 
* p<.05  

In Table 7, the variance of students' graduation scores was calculated as 35.66 by adding 

Level-2 variables to the model. In the study, the variance rate index was obtained by comparing 

the variance values estimated with the two models for the average school graduation success. 

The variance ratio explained in Level-2 [(54.22-35.66)/54.22] was calculated as 0.34. 

Accordingly, 34% of the variance between schools in graduation success is explained by Level-

2 variables. After controlling the mean Level-2 variables of graduation achievement, the ratio 

of variance between schools [35.66/(35.66+52.24)] was calculated as 0.40. After controlling 

for the mean school variables, approximately 40% of the variance in graduation success is 

between schools, and this differentiation between schools is coincidental ( 2=171.91, sd=2, 

p<0.05). 

In the analysis of the mean model as the dependent variable, the reliability for the school 

graduation average was estimated to be approximately 98%. 

3.4. Investigation of the School and Student Variables together Associated with 

Academic Success 

When student-level variables that show a significant relationship with students' academic 

achievement are added to the model, the Intercepts-and-Slopes-as Outcomes Model was 

established to determine the school level variables related to students' academic achievement. 

 

 

 



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1076 

  
  

Level-1: 

(Yij|OPV) = β0j + β1j(Gender) + β2j(Number of siblings going to school) β3j(Dad training) 

+ β4j(Go to school course) + β5j(Going to study/classroom) + β6j(Study hours) + β7j(Number 

of extracurricular books at home) + β8j(Education expenditure for the child) + β9j(Social 

security) + rij  

Level-2: 

β0j = γ00 + γ01 (Number of students) + u0j  

β1j = γ10 + γ11 (Number of teachers)+u1j 

.... 

…. 

Β9j =γ90+γ91 (Number of students)+ u9j 

Mixed Model:  

(Yij|OPV) = γ00 + γ01(Number of studentsij) + γ10(Genderij) + γ20(Number of siblings 

going to schoolij) + γ30(Dad trainingij) + γ40(Go to school courseij) + γ50(Going to 

study/classroomij) + γ60(Study hoursij) + γ70(Number of extracurricular books at homeij) + 

γ80(Education expenditure for the childij) + γ90(Social securityij) + u0j +rij 

The analysis results regarding the estimation of constant effects for these models are given 

in Table 8. 

Table 8. Examination of school and student variables associated with graduation scores 

Constant effects Coefficients Standard 

Error 

t-ratio s.d. 

Overall achievement average, γ00 63.61 2.52 25.23 

764 

Number of students, γ01 -0.01 0.01 -1.71 

Gender, γ10 1.71 0.56 3.03* 

Father education, γ20 0.20 0.07 2.75* 

Social security, γ30 1.30 0.68 1.90* 

Number of siblings going to 

school, γ40 0.47 0.23 2.02* 

Going to school course, γ50 2.95 0.62 4.77* 

Going to study/classroom, γ60 2.31 0.59 3.88* 

Study hours, γ70 2.72 0.63 4.31* 

Number of extracurricular books 

at home,γ80 1.37 0.55 2.48* 

Educational expenditure for the 

child, γ90 1.27 0.63 2.03* 
* p<.05  

In the estimation of the fixed effects of the full model in Table 8, all first and second level 

variables, which are a significant predictor of graduation success, were included in the analysis. 

Accordingly, the variable of the total number of students of the school is not a significant 

predictor of the graduation success of the students at the school level (t=-1.71, p>.05). When 

the results of the analysis are examined in terms of student variables, it is found that the 

variables of gender, father's education, social security, number of siblings going to school, 

attending school courses, going to study/classroom, study hours, number of extracurricular 

books at home, education expenditure for the child are significant predictors of students' 



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1077 

 

graduation scores. observed (p<.05). The analysis results for the estimation of the variance 

components for the full model are given in Table 9. 

Table 9. The Estimation of variance components related to the full model 

Coincidental effect Standard 

Deviation 

Variance 

components 

s.d. 2 

School average, u0j 5.16 26.61 
3 249.31 

Level-1 effect, rij 6.71 44.99 
* p<.05  

It is seen in Table 9 that the variance of the graduation scores of the schools is estimated as 

26.61 according to the full model. According to the full model obtained by including student 

and school variables in the model, the difference between graduation scores was calculated as 

approximately 37% [26.61/(26.61+44.99)]. Accordingly, as a result of including both level 

variables in the model, there is a decrease of approximately 14% in the explained variance 

value. The reliability of the full model was estimated to be approximately 98%. 

