Teaching and learning conditions in schools with a high proportion of

Teaching and learning conditions in schools with a high proportion of

Teaching and learning conditions in schools with a high proportion of students from singleparent families as explanation of the negative effect of sin...

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Teaching and learning conditions in schools with a high proportion of students from singleparent families as explanation of the negative effect of single-parent family composition of schools. Jaap Dronkers,1 Gert-Jan M. Veerman & Suet-Ling Pong Maastricht University, University of Amsterdam & Pennsylvania State University Paper presented at the Twelfth Meeting of the European Network for Sociological and demographic Study of Divorce, October 2-4 in Paris, France. Introduction Prior research on the consequences of divorce for children’s educational performance is mainly restricted to the family context. In this paper, however, we focus on the school context. More specifically, we study how the single-parent family composition of schools affects the educational performance of children from single-parent and two-parent families. A similar approach was applied by Pong (1997, 1998), indeed finding a negative contextual effect of schools with high numbers of students from disrupted families in the USA. De Lange, Dronkers & Wolbers (2014) found that the share of single-parent families at school effects on children’s’ educational performance within 25 OECD countries, including the USA. In the literature, two explanations can be distinguished for the effect of school’s single-parent family composition on children’s educational performance: i.e. the decline of the community network of the school and the lower amount of teaching and learning time at school and at home. According to Pong (1997) and Sun (1999) parental influence on children extends beyond their own child and reaches the communities in which they live and the schools belonging to these communities. As previous research has shown, the type of student attending the school appears to be one of the most important factors influencing the effectiveness of the school (Pong 1997; Pong 1998). Schools with a large concentration of children from single-parent families are usually characterized by a lower socioeconomic status and by less social capital (i.e. indicated by parents’ social relations and networks with other parents). Therefore, all children attending such schools will perform less well, compared to children at schools with a smaller concentration of single-parent families. In addition to this community network explanation for the negative contextual effect of family disruption on children’s educational performance, Dronkers (2010) emphasizes the more difficult teaching and learning conditions in schools with a high proportion of students from single-parent families. The effectiveness of education depends on the amount of time that is available for both teaching and learning, which can be greatly diminished in schools where children have problems inside or outside the home that interrupt the teaching and learning process. As previously described, children of divorced parents have on average more emotional and other problems related to their parents’ divorce. If there are more students in a class with such problems, more learning and teaching time of the whole class might be used for non-academic goals. Garriga (2010) found that children of single-parent families are more often too late at school. A higher percentage of pupils from single-parent families in a school might thus lead to more pupils arriving too late at school and thus disturbing teaching and learning of the whole class. As a consequence insufficient learning and teaching time needed to reach a certain educational performance by all pupils might remain. Conversely, in student populations with none or few 1

Corresponding author: [email protected]

1

children from single-parent families, there might be less loss of teaching and learning time and thus a higher chance on sufficient time. In fact, the real learning and teaching time might differ in these two situations, despite identical class schedules, and thus educational performance will differ between both situations. The two research questions that we address are the following: 1) Does the school’s composition of students from single-parent families affects the conditions of teaching and learning (measured as classroom disruption) in these schools? 2) Do these conditions of teaching and learning mediate the negative effect of the school’s composition of students from singleparent families on students’ educational performance? If we can answer both research questions positively, the teaching and learning conditions is an additional or the best explanation of the negative effect of the school’s composition of students from single-parent families on students’ educational performance. If we cannot answer both research questions positively, the community network explanation is a better explanation of the negative effect of the school’s composition of students from single-parent families on students’ educational performance. Data and variables Data The analyses have been carried out using the cross-national Program for International Student Assessment 2009 (PISA). The cross-national PISA contains both social economic background and lesson behavior information of 15-year-old students from OECD and other developed countries (OECD, 2012). We focus on the Western countries. Thus, our dataset contains information on 232701 students in 28 countries (Australia, Austria, Belgium, Canada, Switzerland, Czech Republic, Germany, Denmark, Spain, Estonia, Finland, France, United Kingdom, Greece, Hungary, Ireland, Iceland, Italy, Luxembourg, Netherlands, Norway, New Zealand, Poland, Portugal, Slovak Republic, Slovenia, Sweden, and United States). We omit 1.8 percent of our students due to student with no information about their home situation. Furthermore, we omit 13.5 percent of these students due to missing data on other independent variables or because these students are in schools with less than 8 students per school. Surveyed pupils reported with whom they regularly live at home, and they were offered six possible persons (mother, father, brother, sister, grandparents, others) whom they could all tick. We only analyzed students who lived in a two-parent family or a single-mother family. We deleted all pupils living in other possible family forms, like single-father and grandparents-family. These possible family forms are rare in a number of involved countries, which might bias our analysis due to selectivity bias. Finally we also deleted all migrant pupils, both first and second generation, because single-parent might have a different meaning for migrants (Dronkers & Kalmijn, 2013). Consequently we use the data of 183982 students in 8001 schools in 28 OECD countries. Variables Dependent Variables The first dependent variable in this study is classroom disruption as perceived by the individual student. Classroom disruption is measured by the categorical question whether there is noise and disorder in the lessons. PISA 2009 contains five possible questions that could measure the classroom disruption with the following topics: ‘students don’t listen’, ‘wait for quiet’, ‘cannot work well’, ‘long time to start’, and ‘noise and disorder’. The questionnaire for students contains 2

