Online courses are the latest technologically mediated form of distance asynchronous learning and today they are among the most popular non-formal education practices. The online courses use the internet (and all related technologies) to provide educational recourses to a wide audience whenever and however is convenient for them, breaking all institutional barriers to knowledge and information. Due to their user-friendly interface and user-oriented content, it is theorized that the online courses would play a key tool for lowering the educational inequalities and increasing inclusivity (Germain-Rutherford and Kerr, 2008). Judging from studies on inequalities in MOOCs, however, online courses appear to be less inclusive as expected.

Unfortunately, the literature on inequalities in participation and inclusivity in the online courses, outside of the MOOCs research, is poorly developed, mostly due to the lack of appropriate data.

Our study seeks to contribute to the literature on inequalities in participation in online courses in three main ways. First, we use unexplored data from the EU Information and Communications Technologies (ICT) usage by Households and Individuals for 2021. Second, our analysis provides a cross-national comparative perspective, using two-level regression analysis taking into account the level of the country's innovation. Third, we theorized the inequalities and the role of technologies in participation inequalities from the perspective of Bourdieu’s theory of cultural capital (Beckman et al., 2018).

Our analytical framework is based on Bourdieu's cultural capital theory but adapted to the online courses and digital context. We interpret the three states of cultural capital - embodied , objectified, and institutionalized statе (Bourdieu, 1986) - using them to create statistically analyzed variables.

We start with objectified cultural capital, arguing that technologies like laptops, internet access and smartphones have a major role in the inequalities in the online courses. Cultural capitals are noticeable through the device used by the students, which is recognised by pre-established algorithms, acting on behalf of their owners. These devices are not equally distributed among the learners, giving them unequal access to the educational content. Laptops and PCs are more adaptable to users’ educational needs but require more cultural capital in contrast to mobile devices, which are hard to adapt due to their original purposes but require less cultural capital.

Embodied cultural capitals are needed not only to use a device but also to navigate the web and to be able to find and recognise exactly what you need (van Dijk and van Deursen, 2014). Digital skills, linked to information seeking, are also distributed unevenly among the different social classes and especially with regard to the individual level of formal education, as studies of the social divide have shown (Hargittai, 2002).

The cultural capitals have ‘efficacy’ in relation to a specific field (Bourideu, 1984). How valuable the skills and knowledge learned in online courses are depends on the everyday struggles over price-forming locked around the strategies in the different fields. Comparing manual with non-manual occupation we study the effects of field efficacy. We also compute the differences between the countries, which Bourdieu considered as meta-fields (Bourdieu and Wacquant, 1992), taking their level of innovation.

Against this background research questions (RQs):

1) Which state (embodied, objectified, institutionalised) of cultural capital is a better predictor for participation in online courses?

2) Does the ‘efficacy’ of cultural capital on participation in online courses differ between specific fields?

3) Are there country differences in the influences of cultural capital in online courses?

4) Can a lack of cultural capital be mitigated by the country’s level of innovation in regard to participation in online courses?

We use data from the EU Information and Communications Technologies (ICT) Usage by Households and Individuals for 2021. This is an annual survey which collects harmonised and comparable information on the use of ICT in households and by individuals. It covers a wide range of characteristics related to access to and use of ICTs, the use of the internet and other electronic networks for different purposes, ICT competences and skills, etc., as well as for various sociodemographic characteristics such as gender, age, level of education, occupation and main status on the labour market. We limited our analysis to people aged 25–64. Our methodological consideration is to cover most of the economically active individuals, having either manual or non-manual jobs, which we use as a proxy to an economic field. After doing some list-wise deletion of the cases with missing values on one or more of the individual variables, we ended up with an analytical sample consisting of 95,345 adults nested in 28 countries. The dependent variable is a dummy variable which distinguishes whether a person had completed an online course in the last 3 months or not. For the embodied state of cultural capital, we have used the level of overall digital skills. For institutionalised cultural capital, we have used the level of formal education. As a proxy for objectified cultural capital, we have combined the usage of laptops and desktop computers to connect to the internet compared to smartphones, tablets and other devices. For the respective social field, we have determined whether the individual is working a manual job or one in the service sector. One independent variable has been included at country (as a meta-field) level: the Innovation Index. We have controlled the results for gender and age Given that our dependent variable is a dichotomous one, we have employed logistic regressions (Long and Freese, 2006), as well as a series of logit models with random effects. These models were considered appropriate because our dependent variable is binary and individuals (level 1) may be nested within countries (level 2). This multilevel modelling technique (Rabe-Hesketh and Skrondal, 2012) allows us to explore not only the associations between variables at individual and macro-level, but also whether there are cross-level interactions between variables at different levels.

This study has revealed that the lack of cultural capital in all three studied dimensions constrains participation in online courses, even after controlling for gender and economic field. The effects of the capitals are measured by classical indicators such as formal level of education but they are also more closely linked to sub-fields of online education such as digital skills and access to devices like laptops. However, it seems that institutionalised cultural capital measured by level of education is the strongest predictor. The results indicate that being a manual worker is associated with lower odds of participating in online courses in comparison to a non-manual worker, given the other covariates. From the perspective of our framework, since manual work is less educationally intensive and values different skills (Lehmann and Taylor, 2015), the knowledge gained through courses is less valued, discouraging manual workers to participate. Our analysis has shown that there are considerable country differences in participation in online courses. These findings suggest that cultural capital interacts in a different way with the national meta-field and could follow different patterns of inequalities. These implications are aligned with other studies suggesting that social class and status have different effects on online activities in different countries (Lindblom and Räsänen, 2017). Our results suggest that the lack of cultural capital in participation in online courses could be mitigated by the country’s level of innovation. Yet, we have only found evidence of this for two of the three studied dimensions of cultural capital: level of education and having a device, not digital skills. As the social classes are stratified in every country, so are nation-states in the global field (Buchholz, 2016), and adult and higher education is playing an especially crucial role as a structuring institution (Marginson, 2008).

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