4. Discussion, Conclusion and Suggestions 

When the demographic changes related to the students within the scope of the research are 

examined, it is seen that the socio-cultural and socioeconomic capital of the families of the 

students is low. The majority of the students come from the lower socioeconomic income 

group. A significant portion of them (45.8%) live below the poverty line. In October 2018, the 

poverty line is 6.252 TL (Confederation of Turkish Trade Unions [TÜRK-İŞ], 2018). The 

average size of the family in the research is five people, because the number of siblings of 

students varies between two and ten (average 3). This is larger than the average family size in 

Turkey (average 2) (Turkish Statistical Institute [TUIK], 2016). The majority of mothers are 

housewives, and the majority of fathers are farmers or workers. There are hardly any mothers 

or fathers in professional occupations (0.5% for mothers, 2.2% for fathers). The education level 

of the parents is below the Turkey average. According to the results of the 2017 address-based 

census, the average education period of the population aged six and over is 7.8 years. This 

period is 7.1 years for women and 8.2 years for men. The average education period of the 

students within the scope of the research is 6.0 years for mothers and 8.0 years for fathers. 

Vocational high school students are widely accepted to be unsuccessful in terms of intelligence 

and ability levels. The education level of their parents is lower than the average of Turkey, the 

number of siblings is above the average, they have a relatively large family structure and the 

overwhelming majorities have to cope with poverty. The social and cultural capital of 

vocational high school students does not support their academic success. The findings of other 

studies on academic success (ERG, 2009; 2010; MoNE, 2013; Oral & Mcgivney, 2014) are in 

line with the findings of this study on students' family profiles. 

The first sub-objective of the study is about whether students' academic achievement differs 

between schools. In the study, the academic achievement of the students showed a significant 

difference according to the schools. Differences in achievement among secondary education 

institutions in Turkey is a frequently expressed problem (Atar, 2014; Berberoğlu & Kalender, 

2005; Bakış, Levent, İnsel & Polat, 2009; MoNE, 2010). Both the international level exams 

(PISA, TIMMS, etc.) and the data on transition to higher education held at the central level 

regarding the achievement differences between schools at the secondary education level 

support this finding. For example, in a study conducted by Karakoç Alatlı (2020) with 2015 

PISA data, it was found that the science literacy scores of students differ according to schools 

and the success differences between schools in Turkey are higher than in Singapore, which is 

compared. When the results of the Higher Education Exam Placement (YKS) for 2020 are 

examined by school types; It is seen that the ratio of the graduates of official general high 



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1078 

  
  

schools (who teach in a foreign language) among the total undergraduate programs is 23.1%, 

and the ratio of those who graduate from vocational high schools, excluding imam-hatip high 

schools, is 5.3%. On the other hand, this rate is 54.5% in social sciences high schools, 49.4% 

in science high schools, 27.9% in official Anatolian high schools teaching foreign languages, 

16.4% in imam-hatip high schools, 3.5% in trade vocational high schools, and technical high 

schools. 5.5% in industrial vocational high schools and 2.7% in sports high schools (Higher 

Education Information Management System, 2020). The results of placement in higher 

education institutions in 2020 are consistent with the research finding. On the other hand, in an 

analysis made according to 2012 PISA results in Turkey, it was found that 61% of the variance 

in students' mathematics scores was due to differences between schools (Anıl, Özer Özkan, & 

Demir, 2012). 

In the research, the two variables that have the highest impact on the academic success of 

the child are the child's attendance to school courses and to the study center/classroom. In fact, 

both variables mean that students receive additional lessons outside of standard school hours. 

