What is the role of citizenship education? In the European context, the role of citizenship education is to support students in becoming active, informed, and responsible citizens and develop knowledge, skills, attitudes, and values related to acting democratically, acting socially responsibly, interacting in an effective and constructive way, and developing critical thinking (European Commission, 2017).

Civic knowledge is the result of different factors. Knowles and McCafferty-Wright (2015) argue that understanding the principles of democracy goes beyond civic knowledge and that students must learn how to use the knowledge they learn. However, civic knowledge creates the cognitive component of citizenship, which helps to create an open classroom climate that promotes higher levels of civic knowledge and political efficacy (Torney-Purta, Lehaman, Oswald, & Schulz, 2001) and develops such skills as interpreting political communication and promoting engagement in daily citizenship activities (Isac, Maslowski, & van der Werf, 2011). Creemers (1994) explains civic knowledge as school-, classroom- and student-level outcomes from the educational system. However, socioeconomic status demonstrates a relationship with civic knowledge whereby the greater one’s socioeconomic status is, the stronger one’s relationship is with civic knowledge (Knowles & McCafferty-Wright, 2015; Schulz, Ainley, Fraillon, Kerr, & Losito, 2010).

Active participation in society is argued to show citizenship knowledge as a factor that prepares young people for political and civic participation and engagement (Knowles & McCafferty-Wright, 2015; Whiteley, 2014). Citizenship knowledge obtained during school time, voting experience, participation as a candidate in a school-level election (Schulz et al., 2017), and an open class climate (Torney-Purta et al. 2001) are argued to be important factors for an excellent level of citizenship education.

Banks (2017) argues that successful citizens have high levels of civic engagement and participation. This engagement includes a number of citizenship activities, such as political participation (Whiteley, 2014; Bolton, 2015), engagement with social media (Koršňáková & Carstens, 2017), a willingness to actualize national values and to protect the nation-state if it is endangered (Banks, 2017), holding ethical norms and values (Veugelers & Groot, 2019; Lin, 2015), community- and school-level engagement (e.g. volunteering) (Schulz et al., 2017), and citizenship self-efficacy (Lin, 2015). However, present and future civic participation must be kept in mind when analyzing post-Soviet and non-post-Soviet countries.

The education curriculum, class culture, and school culture influence citizenship education in schools (Torney-Purta, 2002). In citizenship education, special attention is paid to morals and values (Veugelers & Groot, 2019), a sense of efficacy, and the feeling that a person can change things by political action (Whiteley, 2014In other words, citizenship education is one of the traditional approaches to values education (Hoge, 2002) and gets impressed on all three elements of citizenship – civil, political, and social (Marshall, 1964). Additionally, citizenship education promotes democratic ways of thinking, and students’ sense of efficacy is the starting point for them to realize that things can be changed by political action (Whiteley, 2014).

The survey explores citizenship-related factors and investigates factors related to civic knowledge for students with different levels of civic knowledge in 6 countries – Finland, Estonia, Latvia, Lithuania, Sweden, and Denmark. The aim of the article is to determine which factors have a relationship with civic knowledge. As such, there are two key research questions:     

In this study, we use student data collected in the International Civic and Citizenship Education Study (ICCS 2016). In the analysis, its complex two-stage survey sampling design has to be taken into account, hence weights and jackknife replicates are provided and used to compensate for the disproportional sampling probabilities and allow standard errors for statistical tests to be correctly estimated. For the analysis, we used the survey-weighted linear regression method, which was implemented using the statistical software R. Due to the large scale of the assessment, students were asked to take only part of the complete test, and hence five plausible values were provided as response variables, calculated with the multiple imputation method. Therefore, as suggested by Schulz (2018), all regression steps were performed five times with each plausible value taken as a response, and then the results were combined. All of the explanatory variables that were used in this study were grouped into four blocks that we denoted as background variables, engagement variables, future engagement variables, and values. Regression models were constructed by adding and removing these blocks. We started with background variables, and then other blocks were ranged sequentially from most to least important by calculating the adjusted R squared at each step. Two datasets were created for the regression analysis; the first one was obtained by merging the datasets of Latvia and Lithuania, and those of Estonia, Denmark, Sweden and Finland were merged in the second. At the first stage of the regression analysis, we constructed models on these sets following the procedure described above. In the second stage, we investigated factors that could be related to civic knowledge specifically for students with the highest level of civic knowledge (above 563 points on the test) and for those with a lower level (under 563). Both of the merged datasets were divided at the 563-point threshold, and a regression model was constructed for each of these subsets. We repeated this five times since divisions had to be made according to each of the plausible values. The relatively large size of the samples (2,857 observations for Estonia and up to 6,271 observations for Norway) has to be taken into account when interpreting the regression results. For large sample sizes, even the tiniest differences will be statistically significant, hence confidence intervals are inspected in addition to p-values, and standardized coefficients are used to compare the importance of the predictors.

Our findings complement previous research about factors that have a relationship with the level of citizenship education. However, there are three main findings that will be highlighted in our conclusion at the conference. First, we recognize that there are five very important factors that have a relationship with civic knowledge. For all seven countries, a positive and significant relationship with civic knowledge is shown by two value predictors (Students’ endorsement of gender equality, Students’ endorsement of equal rights for all ethnic and racial groups), two background predictors (Students’ expected educational attainment, Number of books at home), and one future engagement predictor (Students’ expected electoral participation). Second, for the Nordic countries and Estonia, the future engagement predictor factors – Students’ expected participation in legal activities, Students’ expected participation in illegal protest activities, Students’ expected electoral participation have relationship with the highest level of civic knowledge (above 563 points on the test) but not recognized for students in a lower level (under 563). For Latvia and Lithuania the future engagement predictor factors are indicated in a lower level of civic knowledge (under 563). Third, for the Nordic countries and Estonia, immigration status is a statistically significant factor for students with a civic knowledge level of less than 563, but for Latvia and Lithuania, this factor is not found in general. Note: This research was funded by the “Post-doctoral Research Aid” project no. 1.1.1.2/VIAA/1/16/00, research application no. 1.1.1.2/VIAA/1/16/020.

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