four possible answers: ‘never or hardly ever’, ‘some lessons’, ‘most lessons’, or ‘all lessons’. Categorical Principal Components Analysis (CATPCA) in both in cross-national data and country data show factor loadings above 0.72 for all questions except for ‘wait for quiet’. The factor loading of ‘wait for quiet’ is in most countries near 0.7 but in Greece even 0.4. (see appendix 1). Consequently, we created for our cross-national analysis a latent variable for classroom disruption that contains all possible questions from PISA except ‘wait for quiet’ for each student. We also computed the mean classroom disruption using the disruption score of all students in the school as a second dependent variable. Although the answers of the students refer to the experience of the students and the interpretation of the question, we will refer in this paper to individual disruption and class disruption to make the text more readable. The third dependent variable math performance is a score on the math test developed by PISA. To measure school skills accurately would make the test too long to be feasible. Hence, PISA created a large number of very similar but shorter tests. Because such different tests can never offer exactly the same degree of difficulty, Item Response Modeling (IRM) was used to achieve comparable results between students who took different tests. We computed our regressions for every plausible value and averaged the parameter estimates in order to take into account the variance between these five plausible values. The skills scores were standardized for the Organization for Economic Co-operation and Development (OECD) countries using an average of 500 and a standard deviation of 100. Table 1 gives the descriptive statistics of the variables we use in our analyses. Individual level Family forms. A disadvantage of PISA is that it lacks information about the cause of single parenthood or guardianship of one the parents. Although we assume that in most OECD countries divorce or separation is the most common reason for single parenthood of parents of 15-year old students, there might be other reasons for growing up in a single-parent family (with or without a guardian), i.e., birth out of wedlock without a following marriage or cohabitation, and death of one of the parents. However, parents of 15-year old students are generally still too young to die, and the number of people who (intentionally or unintentionally) become a single parent already prior to childbirth will be rather low. An important advantage of the measurement of family form in PISA is that students were asked with whom they regularly live at home, and they were offered a number of possible persons, whom they could all tick. This way, the real family form in the eye of the students is measured instead of the formal situation, as reported by interested parents or authorities. Parents who separated after cohabitation (instead of marriage) before the child reaches the age of 15 are measured in the same way as formally divorced parents. Since separation after cohabitation has more or less the same effect on children as compared to divorce after marriage (Dronkers & Härkönen 2008; Härkönen & Dronkers 2006), the PISA data provide a more accurate picture in countries where cohabitation with children is common. Married parents, who stopped living together before the 15-year old student participates in the PISA survey, are also treated in the same way as formally divorced parents. This feature is especially relevant for catholic countries like Italy, Ireland, Portugal and Spain, where a formal divorce is still difficult to obtain. A disadvantage is that some children may live without a parent temporarily (e.g. fishermen, fathers working in the origin country). We believe, however, that this risk is small, as some students still 2

Only Greece shows a factor loading of 0.6 for ‘long time to start’.

