EDUCATION AND INEQUALITY IN DIGITAL OPPORTUNITIES Differences in Digital Engagement Among Finnish Lower and Upper Secondary School Students Meri-Tuulia Kaarakainen RESEARCH UNIT FOR THE SOCIOLOGY OF EDUCATION, RUSE KOULUTUSSOSIOLOGIAN TUTKIMUSKESKUS, RUSE Koulutussosiologian tutkimuskeskuksen raportti 82 The originality of this dissertation has been checked in accordance with the University of Turku quality assurance system using the Turnitin OriginalityCheck service. Turun yliopiston laatujärjestelmän mukaisesti tämän julkaisun alkuperäisyys on tarkastettu Turnitin OriginalityCheck-järjestelmällä. ISBN 978-951-29-7819-9 (PDF) ISSN 1235-9114 TURUN YLIOPISTO Yhteiskuntatieteellinen tiedekunta Sosiaalitieteiden laitos Koulutussosiologia MERI-TUULIA KAARAKAINEN: Education and Inequality in Digital Opportunities – Differences in Digital Engagement Among Finnish Lower and Upper Secondary School Students Väitöskirja, 197 s. Yhteiskunta- ja käyttäytymistieteiden tohtoriohjelma Marraskuu 2019 Tiivistelmä Tämän väitöskirjan tavoitteena on laajentaa näkemystä digitaaliteknologioista koulutuksessa korostamalla teknologian mahdollistamia digitaalisia affordansseja digitaalisten laitteiden ja sähköisten oppimateriaalien opetuskäyttöön sekä digitaalisen pedagogiikkaan rajautuvan näkökulman sijaan. Tosiasia, etteivät digitaaliset toimintapotentiaalit ole yhtäläisesti avoinna kaikille edellyttää huomion kiinnittämistä digitaaliseen eriarvoisuuteen suhteellisena ilmiönä, joka rajoittaa yksilöiden kykyä hyödyntää tarjoutuvia affordansseja. Tämä tutkimus keskittyy koulutuksen kontekstissa sosiaalisiin hierarkioihin, jotka myötävaikuttavat digitaalisen osallisuuden epätasaiseen jakautumiseen määrittäen yksilöiden asemaa suhteessa digitaalisiin affordansseihin. Näistä teoreettisista lähtökohdista jäsentyvät tämän väitöskirjatutkimuksen kysymykset: Missä määrin sosiaaliset rakenteet, erityisesti sukupuoli, ikä ja koulutusvalinnat, määrittävät 12–22-vuotiaiden suomalaisten digitaalisia taitoja ja käyttötottumuksia? Sekä, missä määrin ja millä tavoin suomalaisten perus- ja toisen asteen opiskelijoiden osaamisesta ja käyttötottumuksista koostuva digitaalinen osallisuus kasautuu tietyille yksilöille? Tutkimuksen empiirinen osuus koostuu viidestä alkuperäisestä artikkelista, jotka hyödyntävät kahta otosta suomalaisista opiskelijoista analysoiden kaikkiaan 11 820 opiskelijan digitaalisia käyttötottumuksia ja digitaalista osaamista. Sukupuoli sosiaalisena kategoriana tuottaa eroja opiskelijoiden digitaaliseen osaamiseen ja digitaalisiin käyttötottumuksiin. Tulokset osoittavat, että sukupuolten väliset erot digitaalisessa osallisuudessa suomalaisten perus- ja toisen asteen oppilaiden keskuudessa ovat suurelta osin aihespesifejä yhdistyen sukupuolittuneisiin mieltymyksiin, toisin sanoen erilaiseen orientoitumiseen digitaalitekniikkaa ja potentiaalisia digitaalisia affordansseja kohtaan. Näiden mieltymysten erottuessa selvästi tutkitussa 12–22-vuotiaita koskevassa aineistossa, on oletettavaa, että erot kehittyvät jo varhaisemmassa vaiheessa lapsuutta ja nuoruutta. Ikä, jopa nuorten Internet-käyttäjien keskuudessa, vaikuttaa sekä digitaaliseen osaamiseen että käyttötottumuksiin. Iän merkitys itsenäisenä muuttujana nuorten keskuudessa selittyy etenkin sillä, että digitaaliteknologian käyttö monipuolistuu iän myötä. Erityisesti juuri käyttökokemusten monipuolisuus kartuttaa nuorten digitaalista osaamista. Koulutus todetaan merkittävimmäksi yksittäiseksi tekijäksi, joka aiheuttaa eroja nuorten digitaaliseen osaamiseen ja käyttötottumuksiin. Se on yhtäältä kategorinen sosiaalinen hierarkia, sillä koulutustason nousu lisää digitaalista osallisuutta. Toisaalta tulokset osoittavat huomattavia eroja saman koulutusasteen sisällä, sillä osaaminen ja hyödylliset käyttökokemukset kertyvät todennäköisimmin niille opiskelijoille, jotka opiskelevat miesvaltaisilla koulutusaloilla. Sukupuolittuneisuus korostuu etenkin opiskelijoiden ilmaisemassa kiinnostuksessa ICT-alasta tulevaisuuden jatkokoulutus- tai ammattialana. Opiskelijoiden teknologia- mieltymysten ja koulutusvalintojen voimakas sukupuolittuneisuus vahvistavat toinen toisiaan ja lisäävät näin tulevien informaatioyhteiskunnan kansalaisten keskuudessa sukupuoleen perustuvia jakoja niin digitaalisessa osallisuudessa kuin myös mahdollisuuksissa hyödyntää tarjoutuvia digitaalisia affordansseja. Tutkimus korostaa sosiologisen tarkastelun tärkeyttä teknologian ja siihen liittyvän sosiaalisen toiminnan merkityksen ymmärtämiseksi koulutuksen kontekstissa. Väitöskirjan tulokset osoittavat, että sukupuoli, ikä ja sukupuolittuneet koulutusvalinnat määrittävät suomalaisnuorten digitaalista osaamista ja käyttökokemuksia. Digitaalinen osallisuus osoittautuu luonteeltaan kasautuvaksi; digitaaliset taidot ja teknologioiden käyttö ovat toisiinsa kietoutuneita ja toisiaan vahvistavia. Yhdistelmällisyys ja peräkkäisyys ovatkin suomalaisopiskelijoiden digitaalisten valmiuksien tunnuspiirteitä. Siinä missä yhdistelmällisyys luonnehtii digitaalisen osallisuuden kumuloitumista tietyille yksilöille, peräkkäisyys viittaa todennäköisyyteen, että kyseiset yksilöt myös hyötyvät eniten käytön myötä tarjoutuvista digitaalisista affordansseista. Äärimmillään peräkkäisyys digitaalisen osallisuuden tunnuspiirteenä merkitsee polkua digitaaliseen menestykseen tai syrjäytymiseen tehden siitä informaatioyhteiskunnassa tärkeän koulutuspoliittisen kysymyksen. Avainsanat: Digitaaliset affordanssit, digitaalinen eriarvoisuus, koulutus, koulutusvalinnat, sukupuolittuneisuus UNIVERSITY OF TURKU Faculty of social sciences Research Unit for the Sociology of Education Sociology of education MERI-TUULIA KAARAKAINEN: Education and Inequality in Digital Opportunities – Differences in Digital Engagement Among Finnish Lower and Upper Secondary School Students Doctoral Dissertation, 197 pp. Doctoral Programme of Social and Behavioural Sciences November 2019 Abstract The purpose of this work is to broaden the debate on digital technology in education by emphasising the digital affordances enabled by these technologies instead of focusing on the integration of digital devices and learning materials and digital pedagogy into educational practices. Digital action potentials are not equally open to everyone, requiring the scrutinisation of digital inequality as a relative issue limiting the abilities of individuals to benefit from these opportunities. In the context of education, this dissertation concentrates on the social structures affecting the unequal distribution of digital engagement which determines individual's positioning in relation to digital affordances. These theoretical backgrounds construe the following research questions: To what extent do social structures, specifically gender, age, and educational choices, determine the digital engagement of 12–22-year-old Finns? And, to what extent and in what ways does digital engagement accumulate, as exhibited by certain individuals more than others among Finnish lower and upper secondary school students? An empirical part answering these questions consist of five original articles utilising two samples of Finnish lower and upper secondary school students. In total, the 11,820 students' digital usage habits and digital skills are analysed through multivariate statistical methods. Gender as a social category appears to be producing differences in students’ digital engagement. The results indicate that gender differences in digital engagement among Finnish lower and upper secondary school students are largely domain-specific and related to gendered preferences and interests. In other words, tendencies towards the ways of experiencing digital technology and potential digital affordances appear to be gendered. Because the patterns of these preferences appear clearly in the data concerning lower and upper secondary school students, they are likely to develop during the early years of childhood and youth. Age, even among young people, has an impact on both digital skills and usage. The importance of age as an independent variable is explained by the increasing versatility of students’ use of digital technology as they grow older. It is the diversity of digital experiences, in particular, that enriches young people's digital skills. Education appears as the most significant single factor producing differences in young people's digital engagement. Education manifests itself as a categorical social hierarchy as the level of education increases the digital engagement. At the same time, there are significant differences in digital engagement within the same educational level, and digital engagement is generally most likely exhibited by students in the male-dominated fields of education. In particular, genderedness is present in relation to students' views of the ICT as a tempting field of education or profession in the future. As both students’ orientation towards technology and their educational choices are heavily gendered, they reinforce each other and increase gender gaps in relation to digital engagement and potential digital affordances among the future citizens of the information society. Overall, the current study emphasises the need of sociological scrutinisation in order to understand the importance of digital technology and related social activities in the context of education. The results of this dissertation indicate that gender, age and gendered educational choices determine the digital engagement of young Finns. Digital engagement tends be exhibited by certain individuals as skills and usage are intertwined and mutually reinforcing. It is evident that compound and sequential dimensions distinctively describe the digital engagement of Finnish lower and upper secondary school students. Where comboundness characterises the accumulation of digital engagement for certain individuals, sequentiality increases the likelihood that these individuals will also benefit most from the available digital affordances. In extreme circumstances, sequentiality of digital engagement describes the path to either digital prosperity or exclusion making it an important educational policy issue to be acknowledged in the information society. Keywords: Digital affordances, digital inequality, education, educational choices, genderedness Acknowledgements First of all, I thank my supervisor, professor Osmo Kivinen, who gave me unrestricted academic freedom to do my research and learn from my own missteps on this meandering path. A special thanks go to his guidance on how writing should serve as a tool for thinking. To achieve this, my journey will continue after the dissertation is completed, but the goal is clear. I am indebted to adjunct professor Sakari Ahola, who offered encouraging feedback on the manuscript in its final stage. Furthermore, I am sincerely grateful to the reviewers of this dissertation, professors Päivi Häkkinen and Petri Nokelainen, who thoroughly evaluated this work. Their feedback helped me finalise my dissertation and complete this journey. Special thanks go to my co-authors, professor Osmo Kivinen, researchers Teija Vainio, Loretta Saikkonen, Suvi-Sadetta Kaarakainen, Antero Kivinen and data specialist Juho Savela, for their contribution to the writing of the original articles of my dissertation thesis. Alongside the co-writers, I owe thanks to my colleagues at the RoSA-lab, Juho Savela, Olli Jalonen, Iiro Jalonen and Heikki Hutri, for their contribution to the technical development of the ICT Skill Test applications, which are an integral part of my research – thanks to them for their work and especially their enthusiastic and agile attitude. Thanks also to RoSA-Lab’s systems analyst Jouni Haltta for maintaining our development and production environment during those years. Thanks to researchers Loretta Saikkonen, Heikki Hutri and Teija Vainio for their contribution to the content of the test instruments, and to visual designers, Katri Kuusisto and Sari Vieri, for the user interface design. Special thanks to the members of the steering group of the ‘Comprehensive Schools in the Digital Age’ project, led by the Ministry of Education and Culture: Tero Huttunen, Mika Puukko, Juho Helminen, Jenni Kellokumpu, Olli Vesterinen, Riikka Savolainen, Sanna Vahtivuori-Hänninen, Pauliina Kupila, Jarmo Viteli, Erika Tanhua-Piiroinen and Suvi-Sadetta Kaarakainen – I am grateful for all the feedback and rich discussions during the project. My colleagues at the Research Unit for Sociology of Educaton (RUSE) enabled a large amount of data to be collected, as Loretta Saikkonen, Heikki Hutri, Marjut Muhonen, Sonja Hyrynsalmi and Suvi-Sadetta Kaarakainen have done a great job recruiting schools for research, applying for research permissions, and visiting schools to collect the data before this laborious process could be automated. Thank you all; your work has given me valuable data to explore so far and will allow for further research for a long time to come. I especially like to thank researcher Loretta Saikkonen for meticulously reviewing many of my manuscript versions, and mister Timo Savela, MA, for the final proofreading of my English. Thanks also to senior researcher Päivi Kaipainen, for all the varied practical assistance along the way. I definitely want to thank my children, Myrsky, Milja, and Usva, having taught me to distinguish between the necessary and the insignificant – and to focus on the first mentioned – and who tolerate the occasional mental absence that mother's research work from time to time requires. Finally, Marko Lenkkeri, my beloved husband. Not only have you given me the opportunity to concentrate on my research in my spare time, in addition to full-time work, but you have also contributed to my research by commenting and proofreading countless versions of my articles during your own moments of leisure. I am sincerely grateful to you for all this. However, you deserve the most recognition for the world you have opened for me to experience and explore – meaning computer games and gaming communities where digital engagement and learning take place in a ways that have been, and still are, well worth exploring. Turku, 1.11.2019 Meri-Tuulia Kaarakainen Table of Contents 1 Introduction.........................................................................................................1 2 Digital Technologies in Education......................................................................9 Varying Expectations for Digital Technologies in Education........................8 Affordances of Digital Technology in Education.........................................14 3 Inequality in Digital Opportunities..................................................................18 Relational Approach....................................................................................20 Conceptual and Empirical Viewpoints to Digital Inequality........................24 Gender Gap........................................................................................27 Age Divide.........................................................................................29 Educational Polarisation.....................................................................31 4 Research Questions...........................................................................................32 5 Research Methodology......................................................................................36 The Measurements.......................................................................................36 Participants of the Tests...............................................................................42 Sample I.............................................................................................43 Sample II............................................................................................44 Analysis.......................................................................................................45 Data preparation.................................................................................45 Description of Multivariate Analysis.................................................46 Long-term Preservation of the Data and the Instruments.............................51 6 Results................................................................................................................54 Digital Engagement by Gender....................................................................55 Digital Engagement by Age.........................................................................57 Educational Choices and Digital Engagement.............................................58 Accumulation of Digital Engagement..........................................................61 7 Conclusion.........................................................................................................65 List of references..................................................................................................74 Appendices............................................................................................................97 Original Publications.........................................................................................105 List of original publications Article I Kaarakainen, M.-T., Kivinen, O., & Vainio, T. (2018). Performance-based testing for ICT skills assessing: a case study of students and teachers’ ICT skills in Finnish schools. Universal Access in the Information Society, 17(2), 349–360. DOI: 10.1007/s10209-017-0553-9 Article II Kaarakainen, M.-T., Saikkonen, L., & Savela, J. (2018). Information skills of Finnish basic and secondary education students: The role of age, gender, education level, self-efficacy and technology usage. Nordic Journal of Digital Literacy, 13(4), 56–72. DOI: 10.18261/issn.1891-943x-2018-04-05 Article III Kaarakainen, M.-T., Kivinen, A., & Kaarakainen, S.-S. (2017). Differences between the genders in ICT skills for Finnish upper comprehensive school students: Does gender matter? Seminar.net International Journal of Media, Technology & Lifelong Learning, 13(2) [online]. Available from: https://journals.hioa.no/index.php/ seminar/article/view/2304/2132 Article IV Kaarakainen, M.-T., Kaarakainen, S.-S., & Kivinen, A. (2018). Seeking adequate competencies for the Future: Digital skills of Finnish upper secondary school students. Nordic Journal of Science and Technology Studies, 6(1), 4–20. DOI: 10.5324/njsts.v6i1.2520 Article V Kaarakainen, M.-T. (2019). ICT intentions and digital abilities of future labor market entrants in Finland. Nordic Journal of Working Life Studies, 9(2), 105–126. DOI: 10.18291/njwls.v9i2.114803 0 1 Introduction Over the last thirty years, digital technology has reshaped the foundation of industrial production, business and civic participation throughout the world (e.g., Phillips 2016, 1–4; Castells 2012, 4). Along with economic-productive restructuring, a much more profound change has been the digitalisation of social life reshaping the everyday life around digital communication and technology- based infrastructures (Brennen & Kreiss 2014; Castells 2010, 389). According to Jan van Dijk (2012a, 22–23), information society is an apt concept for modern society penetrated by digital technology and characterised by high levels of exchange and use of information. The economy of information society builds on increased information production; most of the workforce is employed in positions that require information processing and the culture of the society is permeated by information products. Furthermore, Manuel Castells (2010, 17) has introduced his concept of informationalisation as a main source of productivity and power, characteristic for the informational mode of development where knowledge itself is the main source of productivity. According to Christian Fuchs (2011), theories conceiving that the technological development in the last few decades constitutes a radical societal change are so- called discontinuous theories, whereas others stress the continuities of society. The key viewpoint behind the discontinuous information society concepts is that society and the economy have faced a thorough transformation that has given rise to a new society or economy. Regarding the informational aspects, there are subjective theories emphasising individuals and their actions in society and objective theories stressing the importance of social structures. (Fuchs 2011, 77– 78.) Discontinuous concepts include a famous assumption that the digital technologies and the Internet appear to produce something more than just a new artefact, a communication channel or a platform; they are expected to provide something exceptional, and that is exactly what is believed to justify their in-depth investigation (Sandvig & Hargittai 2015, 18–19). In the early days of digitalisation, many argued that digital technology implies that “something new, different, and 1 (usually) better is happening” (Woolgar 2002, 3) combined with the “pervasive sense of leaving the past behind” (Murdock 2004, 20). Steven Woolgar (2002, 3) states that the idea of virtual society includes the perception of a wide range of epithetised visions about technologically transformed futures, all suggesting “a major and profound change”. However, as Martin Ford (2015) demonstrates, recently the debate around a more prosperous future has been accompanied with fears of mass unemployment and concerns towards artificial intelligence and algorithms. Neil Selwyn (2010, 7) accuses both the popular and the academic stance towards digitality for tending “to be informed by a notion that the development of digital technology represents a distinctively new and improved set of social arrangements in relation to preceding ‘pre-digital’ times”. Instead, technological development in Western countries has reached a point where the ubiquitous digital technologies have penetrated our everyday life making the technology invisible. Nearly everything in our daily life at home, at work or at school is digitally transmitted, including the ways how people observe the world, communicate with each other and transact with the public and commercial actors. (Lupton 2015, 2–4.) Thus, the information society does not have a separate ‘desirable virtual’ and ‘old- fashioned real’ existence, but consists only of presence fulfilled with ubiquitous digital technologies. Along with the spread of digital technologies, researchers from many fields of science have sought to conceptualise skills needed in information society leading to the wide-ranging terminology used to describe these skills (Litt 2013, 613). Technology dominated concepts like information technology (IT), information and communication technology (ICT), or computer literacy became prevalent along with the spread of technologies (e.g., Bawden 2008, 29). Nowadays such concepts as digital competence, digital skills or 21st century digital skills are seen as describing the nature of modern technology and its user requirements more comprehensively (van Laar et al. 2017, 585). It was recently recommended that the concept of digital skills ought to be used when referring to the skills required in the digitalised society (Scheerder, van Deursen & van Dijk 2017, 1614). Consequently, in this study, the term digital skills is seen as providing the apt conceptual tool among alternative concepts for examining these vital competencies in the information society. In the context of education, the information society and its economy, as well as future skills requirements produced by them, raise important issues to be considered. The knowledge base of the modern information society is build on new ways to produce skills and knowledge. Due to technological development and 2 occupational restructuring, many jobs and entire sectors which are currently considered central for economic growth will no longer exist in the future. The changes in the information society require constant updating of the skills of the workforce, and, in particular, the speed of technological development imposes great demands on competence