As part of the Digital Education Action Plan 2021–2027, the European Commission has highlighted the need to utilise the potential of digital technologies for learning and teaching and to develop people’s digital skills (European Commission, 2020b). However, it has been shown that certain groups, such as rural citizens, are more at risk of digital exclusion than others. Due to the fact that computer and internet access and the ability to use them competently can have a significant impact on an individual's quality of life and increase their participation in society, the digital divide poses a problem for the individual as well as society (European Commission, 2020a; OECD, 2020). In 2010, the European Commission considered e.g. broadband internet access in rural areas in the European Union as one of the most important action areas in the implementation of the ICT popularisation policy and the creation of the European Information Society (European Commission, 2010). According to this, the European Commission pointed out the need to respond to digital transformation risks, especially with regard to a digital divide between urban and rural areas (European Commission, 2020b). In this context, educational institutions face the challenge of bridging the gap between regions and providing equal opportunities for all students, regardless of where they live, with regard to the digital transformation (OECD, 2020).

In order to describe from a theoretical perspective at which points educational inequalities can occur between different groups (e.g. rural/urban) in the course of digitalisation, the multidimensional construct of the digital divide (van Deursen & van Dijk, 2018; van Dijk, 2012) can be used. This approach distinguishes four dimensions: (1) access to and (2) use of ICT, (3) motivation (motives) for use, and (4) digital skills. This four-dimensional model of the digital divide can be understood as a sequential stage model of ICT acquisition. Consequently, social inequalities can occur in each of these dimensions and affect social participation in the digital world (van Dijk, 2020, 2005).

While there are numerous studies, like the international large-scale monitoring study ICILS 2018 (International Computer and Information Literacy Study) of the IEA (International Association for the Evaluation of Educational Achievement), which examined the computer and information literacy (CIL) of eighth graders in an international comparison, taking different background characteristics (e.g. age, gender, social background) into account (Eickelmann et al., 2019 [National Report ICILS 2018 Germany]; Fraillon et al., 2019a), there are far fewer studies considering the demographic aspect (rural/urban). For example, Wang (2013) examined the availability of digital devices in primary schools in Taiwan and discovered that the technology availability in urban schools was significantly better than in rural schools. Furthermore, student frequency of using Interactive Whiteboards in learning in urban schools was significantly better than in rural schools (Wang, 2013). On the other hand, a study on the frequency of internet use between urban and rural areas in the UK found no significant difference (Eynon, 2009). However, a difference was found with regard to certain online activities: Internet users from urban areas are more likely to use the Internet for training or formal learning than users from rural areas.

Currently, there has been no research that focuses on all four dimensions of the digital divide (access to ICT, use of ICT, motivation for use ICT and digital skills) in relation to the demographic situation (rural/urban) in Europe. Following this approach, this contribution addresses the following research question: Are there any differences between students from urban and rural areas concerning the four dimensions of the digital divide ([1] access to ICT, [2] use of ICT, [3] motivation (motives) for use ICT, and [4] digital skills) in Europe?

Focusing on the outlined research question, this contribution conducts secondary analyses by using the representative data of the IEA ICILS 2018 study (Fraillon et al., 2019a, 2019b). With this, the data of eighth graders in terms of CIL performance as well as comprehensive background information about the students (student questionnaire) and the school (school questionnaire), which were determined within the framework of the IEA study ICILS 2018, is used. Regarding the research question, the access to ICT, the use of ICT, the motivation for the use of ICT and the digital skills (here: CIL) of students from both urban and rural areas in Europe are examined in the analyses. All analyses carried out in this contribution were conducted by using the IEA IDB Analyzer (Rutkowski et al., 2010). To answer the research questions, in the first step, the school data from the school questionnaire, which could also be filled out by school principals or ICT coordinators (Fraillon et al., 2019a), was aggregated to the student data from the student questionnaire in order to identify the locality (rural: up to 15,000 citizens; urban: more than 15,000 citizens) of the schools the eighth graders attended. To conduct analyses at a European level, the data set was adjusted in a second step so that the seven European countries (Denmark, Finland, France, Germany, Italy, Luxembourg and Portugal) that participated in ICILS 2018 were included. Afterwards, descriptive analyses as well as significance tests using t-tests, separated by area, urban and rural, with regard to the selected items of the four outlined dimensions of the multidimensional construct of the digital divide (van Deursen & van Dijk, 2018; van Dijk, 2012) are carried out: The access to ICT at school (referring to the 1st dimension of the digital divide), the use of ICT by students for school-related purposes (2nd dimension), the motivation to use ICT (3rd dimension) and the digital skills (CIL) (4th dimension) between students from urban regions and rural regions are considered. All analyses conducted in this paper incorporate the complexity of the ICILS 2018 sampling approach by using case weights and the Jackknife Repeated Replication Technique (JRR) to correct standard errors (SE) (Mikheeva & Meyer, 2019). The analysis of students' CIL performance is based on the plausible values approach where each analysis is conducted with each of the plausible values and the results are then averaged (Fraillon et al., 2019b; Mikheeva & Meyer, 2019).

Concerning the research question, secondary analyses for the participating European countries, based on the IEA ICILS 2018 sample, were conducted. Considering the availability of ICT in the classroom (1st dimension), it shows that in two of the seven considered European countries (Denmark and Luxembourg) a significantly higher proportion of eighth graders from rural areas attend a school where digital media are available for use in most classrooms compared to students from urban areas. In terms of the use of digital media at school for school-related purposes (2nd dimension), only Luxembourg shows a difference in favour of students from rural areas. Regarding the motivation to use ICT (3rd dimension), a significant difference can be found for Italy where a higher proportion of students from urban areas report that they use ICT to work online with other students for school. Regarding CIL (4th dimension), it shows that in none of the considered European countries did students from rural areas achieve higher levels of computer and information literacy than students from urban areas. However, there are statistically significant differences in CIL (p<.05) in favour of students from urban regions in the following European countries: Denmark, Finland, Luxembourg and Portugal. Overall, some differences regarding the dimensions of the digital divide between students from urban and rural areas in European countries are visible, which support the necessity of closing the digital gap in ICT as highlighted by the European Commission (2020b). For further research, it would be interesting to examine in more detail why there are significant differences between rural and urban areas in some countries. What factors are involved and to what extent are individual results related to the students' CIL? In addition, the developments in the field of ICT in school and education in individual European countries should also be taken into account.

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