3

will indicate that they live with both parents usually. We created a dummy variable indicating a single-mother family. Parental ESCS. The ESCS index of the parents is a composite index created within the PISA dataset of the parents’ occupational status, measured with the International Socio-economic Index of Occupational Status (ISEI) scale (Ganzeboom et al, 1992), the educational level of the parents, measured with the ISCED (International Standard Classification of Education) classification (UNESCO, 2006), and the presence of any material or cultural resources at the students’ homes. Female. We computed a dichotomous variable to classify gender. Boys are the reference group. Higher track refers to the track levels 2A and 3A of the International Standard Classification of Education (ISCED). The 2A and 3A programmes ultimately lead to tertiary education (OECD, 1999). This control variable takes into account the possible early selection of children of single parents into a lower educational level, as a consequence of lower earlier performance. The result of controlling for educational level might be that the relationship between family form and school’s percentage of singleparent families is underestimated. However, we prefer this risk of underestimation above a too easy confirmation of our hypotheses. We include the dummy “track missing”, representing 1.1 percent of the students. Other tracks are the reference category. School level All these school characteristics are computed with all deleted pupils included. The percentage pupils from single-parent families per school (either father or mother). The mean ESCS per school was calculated using the ESCS score of all students in the school. Percentage of females. We computed the percentage of females using the number of female students in the schools. Percentage of immigrants was calculated using the number of immigrant students in schools. Results All analyses are multi-level analysis with three levels: students, school and countries. Individual classroom disruption Table 2 shows the analysis with individual classroom disruption as dependent variable. The equation of model 1 contains only a few variables: living in a single mother instead of a two-parent family, the socio-economic background of the student (ESCS) and the school ESCS. The higher the individual ESCS and school ESCS the lower the classroom disruption perceived by the pupil. But students who live in a single mother family perceive more often classroom disruption. In model 2 we add other school- and individual characteristics to the equation of model 1, especially the school percentage single parents. This addition hardly changes the results for living in a single mother family: they perceive more class disruption. But interestingly we also find that the higher the school percentage single parents, the higher the level of perceived classroom disruption. This suggests that a higher percentage of single-parent families in a school increases the individual perception of classroom disruption, even controlled for other relevant individual and school characteristics. 4

We test this result with model 3, in which we includes the mean school level of classroom disruption. As we might expect, we find that the higher the mean level of classroom disruption of a school, the higher the level of classroom disruption as perceived by the student. But the significant effect of the school percentage single parents has become insignificant by this inclusion of the school level of classroom disruption. Even this addition hardly changes the results for living in a single mother family: they perceive more class disruption, irrespectively of the school level of classroom disruption or school percentage of single parents. We draw as a conclusion that a higher percentage single-parent family per school increases the school level of class disruption. In an equation, not shown in table 2, we added the interaction term ‘single mother family * level of school classroom disruption’, but the parameter of that interaction term is not significant. Table 3 shows the same outcome at a school level analysis. The higher the school percentage of single parents, the higher the school level of schoolroom disruption, irrespective the ESCS school composition and the school percentages migrants or girls. Math score We analyse in table 4 whether classroom disruption can explain the negative effect of school percentage single parents on educational performance. Model 1 of table 4 shows the well-known outcome that the school percentage single parents has a negative effect on the educational performance of all pupils in that school, irrespective whether they live in a single mother family or in a two-parent family. Living in a single mother family has no significant effect on performance and the same holds for the interaction between single mother family and school percentage single-parent families. With other words it is more the single parent context, which influences all students, instead of the individual situations within the separate single mother families. We include in model 2 the individual classroom disruption as perceived by the student. That variable has the expected negative effect on the educational performance, but the significant effect of school percentage single-parent families or the insignificant effects of single mother family and the interaction-term hardly change by this addition. The inclusion of the school level of class disruption in model 3 lowers the effect of school percentage single-parent family, without making it insignificant. This means that a part of the effect of percentage single-parent family on math score is indirect, mediated by classroom disruption. We estimated a cross-level indirect path of percentage single-parent family to math performance through classroom disruption, using the Structural Equation Model software package Mplus (Muthen and Muthen, 2012). We estimated a Structural Equation Model that is a combination of table 3 and model 3 of table 4 without cross-level interaction terms. Figure 1 shows parameters from this Structural Equation Modelwhich are comparable to those in table 3 and table 4. The indirect relationship that the proportion of single parents have with math performance through disorder have a parameter estimate of -0.055 and is significant at p<0,05.