development. Still, more important than technology itself is the world of international work, where learning and practice will become increasingly blended, not merely because of the changing demands of the labour market, but also because workers themselves need to respond to changing needs. (Teekens 2016, 32.) For van Dijk (2005, 162), information is a positional good; some societal positions offer better opportunities for acquiring digital skills than others making digital engagement increasingly important factor in individuals positioning in the contemporary society. As van Dijk (2005, 144 and 162) stresses, an important feature of digital skills is that the level of inevitable skills for citizens to cope with is constantly rising. In this study this is seen as creating a new kinds of requirements for the education system and exposing individuals to risks of digital exclusion. The new skill requirements arising from technological innovations have implications to educational policy. A topical example is software competency which has grown in importance in many Western countries due to the restructuring of the ICT industry and the rise of such innovations as cloud technology, artificial intelligence and data analytics. As a result, not only sufficient basic digital skills, but also computational thinking and coding have been assimilated into a set of skills required from the future labour market entrants (e.g., Bocconi et al. 2016, 6). As a consequence, in several countries, computer science has been introduced to primary and secondary school curricula with aims to promote computational and algorithmic thinking, teach problem-solving and basics of programming, and familiarise children and young people with career paths that the STEM (Science, Technology, Engineering, and Mathematics) field professions have to offer (e.g., Hubwieser et al. 2015). Technology skills have even gained the reputation of omnipotency; in the United Kingdom, for example, the metaphor of the “pipeline to prosperity” has been adopted into the governmental policy discourse where it is used to define the economy as a machine that “feeds on a fixed, constant supply of digitally up-skilled youngsters” (Davies & Eynon 2018, 2). Although there is a common understanding that in order to increase economic competitiveness in the information society, human capital is one of the key strategic resources for success (e.g., Jin & Cho 2015, 259; Livingstone, Papaioannou, del Mar Grandío Pérez & Wijnen 2012, 6; Sahlberg 2006, 259), scholars from the field of sociology of education have a long ago raised critical 3 considerations about technological determinism (e.g., Robins & Webster 1989, 2– 5) which has gained a foothold in educational policy and in language related to digital technology. Linda Castañeda and Neil Selwyn (2018) argue that framing digital technologies in education in terms of their association with learning separates the technology from its wider contexts, narrowing its role to a mere learning tool. This kind of vision not only neglects the political, economic and cultural aspects of the technology being used, but also obscures the role of socialisation, subjectification and qualification in education (Castañeda & Selwyn 2018, 2.) Such stance toward technology has had a significant impact on the terminology in the field of education. An illustrative example of this kind of language, obscuring the functions of technologies in education, according to Selwyn (2015, 7), is the talk about learning management systems which in fact support rather the management than learning itself. Similarly, the concept of virtual/personal learning environment in fact incorporates a wide range of features from content production to certification, which in practice mainly support the functions like material production and delivery, and student management. Such inaccurate conceptualisations ignore the profound changes in requirements and opportunities that digital technologies enable in society, labour market and social life. Therefore, in this work, such conceptualisations of digital technologies in education are avoided and they are rather referred to as key enablers or targets for valued skills for citizens of the future. This dissertation thesis belongs to the field of sociology of education. The reason for choosing the topic of the work originates in Neil Selwyn’s and Keri Facer’s (2014, 485) notion that despite the invasion of digital technologies into society and all levels of education, researchers of sociology of education have largely been missing from the research and the debate around the matter. Selwyn (2013, 197) argues that sociologists aiming to achieve a comprehensive picture of the dynamic nature of digital technologies in everyday life in the information society have sought it mainly elsewhere than in education. This tendency has been strengthened by the fact that the grand narratives dominating the sociology of education have typically gone a long way round the issues like technology. In fact, while micro-level sociologists in the field have been interested in issues like inequality, resistance, identity and culture describing the processes of educational practices, the macro-level researchers have focused on issues related to social mobility along with the stratification of educational opportunities and outcomes. (Selwyn 2013, 197; Delamont 2000, 96–98.) As a major consequence, the lack of sociological interest has left the area becoming dominated by education scientists and psychologists, alongside the 4 strong dominance of technology vendors, with the intention to understand the effects and increase the use of technology in learning. Absence of the sociology of education has left much room for technocratic discourses of effectiveness and best practices. These approaches have promoted the individualisation of learning as the engagement with most learning tools mainly necessitates abilities to self-direct one’s own learning which is assumed to work in favour of the individual’s learning outcomes. (Selwyn & Facer 2014, 485–488.) In this work it is assumed that such individualistic views on the opportunities of digital technology in education are not advantageous for individuals nor for the education system as they ignore both the wide-ranging potentials and the risks of digital technology that go beyond education. For these reasons, this work promotes a broader understanding of digital technology in education, with particular emphasis on the importance of digital skills in students' future life opportunities, rather than the mere use of digital technology in schools. According to Keri Facer and Ruth Furlong (2010, 451–452), in developed countries the rapid expansion of digital technologies into areas of social, economic, and personal life have made information poverty a key indicator of social exclusion as it refers to being excluded from information loops which connect individuals to, for example, jobs and social networks. As van Dijk (2012b) reminds, in the early stages of the information society, in the time of arrival of the Internet, it was widely assumed that digital technology would enhance digital democracy in the society. Digital technologies, especially the Internet, were seen as an interactive medium that would depart users from one-sided communication of mass media and transform them from viewers to participants, ensuring equal opportunities and acceptance, and promoting collective creation of online products, instead of strengthening the role of corporations. (Van Dijk 2012b, 49.) The actual situation has, however, proved to be more complex than expected; technology has undeniably provided new opportunities, but it has also brought more or less unexpected obstacles to democratisation. Jan van Dijk and Kenneth Hacker (2018, 208–210) emphasise that the question whether the digital technologies do strengthen or weaken democracy is not a binary question, and in fact, at present, no one knows how the story is going to end. The aspirations of the democratising power of technology are related to the desire to eliminate social exclusion in the information society. Social exclusion is described as a complex and multi-dimensional process involving a lack of resources, rights, goods and/or services (Levitas et al. 2007, 9). Often it is also a question of inability to participate in the normal relationships and activities which are available to the majority of people in the society. All this affects the quality of 5 life of individuals and the equality and cohesion of the society as a whole. Above mentioned definition stresses the idea that social exclusion as a concept is broader than poverty, as it embraces issues of denial of rights and lack of participation. (Saunders, Naidoo & Griffiths 2007, 12.) Social exclusion in the information society has been conceptualised as digital exclusion, divide or inequality. In this study, the term digital inequality is used as it emphasises the relational nature of differences related to digital capabilities. Digital inequality is connected with fast changing environment; the specific feature of digital technology is that it becomes outdated much faster than other technologies or traditional media, which forces users repeatedly to catch up with the latest technology in order to avoid lagging behind technological development and its opportunities (van Dijk & Hacker 2003, 316). This brings a fundamentally new kind of demand for individuals and for the education system. Education should not only provide the skills required for information society, but also make it possible to reflect on what actually happens in the rapidly evolving technological environment and to make agile corresponding changes to curricula in order to adapt to the technological advancement and maintain its relevance as well as prevent the exclusion of the least prepared individuals. This dissertation thesis examines to what extent social hierarchies, structured by gender, age and education, effect digital engagement among Finnish lower and upper secondary school students and in what ways some individuals come to exhibit more digital engagement than others. The aim of the work is to open and widen the limited discussion about the significance of digital technology in education by elaborating both the demands and opportunities of digital technology to education of citizens for the future information society. In the theoretical part of the study, the aim is to find apt linguistic tools for conceptualising and for further examination of the topic. Chapter 2 focuses on the general expectations toward the digital technologies in education. The goal is not to introduce, and especially not to commit to, any particular learning theory, but rather to look at some current trends in the technologicalisation of education from the perspective of sociology of education. Chapter 2 presents also an alternative perspective to technocratic discourses on the digitalisation of education, by seeing digital technologies through the affordances they provide in education and wider in society. This perspective is important in this work, as one of the key objectives of this dissertation thesis is to promote discussion about the role of education in providing the skills necessary for capitalising the opportunities offered by digital technologies in the social, economic and personal lives of individuals. 