In Turkey, private teaching institutions have been closed since 2015, and weekend courses 

have been opened in study centers and schools. There are many studies that show that private 

teaching institutions or study centers make a significant difference in the academic success of 

students and thus increase academic success (Akbaba-Altun & Çakan, 2008; Aslan, 2017; 

Morgil, Yılmaz, Seçken & Erökten, 2000; Morgil, Yılmaz & Geban, 2001; Tansel, 2013). For 

example, in a study conducted by Başol and Zabun (2014), it was determined that among the 

students who took the High School Placement Exams (HSPE), which determines the transition 

to secondary education institutions, those who went to a private teaching institution or study 

center had a significantly higher success score than those who did not. A similar result is also 

found in Aslan's (2017) research. In the study evaluating the success of the Transition from 

Basic Education to Secondary Education (BESE) exam, it was found that the academic success 

of the students who went to private teaching institutions or study centers was significantly 

higher than those who did not, and contrary to the current research, there was no significant 

difference in the BESE exam success of the students who went to the weekend school courses. 

In this study, the effect of preschool education in the context of the educational support 

provided to the child was also examined. Contrary to the widespread acceptance regarding the 

positive effect of preschool education on academic achievement (ERG, 2009), preschool 

education did not make a significant difference on academic achievement in the study. 

In academic achievement studies, the child's study habits are often one of the variables taken 

into account. In this study, a significant difference in graduation success was found between 

students who study for one or more hours a day and those who do not have the habit of studying 

daily. Regular study habits are one of the student-level variables that explain the academic 

success of students. There is research supporting this finding. For example, in a study 

conducted by Ozan (2011) the relationship between the study habits of 6th, 7th and 8th grade 

primary school students and their success in Science and Technology course was examined. A 

moderately significant relationship was found between the academic success grades of the 

students in the Science and Technology course and their study habits. Similarly, Bıyıklı (2017) 

found a moderate and positive relationship in his study examining the study habits of secondary 

school students. There are other studies that show that planned study habits predict or positively 

affect students' academic success (Acar Güvendir, 2014; Anıl & Özer, 2011; Kara & Gelbal, 

2013; Öksüzler & Sürekçi, 2010). In this study, while the study habits of the students affected 

their academic success, no significant relationship was found between the time they set aside 

for entertainment and their extracurricular reading habits and their academic success. 



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In the research, the relationship between genders, number of siblings, number of siblings 

going to school and academic achievement was also examined. Among these variables, there 

was a significant relationship between gender and the number of siblings going to school and 

the academic success of the students, but no relationship was found between the number of 

siblings and academic success. In the study, it was determined that female students were more 

successful than males. This difference in success may be due to the social gender perception 

of families. Because when the education, working status and occupation of the parents of the 

students are examined, it is seen that a significant part of them has rural origin, patriarchal and 

conservative family structure. Even if they live in the city center, sending girls to secondary 

education is still a serious problem in families who cannot break their connection with the 

countryside (Aslan, 2021). This is especially true for girls sent to school; it could mean either 

doing well in school or getting married and/or being expelled from school at an early age. Girls 

living in patriarchal families, especially in rural areas, may not be given the chance to fail as 

much as boys. The limited options available for girls and the fact that girls realize this at a very 

early age may cause them to make more efforts to be successful. As a matter of fact, there are 

other studies showing that girls are more successful than boys. (Büyüköztürk & Denizkulu, 

2002; Chiu & McBride Chang, 2006; Giambona & Porcu, 2015; Halpern, 2007; Koç, 

Avşaroğlu & Sezer, 2004; Lynn & Mikk, 2009; Pomerantz, Altermatt & Saxon, 2002). On the 

other hand, there are studies that show that there is no difference between male and female 

students in terms of academic achievement (Sadi, Uyar, & Yalçın, 2014) or that male students 

are more successful (Demir, Kılıç & Ünal, 2010; Türkan, Üner & Alıcı, 2015). 

According to the results of the analysis, the number of siblings is not a significant variable 

in the academic success of the students; however, there are studies that have determined that 

an increase in the number of siblings or children in the household has a negative effect on the 

child's access to school and academic success (Bıkmaz, 2001). As a matter of fact, although 

there was no significant difference in this study, it was determined that the academic success 

of the children decreased as the number of siblings increased. However, it is seen that the 

academic success of children increases as the number of siblings going to school increases. 

This result may be due to two reasons. First, as the number of children going to school 

increases, parents' experience with school may be increasing. Secondly, the academic success 

of students may increase depending on the educational interactions between children. There 

are other studies that support the data of the current research that academic success increases 

especially as the number of children going to school increases (Aslan, 2017; Öksüzler & 

Sürekçi, 2010). 