In model 4 we add the interaction between individual perception of classroom disruption with the school level of classroom disruption. Interestingly, the opposite parameters of individual perception variable and the interaction term have more or less the same strength and thus neutralize each other. We interpret the positive effect of the interaction term in the following way: pupils in schools with high levels of classroom disruption and who also experience a high level of disruption are better able to avoid the most negative consequences of the high level of classroom disruption. However the school level of classroom disruption has a strong negative effect on educational performance, overriding all other effects. This does not mean that there is a 5

change in the negative effect of school percentage single-parent families: it continues to have a negative and significant effect on educational performance (although it is substantially smaller than in model 1). Conclusion Given our results, we can answer our two research questions positively. First, the school’s composition of students from single-parent families affects the conditions of teaching and learning in schools, in our case indicated by classroom disruption. We also show that this negative effect cannot be fully explained by the individual situation of the single mother family, because the school percentage single-parent families has still a significant effect on classroom disruption, also after controlling for living with a single mother. Second, these lower conditions of teaching and learning (as measured by the level of school classroom disruption) mediate the negative effect of the school’s composition of students from single-parent families on individual educational performance of all students. This mediation is only partly, because the school level of classroom disruption cannot explain the full effect of school percentage single-parent families: there remains a significant negative effect of school percentage single-parent families on individual educational performance of all students. This remaining effect gives support to the social capital explanation: schools with a large concentration of children from single-parent families are usually characterized by less social capital (i.e. indicated by parents’ social relations and networks with other parents), as proposed by Pong (1997) and Sun (1999). Another explanation of this remaining effect might be that classroom disruption does not fully measure the quality of the conditions of teaching and learning in schools, as influenced by the school percentage single-parent families. Pupils living in a single mother family have not a significantly lower educational performance if we control for the school percentage single-parent families. This means that the different context (in this case school composition) of the single-mother pupil is a more important explanation of low educational performance than the ling in a single mother family. However, living in a single mother family increases the individual level of classroom disruption and also the chance to attend a school with higher percentages of single-parent families. These factors in their turn influence negatively educational performance of pupils, irrespectively whether they live in a single mother family or in a two-parent family. The analyses of these contexts of single-parenthood are also important because that will learn us more the processes which bring about the relative strong variation in negative outcomes of various family forms (Pong, Dronkers & Hampden-Thompson, 2003). But this analysis is the first step to unravel this interesting and important effect of school percentage single-parent families, which is a modern form of a negative school composition, independent of the socio-economic school composition. It is important to analyze this effect further, because it illustrates that single-parenthood is not only a divorce or separation decision, taken by two individual partners; it is an decision which effect also the life chances of other children, and thus of their society. Literature De Lange, M.. Dronkers, J. & Wolbers, M. (2014). Single-Parent Family Forms and Children’s Educational Performance in a Cross-Comparative Perspective: Effects of School’s Share of Single-Parent Families. School Effectiveness and School Improvement 25 (3): 329-350. Dronkers, J. (2010). "Features of educational Systems as Factors in the Creation of Unequal Educational Outcomes." pp. 299-328 in Quality and Inequality of Education. Cross6

National Perspectives, edited by J. Dronkers. Dordrecht /Heidelberg/ London/New York: Springer Dronkers, J. & Harkonen, J. (2008). "The intergenerational transmission of divorce in crossnational perspective: Results from the Fertility and Family Surveys." Population StudiesA Journal of Demography 62:273-288. Dronkers, J. & Kalmijn, M. (2013). Single-parenthood among migrant children: Determinants and consequences for educational performance. CReAM Discusion Paper Series 09/13 Garriga, A. (2010). Consequences of parental divorce and family structure on children's outcomes in European societies: individual, cohort and country explanations. PhD dissertation, University Pompeu Fabra (Barcelona). Härkönen, J. & Dronkers, J. (2006). "Stability and Change in the Educational Gradient of Divorce. A Comparison of Seventeen Countries." European Sociological Review 22:501 517. Muthén, L.K. and Muthén, B.O. (1998-2012). Mplus User’s Guide. Seventh Edition. Los Angeles, CA: Muthén & Muthén Organization for Economic Co-operation and Development. (1999). Classifying Educational Programmes. Manuel for ISCED-97 Implementation in OECD countries. Paris: OECD Publishing Organization for Economic Co-operation and Development (2012). PISA 2009 Technical Report, PISA. Paris: OECD Publishing. doi: 10.1787/9789264167872-en Pong, S.-L. (1997). Family Structure, School Context, and Eighth-Grade Math and Reading Achievement. Journal of Marriage and the Family, 59(3), 734-746. Pong, S.-L. (1998). The school compositional effect of single parenthood on 10th-grade achievement. Sociology of Education, 71(1), 24-43. Pong, S.-L, J. Dronkers en G. Hampden-Thompson, (2003). "Family Policies and Children's School Achievement in Single- Versus Two-Parent Families." Journal of Marriage and Family 65:681-699. Sun, Y. (1999). The Contextual Effects of Community Social Capital on Academic Performance. Social Science Research, 28(4), 403–426.