6 In addition to emphasising the perspective of digital affordances, one of the aims of this work is to raise awareness of the importance of digital inequality in education and its negative impact on students' future opportunities in further education, labour market and in life in general. Digital inequality is the theme of Chapter 3 which introduces concepts and central theories, as well as recent empirical evidence of digital inequality. It is examined, in which ways the digital technologies and related capabilities will expose individuals unevenly to various potentially beneficial opportunities. Inequality of the opportunities is manifested as different possibilities to engage with and benefit from technology, in various domains of life, relating to differences in skills and usage. According to Maria Bakardjieva (2006, 74), different kind of engagement with digital technologies, rooted in social relations, creates the meeting point where micro-practices and macro-structures encounter; digital engagement effectuates the individual's action in relation to social arrangements in society determined by patterned relations between social structures, such as socio-economic stratification or social institutions. For this reason, digital usage and the related skills are the main research objects of this study. When examining the digital skills, this dissertation thesis applies the categorisation of medium- and content-related skills by Jan van Dijk and Alexander van Deursen (2014). This rather robust classification offers an applicable framework to explore these skills over different studies, instruments and changing technological milieu, which has proven to be a challenge for many previous studies in the field due to the conceptual ambiguity. Correspondingly, for the same reason, when focusing on digital usage, Helsper's (2012) classification is utilised, and individual online activities are condensed according to their purpose into economic, cultural, social and personal usage domains. It is commonly thought that digital skills enable the use of digital technologies. In this study, this connection is understood alternatively: digital skills are seen as evolving in digital usage. The difference is, however, largely artificial as digital usage and skills are predominantly intertwined; digital skills have no purpose without usage, as only the intentions of using technology for some purpose make these skills topical and meaningful. The term digital engagement, deliberately instead of participation, is used to describe this inseparability of usage and skills when confronted with technology and the services and communities building on it. Digital engagement, like engagement more generally (see Fredricks 2011, 328), is considered to consist of three dimensions, including behavioural commitment, such as attendance and participation, emotional commitment, such as a positive attitude, and cognitive commitment describing an individual's level of investment in learning the skills 7 needed for particular engagement characterised by self-regulation and strategic behaviour. Engagement is more than mere participation as it bears the potential to transform people. Chapter 4 draws together the theoretical standpoints of the study and refines the research goal into research questions concerning digital engagement in educational context. The research questions that structure the empirical part of this dissertation are: To what extent gender, age, and educational choices and future educational intentions associate with digital engagement among Finns aged 12–22? And, to what extent and in what ways does digital engagement accumulate among Finnish lower and upper secondary school students? Chapter 5 describes the research processes, the methodology and the data of the five original articles of this dissertation thesis. In these articles, digital technologies provide a platform for collecting and processing the empirical data, but above all, ubiquitous digital technologies are seen as penetrating the social and cultural relationships, which are the actual object of this research. In essence, this work is primarily about human activity in the context of digital technology. Chapter 6 presents the core results of the five original articles from the point of view of the research questions. The research questions are answered individually by combining the key results of the original articles included in this work. Finally, Chapter 7 concludes the work; the results, based on the original articles, will be discussed in relation to prior research and current public debate, as well as the policy implications the results give ground for. 8 2 Digital Technologies in Education Varying Expectations for Digital Technologies in Education Since the 1970s, the benefits of technology-based education have been widely discussed (Kulik, Kulik & Bangert-Drowns 1985; Alpert & Bitzer 1970). Alongside with this debate, the social problems caused by computerisation and automation have also been articulated (e.g., Beynon & Mackay 1988; Ofner 1985). On international level, many global organisations have been highlighting the potential benefits of digital technologies in education, emphasising their impact on global equality, development opportunities and economic growth (e.g., UNESCO 2017; UN 2005; World Bank 2003). For example, in the report of Students, computers and learning by The Organisation for Economic Co-operation and Development (OECD 2015, 124), digital technologies were presented as providers of material, cultural and cognitive resources which promote opportunities for civic participation, networking and improving productivity in work. In Finland in 2015 the digitalisation of education gained a major role in the strategic program of government (Prime Minister’s Office 2015, 18). In this strategic program it was expected that digital learning environments and the new pedagogical approaches they promote would not only nourish the favoured skills and future knowledge base, but also enhance lifelong learning, decrease the dropout rate in education and encourage the equal opportunities and overall renewal of Finnish society. Concurrently, the curriculum reform in both basic and secondary education emphasised the importance of information technology and new media (multi-literacy) skills in Finnish education (FNBE, 2016a; 2016b) which led, for example, to a desire to increase the use of digital devices and learning materials in schools and brought programming to common basic education. The same trends are also visible in the international context, especially in developed Western countries, where teachers have been attracted to accept the digital technology into teaching through the advertising campaigns sponsored by technology vendors and with the support of political actors. Under this influence, 9 on a micro-level, schools have invested in computer-assisted and technology- enhanced learning (e.g., Phillips 2016, 4–6) and more lately in algorithm-driven technologies of personalisation and educational data science (e.g., Williamson 2017a, 105) in order to meet the current expectations and to improve the educational outcomes. However, education has encountered serious obstacles in harnessing technology as a resource for achieving the desired transformation. According to Selwyn (2013, 202), there have been high expectations about how education is going to change with technology, but the actual change has not been realised. Diana Laurillard (2008, 1) has incisively noted already a decade ago that “education is on the brink of being transformed through learning technologies; however, it has been on that brink for some decades now”. The OECD (2015, 3), which firmly believes in the power of digital technology, states that schools all over the world have fallen considerably behind the promise of technology. It has been claimed that there exist a disjuncture between the rhetoric and the reality, and between the policy and the practice in pedagogical use of digital technologies (Phillips 2016, 7). In addition to this disjuncture, Selwyn (2013, 200) claims that the actualised digital reformation of education, promoted by learning technologists, has predominantly implied a reshaping of the educational practices around the individuals without wider transformative power. Castañeda and Selwyn (2018, 5) argue that one of the adverse factors in contemporary education is the hyper-individualisation of education. Quite often, when aiming to improve participation and achievement rates in education, solutions have been sought from functionalities of technology which enable to customise learning for the needs of an individual learner and from the flexibility of technology that allows the individuals to learn at their own pace, in their own time and from changeable locations. Castañeda and Selwyn (2018, 5) claim that the most prevalent forms of digital technology in education are based on a vision of individual students' responsibility for their own learning and the consequences of their learning-related choices; individuals are expected to become industrious self- improvers, driven by external goals and striving to improve one’s own performance. Such ideas of learning are rooted in values of neo-liberalism and surveillance, economic productivity and competitive entrepreneurialism and require increased self-determination, entrepreneurial spirit as well as the ability to self-engage in preferred technology to achieve the educational goals (Kuntz & Petrovic 2018, 68). Hyper-individualisation in education goes hand in hand with technology-driven commercialisation of education (Castañeda & Selwyn 2018, 6) building on a 10 behaviourist view of learning which implies that human behaviour can be manipulated and adjusted through the design of digital architectures (Williamson 2017c). According to Rob Creasy (2017, 7) such process is likely to reduce the professional expertise of teachers as well as rewarding learning and originality of student’s own activity. Technology-led individualisation of learning includes not only opportunities, but also significant risks and burden for individual students as e-learners are assumed to be self-motivated, independent, and diligent workers taking responsibility for their own learning (e.g., Nunes 2006, 133). Although the efforts to improve educational outcomes are based on good intentions, they have enhanced a technology-led personalisation and self-determination of education by means of digitising and datafying education (Williamson 2017b; Selwyn 2013, 200; DfES 2002, 4). Data science approach in education promotes knowledge production and theory generation reflecting the professional mindset typical for the data science. This mindset is not neutral, value-free nor atheoretical; it sees learning in scientific terms emphasising quantifiable, measurable and consequently optimisable nature of human action. (Williamson 2017b, 119.) Castañeda and Selwyn (2018, 3) warn that pedagogies of technology-based education have often been taken for granted and can not be negotiated. Undoubtedly this is true in many cases because traditional education professionals lack understanding of algorithms that underpin the modern learning systems. Therefore, there is a great danger that this will lead to a situation where there exists an absence of pedagogical insight to the digital technology used in education (Williamson 2017c). According to Williamson (2017c), the fact that education researchers should understand is that numerous issues related to teaching, training, learning and pedagogy, have been outsourced to technology vendors for the sake of the digitalisation of education. As a result, “engineers, data scientists, programmers and algorithm designers are becoming today’s most powerful teachers, since they are enabling machines to learn to do things that are radically changing our everyday lives” (Williamson 2017c). A general educational policy atmosphere is in fact implicitly supporting this trend, because, as Williamson (2017b, 119) argues, the power of educational data scientists over the educational field is secured by the fact that these actors are able to provide the data-driven explanations required by a current accent on evidence-based policy. It is an interesting question to ask, whether there is evidence that technology- enhanced learning has led to positive results or favourable changes in education? In the OECD report (2015, 162), based on PISA 2012 data, it was claimed that there exists only a weak and sometimes negative association between investments in digital technology and students’ performance. The same report (OECD 2015, 