Another variable that affects the academic achievement of students is the social and 

economic status of the family. In the research, the relationship between the working statuses 

of the parents, their education level and occupation and the academic achievement of the 

students were discussed. Among these variables, only father's education level was found to be 

associated with the academic achievement of vocational high school students. On the other 

hand, no significant relationship was found between the education level of the mothers and the 

academic success of the children. Two reasons may have been effective here. The first of these 

is that the education level of the mothers is lower than that of the fathers; and the second, also 

related to the first reason, may be that the education level of the mothers is insufficient to 

support the students due to the fact that the students are at the secondary education level. As a 

matter of fact, the fact that the education level of mothers is lower than that of fathers and 

Turkey's average supports this finding. On the other hand, in many studies conducted on 

international exam success, data regarding the educational level of parents positively affect the 

academic success of the child (Alomar, 2006; Anıl, 2008; Anıl, Özer Özkan, & Demir, 2012; 

Aslan, 2017; İnce, 2016; Kuyper, Van der Werf, & Lubbers, 2000; Lemke et al., 2002; Long 



Aslan& Küçüker 

1080 

  
  

& Pang, 2016; Bouhlila, 2017). In a comprehensive study conducted by Rinderman and Ceci 

(2018) on 15,297 children in the United States, Austria, Germany, Costa Rica, Ecuador, 

Vietnam and Brazil; In the cognitive development of children aged 4-22, the economic status 

of the families (income, assets) and the education level of the parents were investigated. In the 

study, it was concluded that the education level of the parents is more important than the 

financial wealth of the family in student success. 

Another variable to be evaluated in relation to the socioeconomic status of the family is the 

working status and occupation of the parents. In the study, no significant relationship was found 

between both variables and academic achievement. Aslan's (2017) study evaluating the success 

of the Transition from Basic Education to Secondary Education (BESE) exam partially 

supports this finding. In the related research, while no significant relationship could be found 

between the working status of the father and academic success, it was determined that the exam 

success of children whose mothers were working was higher. In the same study, a significant 

relationship was found between the profession of parents and academic achievement. 

Especially the academic success of the students whose parents are public employees is 

significantly higher than the others. Similarly, in another study conducted with the PISA 2009 

data of fourteen countries, it was determined that the education level and professional status of 

the parents are more important in student success than the financial wealth of the family (Xie 

& Ma, 2019). On the other hand, there are also studies that have determined that the profession 

of parents has no effect on academic achievement. For example, in a study conducted by 

DeGarmo, Forgatch, and Martinez (1999) with 238 divorced mothers with six-nine-year-old 

boys, it was found that mothers' occupation and income had no effect on children's academic 

success. 

Some of the student level variables in the research are also related to the opportunities 

provided to the child. The effects of variables such as having a computer and internet at home, 

the number of extracurricular books and the child's own study room were also examined. These 

variables are also related to the socio-cultural and socioeconomic levels of the households. Of 

these variables, only a significant relationship was found with the number of extracurricular 

books at home. In the research, poems, novels, stories, dictionaries, etc. at home, this can be 

considered as one of the indicators of the socio-cultural capital of the families. The number of 

extracurricular books was determined as one of the variables explaining the academic success 

of the students. There are other studies that support this finding (Giambona & Porcu, 2015; 

Gülleroğlu et al., 2014; Kaya, 2017; Türkan, Üner, & Alıcı, 2015; Zasacka & Bulkowski, 

2017). For example, Anıl and Özer (2011) found in a study they conducted with PISA 2006 

Turkey data that the variable that most predicted Science and Mathematics achievement was 

the "time spent learning", and the second place was the extracurricular "literary" works at 

home. Both results determined by the researchers are consistent with these research findings. 

On the other hand, there is no significant relationship between having a computer, internet 

and a study room at home and academic success. A similar conclusion was reached in the 

research of Acar Güvendir (2017). In the study in which PISA 2012 data were analyzed in the 

context of Mathematics achievement; No significant relationship was found between the 

student's own room, an environment suitable for working at home, and an internet connection 

and mathematics achievement. However, there are studies showing that having a computer, 

internet connection and a child's own study room predicts students' academic success or 

positively affects academic success (Dinçer & Kolaşin, 2009; Erdoğdu & Erdoğdu, 2015; 

Türkan, Üner & Alıcı, 2015). For example, in a study conducted by Kaya and Doğan (2017) 

with PISA 2012 data of 23,710 students from four countries (United States, Turkey, Finland, 

Israel); in all four countries, a significant relationship was found between the students' science 



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1081 

 

achievement and the education level of their parents, having a computer at home, using 

educational software, having world classics, poetry books and auxiliary books at home, the 

number of telephones, computers and books at home. 