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Figure 1: Direct and indirect effects of percentage single parents family per school on math performance of pupils.

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Table 1: Descriptive Statistics N Minimum Maximum Mean Std. Deviation Individual classroom Disruption 181916 -1,12 3,26 -,01 ,99 Student ESCS 183982 -3,82 3,41 ,16 ,91 School ESCS 183982 -1,90 1,72 ,11 ,51 School % single parents* 183982 ,00 75,00 11,93 7,69 Mean school classroom disruption 183982 -1,02 1,91 -,01 ,39 Math score 183982 20,96 864,32 511,34 84,65 Single parent 183982 ,00 1,00 ,11 ,32 Female 183982 ,00 1,00 ,50 ,50 % school migrants* 183982 ,00 96,77 8,23 11,93 % school Female* 183982 ,00 100,00 50,14 18,19 Higher track 183982 ,00 1,00 ,77 ,42 Track missing 183982 ,00 1,00 ,01 ,10 *Mean centered at country-level in analysis

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Table 2: The effects of individual characteristics and the school compositions on individual classroom disruption Model 1 Model 2 Model 3 B S.E. B S.E. B Constant 0.028 0.031 0.148 0.033 0.045 Single mother 0.058 0.007 0.054 0.007 0.053 Individual ESCS -0.016 0.003 -0.019 0.003 -0.020 School ESCS -0.175 0.010 -0.133 0.010 0.023 School % single parent 0.005 0.001 -0.001 Female -0.105 0.005 -0.105 Higher track -0.102 0.014 -0.008 Track missing -0.035 0.037 0.007 % migrants 0.001 0.000 0.001 % Female -0.003 0.000 0.001 Mean school classroom disruption 1.009 Variance Country 0.025 0.007 0.026 0.007 0.000 School 0.105 0.002 0.098 0.002 0.000 Student 0.864 0.003 0.861 0.003 0.831 -2*loglikelihood 500.001.413 499.081.274 482.684.675 Source: own computation of PISA wave 2009; n-countries = 28; n-schools = 8001; n-students = 181916

S.E. 0.006 0.007 0.003 0.005 0.000 0.005 0.005 0.021 0.000 0.000 0.006 0.000 0.000 0.003

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Table 3: The effects of the school compositions on school classroom disruption (school level analysis) B S.E. Constant 0.031 0.030 School percentage single parents 0.005 0.001 School ESCS -0.165 0.009 Percentage school migrants -0.000 0.000 Percentage school female -0.004 0.000 Variance Country level 0.025 0.007 School level 0.132 0.002 -2*loglikelihood: 6.589.861 Source: own computation of PISA wave 2009; n-countries = 28; n-schools = 8001.