163) 11 continues, based on both PISA data and the wider research evidence, that the positive effects of technology are at best "limited to certain outcomes, and to certain uses of computers". This confirms Selwyn’s (2013, 202) allegations that no striking change or significant improvement in learning outcomes, commitment to education or equal opportunities has occurred as a consequence of adopting digital technology into education. Therefore, expectations about the liberation of education from time, space, place and material constraints or increasing diversification of learning opportunities due to diffusion of digital technologies seems to have been unfounded so far. The following paragraphs examines the above-mentioned claims by considering the experiences from Massive Open Online Courses (MOOCs) which are one example of learning innovation that has aroused a great deal of hope for diversifying education and overcoming constraints related to temporal, spatial or material access. Despite the good intentions, it is unclear whether online courses, such as MOOCs, actually provide a solution to these user constraints. At first, most online learning contents present severe accessibility barriers for learners with special needs due to their inept interface design (Sanchez-Gordon & Luján-Mora 2016). This actually poses significant challenges not only for learning but generally for online content, networks and services in all areas of life, which is why the European Commission has set the European Accessibility Act (EC 2015) aimed at reducing barriers for people with disabilities within the area of European Union. In addition, as MOOCs cater for an increasingly internationally audience, they place cognitive barriers on the non-native language users due to the expected high level of proficiency in the course languages (Sanchez-Gordon & Luján-Mora 2015). The most severe criticism of these learning products, contrary to the ‘education for all’ claims attached to the MOOCs, is that according to previous studies, the users engaging in these kinds of digital learning products are mostly people with already priviledged socio-economic status (e.g., Castaño-Muñoz, Kreijns, Kalz & Punie 2017; Rohs & Ganz 2015; Hansen & Reich 2015). Succeeding in online learning is associated with higher knowledge capital such as strong background knowledge, technical skills, strong reading, writing and typing skills combined with adequate (online) communication abilities (Castaño-Muñoz et al. 2017; Khalil & Ebner 2014). At the same time the influential factors for insufficient performance with digital learning products consist of lack of time and motivation, lack of possibilities to interactivity and related feelings of isolation, but also the hidden costs associated with seemingly free learning products (Khalil & Ebner 2014). Under these circumstances, the kind of self-determination required by self- 12 directed digital learning is simply too demanding for many individuals, especially for children and young people and those with a low socio-economic background. In addition to the above-mentioned, as Selwyn (2013, 202) points out, digital technologies seem to have little or no effect on the lack of interest to engage in education as it is primarily associated with non-technological socio-economic issues, such as the employment situation and intergenerational inequalities. It is therefore deceptive to profess that the use of digital technology in education would re-configure issues like non-engagement in education. In fact, high dropout and low engagement rates are a major concern particularly among online courses (e.g., Bozkurt, Akgün-Özbek & Zawacki-Richter 2017, 137). The study conducted by Helen Thornham and Edgar Gómez Cruz (2016) provides another example of the constrains of technology optimism; the use of mobile devices among young NEETs (Not in Education, Employment or Training), contrary to the opposite expectations, has been shown to have failed in empowering marginalised groups. Researchers remind that, like all technology, “mobile phones are not free from sociocultural, political and economic power structures, and any mobility or agency they may offer the user is momentary, contentious, negotiated and ambivalent” (Thornham & Gómez Cruz 2016, 1805). Peter Stevens, Marious Vryonides and Gary Dworkin (2018, 513) recently listed contemporary issues in education: educational inequalities between social classes and ethnic groups and the impact of the evidence-based accountability movement in education. Race, class and educational opportunities have been the central focus of interests in the sociology of education already for decades (see e.g., Epps 1995). One central phenomenon of education has been the global expansion of higher education, which has not eliminated the significant correlation between low social origin and modest level of education (e.g., Guetto & Vergolini 2017, 1). In many developed countries, gender inequality has not disappeared, but the traditional gap referring to the under-representation of women in education, has transformed to a ‘new gender gap’ in which the proportion of women in higher education has risen above men in many areas of education (e.g., Klesment & van Bavel 2017). However, on a global scale, gender equality continues to be linked to ethnic and class-based discrimination and human rights in general (Schofer & Meyer 2005; 909 and 916). The diffusion of digital technology in education has not changed these major trends (see Selwyn 2013, 202–203). Instead, it has re- increased their relevance as the digital environment tends to reproduce traditional inequalities and divides as will be discussed in more detail and in a more wider context in Chapter 3. 13 Affordances of Digital Technology in Education However, the aforesaid does not mean that digital technology has nothing to give for education. Quite opposite, but the importance of digital technologies for education should be seen from the point of view of what ubiquitous digital technology provides to the students’ future opportunities in the realm of lifeworld where all human activity takes place. This leads to face the idea of digital affordances, as it offers a theoretical concept that enables the conceptualisation of the wider meaning and impact of digital technology in education and in individuals' lives. Concept of affordance, by James Gibson (1986; 1977), has been originally used in ecological psychology to assign the latent action opportunities held by the particular environment or situation. For Gibson (1986, 127), affordances “are what it offers the animal, what it provides or furnishes, either for good or ill”. According to Gibson the affordances are unique for the subject and should be measured relative to the subject; affordances “have unity relative to the posture and behavior of” the subject being considered (Gibson 1986, 127–128). Affordance are thus a relational property, meaning that they refer to both the environment and the actors involved. However, among technologists, affordances have been considered as a much more limited concept. Typical for these views is seeing the potential of affordances in a narrow, physical interpretation referring so called low-level or technology based perspective on affordances (see e.g., Bucher & Helmond 2018) which treats affordances as properties of objects. Technology researcher Auke Pols (2012) calls basic visible action potentials, which he assumes being the lowest level of affordances, as manipulation opportunities. As people learn what the effects of their manipulations are, the effect opportunities emerge. The highest level of affordances for Pols are the use opportunities, referring to the possibilities that users can do with artefacts. (Pols 2012, 117–118.) This idea comes closer to the original Gibsonian meaning of affordances. Technology design researcher William Gaver (1996, 114) sees affordances as “primarily facts about action and interaction, not perception”. Gaver builds on Gibson’s relational model of affordances and argues that affordances are the properties of the environment that are defined in relation to the human interaction within it (Gaver 1991, 80). Thus, for Gaver, the affordances of the technological artefacts are not only visible, but also hidden and possible to detect through experimentation. Affordances do not exist only for individual’s action, but also for social interaction (Gaver 1996, 114; 1991, 80). Ian Hutchby (2001, 30) argues that affordance is a concept which rejects both technological determinism and strict social constructivism as it combines the 14 socially constructed and situated nature of digitality with the material constraints of technology. When considering digital technology in education more broadly than just as a learning tool, a wide-range of digital affordances opens up to education. As Wan Ng (2015) phrases, educational technology for learning, as defined as “consumption of information for conceptual development of subject matter”, represent only one available affordance for education. In addition to this limited instrumental affordance, digital technology provides a wide variety of opportunities for education, some of which are discussed below. Multi-modality, referring to the multiple modes of representing the information, allows simultaneous exploration, interpretation and production of information. Tools for information gathering and analysis, understood as tools for research, is an example of a more general affordance as it provides tools and experiences not only for school but also for the outside lifeworld. Similarly, communication, collaboration and sharing are wide- range affordances that cross the limits of school and outside world. Digital content creation and presentation also offer these kinds of wide-ranging affordances. (Ng 2015, 97–121.) Similarly, in the report of Futurelab (Fisher, Higgins & Loveless 2006, 3) the affordances of digital technology for education were seen as being based on knowledge building, distributed cognition, community and communication, and engagement. Both Futurelab’s report and Ng emphasise the opportunities that are associated not only with learning and schooling, but with the broader context of a modern information society. This kind of stance toward digital technology in learning is reminiscent of the discussion within socio-cultural theories which emphasise active and authentic learning in digital learning environments, especially in informal contexts. Participatory learning emphasises that a significant part of learning takes place in collaborative digital environments, as these informal networks take collective responsibility of building accumulative information. (Crook 2008, 31–33; Beer & Burrows 2007, 2.1.) In fact, according to Keri Facer (2006, 1), one of the most potential affordance of digital technology is their power to enhance and expand learning environments within classrooms and beyond schools. The important affordances of digital technology for education are also the numerous career opportunities it enables. Globally, there is a growing demand particularly for highly skilled workers in the ICT field (e.g., Falk & Biagi 2017a). In fact, digital fluency (Briggs & Makice 2012) is a prerequisite for all fields of economics as evidence indicates a growing demand for digital skills both in and outside the technology field (Berger & Frey 2016, 19). Simultaneously, ICT 15 professions are no longer restricted to traditional IT occupations, such as software, hardware and network related professions, as there are also new professions emerging in various fields related to Internet services, multimedia, e-commerce platforms or so-called user-related software development, such as e-learning, bio- informatics and electronic archiving (Chillas, Marks & Galloway 2015, 2). For example, like Sanna Rimpiläinen, Ciarán Morrison and Laura Rooney (2018, 15) remind, digital health sector is globally one of the fastest growing fields of economics. The prospects for