In this study, while some of the variables at the student level are related to the personal 

characteristics of the students such as gender, number of siblings, and study habits, an 

important part is related to the socio-cultural and socioeconomic characteristics of the child's 

family. There are also studies showing that these characteristics are more effective than 

personal characteristics in academic success (Coleman, 1988; Sirin, 2005). In the study, in the 

context of socioeconomic variables; the ownership of the house where the child lives, heating 

status, and household income, annual education expenditure for the child and whether the child 

has a social security from his mother or father were examined. Two of these variables, the 

annual education expenditure for the child and the child's social security, made a significant 

difference in academic achievement. No significant relationship was found between ownership 

of the house, heating status, household income and academic achievement. There are studies 

that show that household income makes a significant difference in academic achievement and 

access to school (Aslan, 2017; Öksüzler & Sürekçi, 2010). As in the current research, there are 

also studies showing that there is no effect of household income or a weak relationship. A study 

was conducted by Turmo (2004) to determine the factors affecting the science achievement of 

students in Scandinavian countries. For the purpose of the research, PISA 2000 data were 

analyzed and it was concluded that there was a weak relationship between household income 

and student's science achievement. However, it was found that there is a strong relationship 

between the cultural wealth of the family and the student's science achievement. In the current 

study, the fact that household income does not make a significant difference in the academic 

success of the child may be related to the fact that the income levels of the families of the 

students are homogeneous and low in general, and that almost all of them live below the 

poverty line. 

While household income does not make a significant difference in the research, annual 

education expenditure for the child is one of the variables that explain academic success. The 

academic achievement of the children of the families whose annual education expenditure for 

the child is above the group average was found to be significantly higher than those of the 

lower ones. There are other studies showing that annual education expenditure for children 

positively affects academic achievement (Abbott & Fouts, 2003; Aslan, 2017). For example, 

one of the comprehensive studies examining the effect of socioeconomic factors was conducted 

by Sirin (2005). The study sample, which analyzed studies conducted between 1990 and 
2000 through meta-analysis, consists of 101,157 students and 6871 schools. As a result 
of the study, a strong relationship was found between the socioeconomic status of the students' 

families and student achievement. 

In the study, it was determined that the child's social security is one of the variables 

explaining academic success. For the secondary education level, this is a very significant and 

consistent finding with other data. Because the absence of social security in the household can 

be considered as an indicator of the child's ability to work outside of school and the level of 

household poverty. Both situations are negative in terms of education. As a matter of fact, a 

study conducted by ERG (2009) revealed that social security is especially important for boys. 

According to this research, the probability of participation in education increases by 20% for 

boys with social security in their households. Similarly, in Aslan's (2017) research, having 

social security in the household made a significant difference in the success of children in 

transition to secondary education. On the other hand, although socioeconomic background has 

important effects on success, according to PISA reports, there may be high achievers among 



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students with low socioeconomic status. It can be said that countries with a low socioeconomic 

level and successful achieve equality in education significantly (Fındık & Kavak, 2013).  

Another student level variable that is expected to show a significant relationship with 

students' academic success is their attitude scores towards school. Although the academic 

achievement of the students increases as the students' attitude scores towards school increase, 

attitude towards school is not one of the predictors of academic success in this study. This 

finding is consistent with the findings of the research conducted by Atik in 2016. In the study 

conducted with 8,130 high school students studying in 11 Anatolian high schools, it was 

determined that students' attitudes towards school did not directly affect their academic 

achievement, but indirectly affected academic achievement through the variables of alienation 

from school and school burnout. There are also studies that detect a relationship between 

attitude towards school and academic achievement (Mombourquette, 2007). 

In the study, the number of students and teachers of the school were considered as school 

level variables. While the number of students in the school is a significant predictor of 

academic success, no significant relationship was found between the number of teachers and 

academic success. This may be related to the norm staff calculations according to the schools. 