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Table 4: The effects of the school composition and classroom disorder on math score. Model 1 Constant Single mother School percentage single parent Single mother* school % single parent Individual ESCS Mean school ESCS Female Higher track Track missing Percentage migrants Percentage Female

B 480.061 -0.741 -0.465 -0.104 18.330 53.753 -17.466 36.423 20.029 -0.130 0.149

Model 2 S.E. 3.648 0.559 0.059 0.061 0.201 0.995 0.323 1.127 3.091 0.033 0.024

Individual Disruption Mean school disruption Individual Disruption*Mean disruption Variance

Country School Student -2*loglikelihood:

341.637 93.425 1.268.552 23.412 4.117.288 13.878 2.069.659.769

B 481.332 -0.372 -0.440 -0.096 18.163 52.710 -18.229 35.986 20.731 -0.121 0.127 -5.770

Model 3 S.E. 3.575 0.558 0.058 0.061 0.201 0.985 0.323 1.130 3.081 0.033 0.024 0.163

326.835 89.424 1.234.229 22.866 4.060.795 13.768 2.043.907.270

B 482.021 -0.363 -0.371 -0.101 18.172 50.601 -18.200 35.551 20.376 -0.123 0.073 -5.516 -13.196

Model 4 S.E. 3.472 0.558 0.058 0.061 0.201 0.995 0.323 1.127 3.071 0.033 0.024 0.165 1.181

306.648 83.994 1.211.858 22.509 4.060.841 13.768 2.043.783.271

B 481.046 -0.323 -0.365 -0.101 18.128 50.374 -18.366 35.519 20.428 -0.125 0.072 -6.682 -14.052 7.310

S.E. 3.462 0.558 0.058 0.061 0.201 0.996 0.323 1.127 3.069 0.033 0.024 0.177 1.182 0.405

304.525 82.631 1.213.275 22.521 4.053.118 13.742 2.043.457.666

Source: own computation of PISA wave 2009; n-countries = 28; n-schools = 8001; n-students = 181916

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Appendix 1: Factor structure of classroom disruption per country Australia: Dimension 1 ,848 ,837 ,860 ,860

Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,321 ,389 -,349 -,346

Austria Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,836 ,882 ,816 ,819

,459 ,204 -,390 -,299

Belgium Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,831 ,840 ,815 ,811

-,387 -,317 ,336 ,388

Canada Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,831 ,834 ,842 ,827

-,374 -,348 ,315 ,407

Switzerland Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,831 ,834 ,842 ,827

-,374 -,348 ,315 ,407

Czech Republic Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder

2 ,846 ,867

-,403 -,310 13

Lessons - Cannot work well Lessons - Long time to start

,826 ,830

,380 ,357

Germany Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,773 ,864 ,824 ,793

,578 ,032 -,135 -,458

Denmark Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,800 ,812 ,771 ,795

-,429 -,391 ,468 ,377

Spain Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,806 ,867 ,835 ,823

,528 ,125 -,302 -,342

Estonia Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,828 ,864 ,831 ,809

-,442 -,254 ,279 ,438

Finland Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,839 ,868 ,812 ,846

-,411 -,277 ,462 ,248

France Dimension 1 Lessons - Students don’t listen

2 ,815

-,340 14

Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

,845 ,841 ,765

-,255 ,034 ,608

United Kingdom Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,859 ,864 ,872 ,863

,360 ,331 -,320 -,366

Greece Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,809 ,799 ,791 ,600

-,300 -,265 -,019 ,784

Ireland Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,847 ,863 ,867 ,868

,393 ,302 -,340 -,344

Iceland Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,839 ,840 ,849 ,854

,351 ,344 -,358 -,328

Italy Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,775 ,849 ,813 ,819

,595 ,033 -,364 -,236

Luxembourg Dimension 1

2 15

Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

,808 ,877 ,837 ,829

,549 ,058 -,296 -,298

Netherlands Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,799 ,852 ,788 ,784

,509 ,176 -,292 -,417

Norway Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,799 ,852 ,788 ,784

,509 ,176 -,292 -,417

New Zealand Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,849 ,847 ,852 ,850

,343 ,352 -,343 -,350

Poland Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,809 ,855 ,813 ,836

,475 ,257 -,429 -,306

Portugal Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,829 ,859 ,837 ,814

-,436 -,235 ,236 ,449

Slovak Republic Dimension 16

1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,808 ,840 ,778 ,821

-,444 -,325 ,493 ,302

Slovenia Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,842 ,896 ,834 ,879

-,454 -,156 ,461 ,156

Sweden Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,795 ,847 ,817 ,812

,499 ,246 -,355 -,388

USA Dimension 1 Lessons - Students don’t listen Lessons - Noise and disorder Lessons - Cannot work well Lessons - Long time to start

2 ,812 ,826 ,831 ,841

,416 ,327 -,388 -,339

17