the future labour market entrants enabled by digital technology are therefore more versatile than ever before. Nonetheless, engaging with various digital affordances related to social inclusion and personal enjoyment are often even more important for the overall well-being and participation in digital society than labour market prospects as they provoke types of usage having the most collateral benefits for individuals’ lives (van Deursen & Helsper 2018, 2345). However, opportunities are not limitless or open to everyone. Joshua McVeigh- Schulz and Nancy Baym (2015) proposed the term vernacular affordance referring to ways in which individuals themselves understand affordances they encounter with technology. Affordances exist simultaneously for people at multiple levels of technology (i.e., infrastructure, device, software, feature, etc.) creating the vernacular frame of material structure and practice. Vernacular affordance as a concept stresses the variability of affordances in ways in which different individuals emphasise different aspects of digital action possibilities. (McVeigh- Schulz & Baym 2015, 10–11.) Nina Bonderup Dohn (2009, 163) states that affordances are both dynamic and relational, but also culture-, experience- and skill-related. Building upon the Merleau-Pontian concept of the body schema (see Merleau-Ponty 1962), Bonderup Dohn argues that “affordances are the actionable meanings of objects for a particular agent and as such their existence must be determined relative to the body-schematic space of possible interactions for that agent”. Hence, the perceived and enabled affordances in a certain situation for a particular individual are associated with the knowledge, skills and action potential that this individual has acquired through accumulated experiences which have been physiologically, personally and socio-culturally achievable for the individual. Therefore, even in the same technological environment, individuals are not able to engage the same action potentials. (Bonderup Dohn 2009, 163–169.) Bonderup Dohn (2009, 161) emphasises the agency of actors as the affordances of an object are not perceived equally; individuals do not live in a world of their own mentalistic making, but the surrounding world transforms in congruence with what individuals learn to do in it implying an interdependency of the individual and the world. 16 According to Jenny Davis and James Chouinard (2016, 241), the concept of affordance possess a capacity to recognise technology as efficacious, still rejecting technological determinism. Digital affordances are the link between subjects and objects within digital environment. Davis and Chouinard (2016, 242) propose that artefacts “request, demand, allow, encourage, discourage, and refuse”. They do not appear to everyone in the same way, and neither consistently in every moment of the time: “[w]hat an artifact requests of one user it may demand of another; what the artefact refuses in one moment, it may later allow” (Davis & Chouinard 2016, 245). Davis and Chouinard (2016, 245) argue that the mechanisms of affordances mediate through material and social circumstances by perception, dexterity, and both cultural and institutional legitimacy. This means that the digital affordances vary between individuals depending on awareness of the function, skills and abilities to perform the function in practice, and available social support while execution. For Davis and Chouinard (2016, 245) the digital inequality research serves as an exemplar of this process, as it demonstrates the effects of skills and usage, and the mediating factors which affect the likelihood that artefacts produce (or not) outcomes such as increased competency, accumulated information, and more versatile digital engagement. 17 3 Inequality in Digital Opportunities Understanding digital technology in education in a broader sense than just comprehending it as a learning tool, practice or new pedagogy makes it inevitable to confront the issue of digital inequality. According to Christian Fuchs (2009, 46) digital inequality as a concept refers to inequalities in material access, usage capabilities, engagement, and the potential to benefit from information and communication technology. Due to continuous stratification, digital inequalities lead to differences between groups, which, in the extreme, leads to the gainers and losers of the information society (Fuchs 2009, 46). In the early stages of the study on digital inequality, the focus was mainly on inequalities in availability of technology (van Dijk 2005, 49–52; Selwyn 2004, 343–344), drawing a dividing line between those who were connected to information and digital technology and those who were not. The gap between the information haves and have-nots or the computer literate and illiterate is the result of two major divides: divide in access and divide in usage (Bélanger & Carter 2009, 132). Broadly speaking, these kinds of studies are said to represent the first-level digital divide research, primarily interested in material access (e.g., Friemel 2016, 312) and focused on the availability of hardware, software, applications and information networks such as the Internet (Fuchs 2009, 46). As more people gained access to the technology, their skills and diverse usage became the primary focus of research, as insufficient skills play a major role in digital inequality (van Deursen & van Dijk 2010a, 893–894). This phase became known in the beginning of the 21st century as the second-level digital divide due to being closely related to the differences in abilities to effectively use the medium and engage with online contents despite the spread of relevant technology (Hargittai 2002a). Usage and skills describe the capabilities needed for using digital devices and applications to produce meaningful online content and to engage in online communities (Fuchs 2009, 46). According to Alexander van Deursen and Jan van Dijk (2010a, 908–909), people's Internet skills, in particular, come to determine their positions in the contemporary society, not only in the 18 labour market, but also in social life. The second-level digital divide has therefore been thought to lead to a democratic divide where human interest and skills play important roles, as prerequisites for inclusion and participation in society (Min 2010, 32–33). The second level digital divide is also called the ‘production gap’ when describing the difference between the consumers and the producers of the content on the Internet as inequality in digital production has been said to cause domination by elite voices (Schradie 2011, 145). More recently, researchers have raised up the issue of a third-level digital divide which concerns the differences in tangible benefits that users gain from using digital technology and the Internet (e.g., Scheerder, van Deursen & van Dijk 2017; van Deursen & Helsper 2015). Research indicates that less privileged people are at risk of being excluded from the benefits of digital technology, such as gaining access to jobs or other economic opportunities, opening possibilities to maintaining their health, and political opportunities like participation or online services. This is a current issue in societies where users have relatively autonomous and unlimited access to both technology and the Internet and display relatively similar profiles of usage and skills. In other words, the question relates to differences in users’ ability to translate their time online into favourable offline outcomes. Research on this third-level divide therefore focuses especially on examining who will benefit the most from the use of the digital technology and in what ways. (Van Deursen & Helsper 2015, 30; van Deursen & van Dijk 2010a, 908–909.) Two theoretical approaches have strongly contributed to the digital inequality research. The first, the technology deterministic approach, focuses on the diffusion and acceptance of technology, relying on the idea that with the spread of technology digital inequality will eventually disappear or at least be largely mitigated. The spread of technology has been described with the theory of diffusion of innovations (Rogers 1983) known as the explanation for the technologicalisation and for the means by which innovations, products and services are spread and accepted or rejected in societies. The spread of digital technology has also been explained with the technology acceptance model (e.g., Davis 1993; Davis, Bagozzi & Warshaw 1989). In general, while the theory of diffusion of innovations does deal with the process by which a technological innovation is communicated through different channels in the society, the technology acceptance models focus more on individual’s decision-making through which innovations come to be adopted. Both the diffusion of innovations theory and the acceptance models can be regarded as technology-based approaches leading to a simple dichotomy between the ‘haves’ and ‘have-nots’, in other words, those who have been able to adopt the 19 latest technology and those who have not. Among technology determinists it is widely believed that technological progress will reduce social inequalities and allow more equal participation in society (e.g., Howard, Anderson, Busch & Nafus, 2009, 213). In this way, digital divide is seen as a problem of material access which is caused by bottlenecks in the diffusion of technological innovations and is therefore a temporary issue (Adriani & Becchetti 2003, 18–19). In this study, the technology deterministic approach described above is considered inadequate and over-simplistic to describe digital inequalities in modern society. Instead, another theoretical perspective, relational approach, is seen as offering more apt theoretical premises and linguistic tools to understand the phenomenon and its implications for the individuals than what is offered by the technology deterministic approach. The relational approach focuses on the association of digital inequality with the different forms of social inequality. From this point of view, digital inequality is not a phasing out phenomenon, as it occurs not only in societies with a low level of spread of digital technology, but also in highly technologically advanced societies. The next section examines the concept and research of the digital divide from the point of view of a relational approach, by looking at the domains and factors of digital inequality, as well as the essential empirical findings. Relational Approach Neil Selwyn (2003, 105) argues that the ‘natural diffusion’ thesis leads to a false assumption, that inequalities are a passing phase of technological adoption. From Selwyn’s (2004, 349) point of view, the dismissal of long-term significance of digital divide among technology deterministic approach is dangerous as it ignores the complex relationship between access and usage; the access to technology does not denote the use of it, neither does the use of technology necessarily involve productive or meaningful use. Instead, individuals’ engagement with technology is not determined by issues of physical access or adoption, rather it consists of a versatile combination of social, psychological, economic and pragmatic factors (Selwyn 2004, 349). Technology deterministic research deals with digital inequality as if it were binary in nature, differentiating between only two options, one representing negation or opposite to another variable (such as ‘haves’ and ‘have-nots’ or ‘skilled’ and ‘unskilled’). Digital inequality is not binary, but originally equivocal, plural, and varied (e.g., Gunkel 2003, 516). Where technological determinism assumes that socio-economic problems can be reduced 20 to technological or availability issues, a relational approach provides a better basis for research for conceptualising and understanding the issue. Jan van Dijk (2013, 30) reminds that the relational approach enables to differentiate between types of inequality as by drawing attention to the structures producing and maintaining