The fact that all of the schools are in the center and the number of teachers is determined 

according to the norm staff over the course load may have caused a significant difference 

between the schools. On the other hand, an increase of one standard deviation in the number 

of students in the school causes a decrease of 0.03 points in the academic achievement of the 

students. A study supporting this finding was conducted by Egalite and Kisida (2016). In their 

study, in which the researchers examined over one million student groups in the USA over 

longitudinal data, they found that as the size of the school increased, the academic achievement 

of students in Mathematics and Reading decreased and the negative effect of large schools on 

success was more significant in the upper grades. In a study conducted in 20 schools in Ankara, 

a positive relationship was found between the size of the school (the number of students in the 

school) and the academic achievement of the students. In the study, it was determined that the 

students in schools with a large number of students have higher Secondary Education 

Institutions Exam (SEIE) success. In another study conducted with PISA 2015 data, as the 

teacher/student ratio in the school increases, success increases, but as the school size (number 

of students) increases, student success decreases (Erdoğan & Acar Güvendir, 2019). 

In the full model, this is determined as a significant predictor of the academic achievement 

of vocational high school students, and includes student and school level variables; while the 

student level variables did not change, the number of students in the school among the school 

level variables ceased to be significant. As a matter of fact, the differences in the graduation 

scores of vocational high school students in the study, when the student and school level 

variables are taken together, result from the school variables with a probability of 37% and the 

variables at the student level with a probability of 63%. It is seen that student level variables 

are more determinant in the academic success of vocational high school students than school 

level variables. 

In order to increase the academic success of vocational high school students within the 

framework of the results obtained in the research, weekend courses planned in accordance with 

the level of the students can be expanded. Guidance services can follow an effective study 

program in order to gain regular study habits to students. Increasing the education level of 

parents by reducing the dropout rates of children from disadvantaged backgrounds with low 

socioeconomic status and preventing early marriage of girls, may be important in terms of 

increasing academic success in the long term. In households with low socioeconomic and 

socio-cultural levels, the Ministry of National Education can provide students with free 



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extracurricular literary books in cooperation with publishing houses. At the same time, one of 

the fields of activity of non-governmental organizations working in the field of education may 

be to create libraries primarily for the households where disadvantaged children live. The 

dissemination of such activities to all levels, starting from the pre-school education level, may 

increase the academic success in the secondary education level, especially in vocational 

education, in the long term. Considering the difference that parents' socio-cultural capital 

creates in academic success, non-formal education activities aimed at improving parents' 

perceptions of education and increasing their cultural capital can be given weight in schools. 

By re-establishing the link between vocational and technical education with employment and 

higher education, attracting successful students to these schools, changing the perception of 

these schools can contribute to better motivation of both students and teachers, thus increasing 

academic success. Since the overwhelming majority of vocational high school students live 

below the poverty line, political governments should implement an effective anti-poverty 

program; It may be suggested to provide cash support based on the number of children going 

to school so that there are no households without social security left and families can be 

supported for their education expenses. 

In addition to these suggestions developed from the current research results, there are also 

suggestions that can be developed over the general academic achievement studies in the 

literature. When the results of the studies on academic success in the literature on the causes of 

failure are examined, many reasons have been identified; Some studies seem to reach 

contradictory results. The reasons for this situation are that the participants/sample are 

different, the studies were conducted at different times and in different school types and with 

different dependent variables. This differentiation revealed by the studies in the literature leads 

to the inability to reveal clear cause-effect or inter-variable relationships related to academic 

achievement. This situation creates uncertainty about what measures education administrators 

should take to increase academic success. In order to eliminate this uncertainty, research on 

academic achievement should be carried out in a continuous, systematic and institutional 

framework. In other words, the Ministry of National Education should employ educational 

science experts who will conduct research on the problems of the education system, especially 

academic success, in schools, districts and provincial organizations in order to increase 

academic success. 

This research can be re-done by including other high school types and regional-level 

variables in the analysis. Each variable and/or each student's academic success history that is 

determined to affect academic success can be examined in depth with qualitative research 

methods. The small number of schools analyzed in this study can be considered as a limitation. 

In this context, it is recommended that similar studies be conducted with more schools. 

  



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