inequalities. Van Dijk adopts Charles Tilly’s (1999) definition which describes inequality as unequal distribution of resources in society as a result of the competition between representants of categorical pairs such as male–female, skilled–unskilled or high–low educated producing social closure, exploitation and control. There are two causal mechanisms in behind categorical inequality: exploitation and opportunity hoarding. Despite the constant changes, the categorical pairs reproduce themselves through these mechanisms making inequality a structural feature of all societies. (Tilly, 1999, 7–9.) Moreover, Tilly (1999, 7) adds that “[l]arge, significant inequalities in advantages among human beings correspond mainly to categorical differences ... rather than to individual differences in attributes, propensities, or performances”. According to van Dijk (2013, 31), the relational approach does not require describing the priority of categorical pairs in advance because their relative importance is always formed in relation to empirical observations which produce different results for each country, society and societal unit under consideration. In addition, priority in terms of one type of pair does not guarantee the priority in the case of other types of pairs; an individual can simultaneously be on the better side of a digital divide with some of the pairs and stay on the opposite side with the others (van Dijk 2013, 31). According to van Dijk (2013, 47–48), the relational view of digital inequality stresses the role of skills and usage over physical access, as the former are strategically more important than the latter in contemporary information societies which are built on and linked by social and media networks (e.g., van Dijk 2012a, 31), relying strongly on information as a primary good (van Dijk 2005, 131). In this kind of society, the role of relational differences in possessing and controlling information is becoming increasingly important (van Dijk 2006, 231). It is imperative for information society citizens to be involved with information in at least a certain minimum, and this minimum will increase with the increasing complexity of the information society. To a certain extent, participation above this level leads to, for example, power, productivity, ownership, and identity, and the difference in these beneficial outcomes is the basis for inequality in modern society. Another origin of inequality in the information society is the information itself as it is a source of skills and, together with technology, is related to the uneven capacity of individuals. Thereby, within the labour market, uneven skills of individuals lead to increased knowledge-based divisions. The unequal nature of 21 information is emphasised in van Dijk's thinking as for him information is a positional good; despite the excessive increase in information in society, it is limited in certain circumstances and some societal positions enable better opportunities than others for engaging with valuable information. Thus, possessing particular position in social networks is increasingly dominating the status of an individual in the contemporary society. (Van Dijk 2005, 144 & 162). Pierre Bourdieu’s theories leaning on methodological relationalism have significantly influenced the language of digital sociologists. According to Gabe Ignatow and Laura Robinson (2017, 962), Bourdieu shifted social sciences toward a relational approach instead of variable-centred hypothesis-testing. They continue (2017, 962) that Bourdieu's ontological attitude combining moderate realism and social constructivism, offers useful conceptual foundation for empirical sociology. Bourdieu's relational sociology combines both objectivistic and subjectivist aspects of social action occurring in a social space by seeing an individual as an actor, without ignoring the structure of society (e.g., Kivinen & Piiroinen 2006, 315– 320). For Bourdieu, “the real is the relational” (Bourdieu & Wacquant 1992, 97) emphasising that every distinction is “a relational property existing only in and through its relation with other properties” (Bourdieu 1998, 6). Bourdieu (1977, 164) argues that individuals adjust their practices and choices according to the social realms. Social space is from Bourdieusian viewpoint a construction of unequally distributed capitals. It is made up of intersecting fields, understood as a network of relations between social positions. Individuals’ practices and choices are linked to the position they possess in society. This positioning is dependent on the overall amount of economic, cultural, social and symbolic capital they hold and the structure of this capital, but also the habitus they live through. Bourdieu’s concept of habitus is constructed by incorporated social habits of the field in question. It refers to the susceptibility of individuals to adjoin with certain types of social life, norms, values and linguistic habits related to their position which generate a system of schemes or tendencies towards the ways of perceiving, thinking, experiencing and feeling. It is both a system of schemes of producing practices and a system of perception and appreciation of practices. (e.g. Bourdieu and Wacquant 1992, 126−127; Bourdieu 1989, 17–19). According to Bourdieu (1989, 19), habitus implies “a sense of one’s place but also a sense of the place of others” and “through habitus, we have a world of common sense, a world that seems self-evident”. For Ignatow and Robinson (2017, 954), the concept of habitus, in particular, elaborates the applicability of Bourdieu’s work in the field of digital inequality. In her study, Laura Robinson (2009) gives an enlightening example of applying the 22 concept of habitus (in Robinson’s words, information habitus) which plays a key role in defining the ways of online engagement which become habitualised by individuals within particular social context. Two opposite stances towards the technology usage emerge as young people from upper-middle-income families gain more benefits from technology use than their less privileged peers. Privileged young individuals enjoy their leisure time activities and the ‘distance from necessity’ that allows them to engage online in ways of enriching recreation as a form of Bourdieusian ‘serious play’. This enables them to engage in ‘studious leisure’ and further development of playful and exploratory habitus leading to a positive dispositions towards technology as a results of increased skills and positive experiences. In turn, fewer digital resources limit the online engagement of the less-privileged young people who tend to develop a task-oriented information habitus looking for more unambiguous outcomes. This can be described with the concept of the ‘taste for the necessary’, something that Bourdieu sees originating from conditions of scarcity and want. The aspiration to only reach the particular goal prevents them engaging online in more exploratory ways in contrast to their more privileged peers. (Ignatow & Robinson 2017, 954; Robinson 2009, 503–505; Bourdieu 2000; Bourdieu 1984.) This vision successfully illustrates the influence of interaction between individuals' actions and social structures in digital engagement. According to Ellen Helsper (2017a, 223), research on digital inequalities necessitates even more profound shift toward a contextual and socially comparative approach; not only to theorisation, but also the applied research methods and the planned interventions should recognise the relational nature of inequalities. The dependency between digital exclusion and the ways in which the individual perceives significant others’ attitudes towards usage of digital technology in particular context should be brought to the centre of the consideration. Helsper (2017a) criticises the majority of digital inequality research for its reliance on measuring individuals’ exclusion levels and related socio-demographic characteristics. Such an approach describes the problem of digital inequality individualistically preventing the success of possible interventions because it isolates individuals from their significant social contexts. (Helsper 2017a, 233.) Attention should be paid to individuals' everyday experiences and relationships which determine the relational inequality. The successful interventions require the understanding of individuals’ experiences of their own relational deprivation. It concerns the individuals’ own evaluation of and feelings about the value or acceptability of one’s objective inequality. If an individual is, for example, surrounded by people who do not value the digital engagement, they do not 23 necessarily even see their own disconnection as problematic. Researchers should therefore trace the digital referents and the influence they have on individuals’ engagement with digital technology. (Helsper 2017a, 235–237.) This study leans on the idea that there is no need for explicitly defined or observable limit for sufficient access, competency or online participation, and sees that inequality is determined in relation to other actors, the particular situation and the objectives of desired action. This stance is adopted especially because the relational approach understands inequality through structural aspects of differentiation without falling to exclusively individualistic explanations. Although digital inequality, understood as a relational lack of engagement, is based on unequal resources, it is mediated by behavioural patterns that reflect the general social situation and resources of the individual. From these starting points, the conceptual and empirical viewpoints to digital inequality are discussed in more detail below. Conceptual and Empirical Viewpoints to Digital Inequality Leaning on the system theory, Christian Fuchs (2009; 2008) argues that society consists of interconnected subsystems. However, these subsystems are not independent or fulfilling only one specific function, rather they are open, interconnected and networked. According to Fuchs (2008, 62), in order to survive, individuals are forced to tame the nature (ecological subsystem) with technology (technological subsystem) in order to produce resources which can be distributed and consumed (economic subsystem), enabling collective decisions (political subsystem) and constituting values or acquiring skills (cultural subsystem). For Fuchs (2008), economical, political and cultural subsystems build a core of contemporary society. The similar distinction is in fact presented in several traditional sociological theories. Anthony Giddens (1984, 28–34), for example, distinguishes between economic, political, and legal institutions and symbolic orders of discourse as the basic institutions of society. For Pierre Bourdieu’s language, economic, political, and cultural capitals form three basic types of structures in society (Bourdieu 1986, 241–252). Jürgen Habermas (1987, 113– 118), in turn, conceptualises the corresponding structures as the lifeworld, the economic system and the political systems. Human agents and the circumstances in which their practices shape the subsystems of society, produce the social structures (Fuchs 2009, 46). Society is primarily an interconnection of social systems in which people enter into social relation with others. In each of these relationships, individuals sense their position 24 towards one another and their practices produce and reproduce certain social structures enabling and constraining individuals' thinking and actions, extending further to other social practices “and so on ad infinitum.“ (Fuchs 2017, 452.) Like social relations, technology also both enables and constrains human practices. This happens through individuals’ material access, abilities to use technology, capabilities to use them in beneficial ways, and through associated institutions (Fuchs 2009, 46). Eszter Hargittai and Yuli Hsieh (2013, 144–147) argue that digital usage and engagement offer potential implications for human capital as a form of academic achievement and financial capital, relevant not only to labour market success, but also for social capital and civic engagement. Therefore, digital divide is associated with an economic divide, a political divide, and a cultural divide as in modern society social structures take the form of accumulated and unevenly distributed capitals (Fuchs 2009, 47). From this perspective, it is basically the multidimensional class structure of the society which causes the structural inequalities. Laura Robinson (2009, 505) argues that the disparities in digital skills are derived from social stratification in society. Patterns of digital inequality consists of categorical social hierarchies and uneven distribution of resources (see van Dijk 2013, 33; 2012, 61; Fuchs 2009, 46). Social hierarchies emerge from the influence of personal categories such as age and gender, and positional categories such as labour market position and education level. The uneven distribution of digital resources (such as access and capabilities) is another side of the stratification alongside categorical social hierarchies (Fuchs 2009, 46). Uneven distribution of resources originates from the asymmetric distribution of economic, political, and cultural capital manifested as, for example, income, relationships and skills (Hargittai & Hsieh 2013, 129; Fuchs 2009, 46.) For Jan van Dijk (2013, 33) such resources in digital inequality research are: material, referring to possession or income, temporal, referring to time to use technology, mental, referring to ability or motivation, social, referring to supportive network, and cultural, referring to status or preference for being present online. Van Dijk (2005, 129–130) assumes that higher levels of material and mental resources are indeed the factors of digital inequality, but more personal indicators like temporal, cultural, and social resources are even more important aspects. In the contemporary society, digital inequality manifests itself in economic, cultural, social and personal domains, which are the corresponding domains of traditional (offline) exclusion (van Deursen, Helsper, Eynon & van Dijk 2017, 468). This is based on the Helsper’s (2012) model which focuses on the resources that people possess in their daily lives in the information society (van Deursen & 25 Helsper 2018, 2337). Helsper’s model conceives both social and digital exclusion. The model does not assume that a certain type of engagement would defeat another or that more frequent use necessarily would mean deeper digital inclusion. Depending on individual’s offline conditions, digital exclusion from a particular type of online engagement can be linked to more or less disadvantage in individual’s daily life. (Helsper 2012, 405.) The four key domains (economic, cultural, social, and personal) of corresponding online resources, are based on empirical research and, for instance, Bourdieusian theorisation and van Dijk’s (2013; 2005) conception of resources (van Deursen et al. 2017, 454). According to Helsper (2012, 404), economic online resources refers to commercial and information -related uses and learning via digital resources which increase abilities to gain benefits like income or savings, improvement in employment status or finances, and better educational grades or degrees. These resources can be operationalised by engaging in online shopping or banking, distance learning or online information seeking. Bourdieu (1986, 16–17) saw education as a part of cultural capital referring it to objectified and institutionalised form of qualifications providing status in society. However, for Helsper (2012) and van Deursen et al. (2017, 454) education is a part of economic capital, because it is a resource that gives the opportunity to acquire more income, better jobs, and increased wealth. Cultural resources refers to gender, ethnicity, and religion, but also creative and productive activities related to participatory cultures (see e.g., Jenkins et al. 2009). Cultural usage produce outcomes associated with identity and belonging (van Deursen & Helsper 2018, 2336; Helsper 2012, 414). Personal resources emphasise personality, aptitudes, and well-being, and are related to entertainment and leisure, self-actualisation, and health-related online engagement. Personal resources can be measured as interests (leisure or hobbies), intelligence, and both psychological (confidence) and physical (health) well-being. The resources in the social domain relate to connections to networks which provide attention and social support from other people. These can be operationalised as family ties, networks build on common interests, group membership, voting, power within the community, and influence over unknown others. Therefore, civic and political participation are also included in the social domain of resources in this model. (Van Deursen et al. 2017, 454–455; van Deursen & Helsper 2018, 2336; Helsper 2012, 414.) Helsper (2012, 412) argues that access, skills, and positive attitudes toward digital technology and the Internet are important but not a sufficient condition of beneficial use. The most important are the ways in which individuals engage with technology. Albeit, the four domains of digital resources belong to separate scales, they are interrelated (Helsper 2012, 414). The model sees the actor him- or herself as a locus of capital 26 and as a player in different (sometimes overlapping) fields, instead of focusing on the social structure of the fields in which online resources are activated (van Deursen & Helsper 2018, 2337). For Helsper (2012, 405) the digital inclusion is embedded in an individual’s offline circumstances, and therefore digital exclusion should be analysed in connection with social exclusion. In empirical studies, social status has been found to relate to different types of profitable technology usage (e.g., Hargittai & Hinnant 2008; DiMaggio, Hargittai, Celeste & Shafer 2002) and individuals who are already in privileged positions in the society are identified as gaining more benefits of their technology use than disadvantaged individuals (Zillien & Hargittai 2009, 287). Studies examining digital inequality in countries where the Internet and digital technology are highly available indicate that education, gender and age are the most crucial factors for individuals’ digital inclusion (e.g., Hatlevik, Scherer & Christophersen 2017; Hargittai & Shaw 2015; van Deursen & van Dijk 2014; van Deursen, van Dijk & Peters 2011; Helsper & Eynon 2010) whereas, for example, income and residency (e.g., van Deursen & van Dijk 2014), Internet experience and the number of hours spent online (van Deursen et al. 2011) are less relevant for inclusion. Therefore, empirical evidence pertaining to digital skills and usage in association with gender, age and education is introduced in more detail below. Gender Gap In Europe, gender inequalities in access, skills, and usage, but especially in digital education and digital labour market have long been at the centre of political concerns (see e.g., EIGE 2016). At European level, the traditional access divide still emerges between nations and overall Internet access of EU households ranges from 57% in Bulgaria to 96% in the Netherlands (EIGE 2016, 5). In Finland, based on official statistics from year 2017 (OSF 2017) and PISA 2012 results (OECD 2015, 36), the proportion of individuals using the Internet is 100% among young people aged 15 to 34, which indicates that, at least among young Finns, there is no divide in terms of access to the Internet between genders. As disparities have decreased in material availability, at least in highly technologised countries, the gender gap has been identified as being linked to the differences in digital skills and usage. Empirical evidence of gender difference in digital skills has proved to be quite contradictory and consistent results are missing. While previous studies based on performance tests suggest that there are no gender differences (van Deursen & van Dijk 2010a), others show females to be more successful than males 27 (e.g., Aesaert & van Braak 2015) and still some others vice versa (e.g., Correa 2016, 2010; van Deursen & van Dijk 2015; van Dijk 2013; 2012b; Fuchs 2009). What comes to usage, a previous study on young people (Tondeur, Van de Velde, Vermeens & Van Houtte 2016) shows that, generally speaking, females tend to be less positive toward digital technology, but nevertheless attitudes towards using technology for educational purposes are not affected by gender. This indicates that female's interest in using technology is influenced by its utility (Tondeur et al. 2016, 69). Females engage with more restricted range of online activities and participate less than males particularly in conversations or user- generated content platforms or sharing content online (Correa 2016, 2010; Hargittai & Jennrich 2016; Hargittai & Shaw 2015; van Deursen & van Dijk 2014; Hargittai & Walejko 2008). According to Teresa Correa (2010, 85), the gender differences in digital technology usage are influenced by psychological factors such as lower levels of confidence and weak motivation. In a study concerning social media usage and skills, digital skills did not associate with frequency of usage, which was, instead, influenced by other socio-economic factors such as education (Correa 2016). Thomas Friemel (2016) shows that gender differences in technology usage disappear among elderly people when controlled for education, income, technical interest, pre-retirement, computer use and marital status. Friemel (2016, 325–326) concludes that the social context affects Internet use and the usage is not simply a gendered issue. The significance of gender differences in digital skills and usage is in the far- reaching consequences. As the report of European Parliament (EP 2018, 19) shows, women tend to avoid ICT related studies and digital careers; only about 32% of ICT field employees are women. Because of the strong growth and demand for workforce, improving women’s digital skills is deemed desirable. It would strengthen their inclusion in the ICT workforce, which would increase both female’s employment and decrease the labour shortage in the ICT field. In addition, as the ICT field is known as a high paying sector, women’s inclusion in the field is expected to reduce the gender pay gap as well. (EP 2018, 20.) The reasons for a small number of females in the field of ICT are assumed to be rooted in long-held stereotypes related to teachers’ and parents’ tendency to encourage particularly boys toward technology combined with the lack of female role models, misconceptions on girls' aptitudes, organisational constraints and the lack of work- life balance at work (Cheryan, Ziegler, Montoy & Jiang 2017; EP 2012, 7–8.) Van Dijk (2005) claims that gender differences in the adoption of technology evolve early in life; while little boys pick up technical toys and devices, girls usually choose to play with other toys. This triggers a reinforcement process where girls 28 avoid learning technical skills whereas boys build up cumulative technical abilities. In adulthood this allows men to grasp technically and strategically important job opportunities and have an advantage over women in the field. (Van Dijk 2005, 11– 12.) This quite rough generalisation brings together familiar assumptions that gender preferences are reproduced through socialisation that takes place in families and education during the years of early childhood and adolescence. The PISA results provide an important reminder of the impact of attitudes and interests on technology orientation. Based on these results, young Finnish people are relatively passive in engaging with science-related activities outside of school. Especially Finnish girls do not show strong interest in these topics. (OECD 2016, 119–120.) However, Finland is the only country in included PISA 2015 study in which girls are as more likely to be among the top performers in science instead of boys (OECD 2016, 17). Despite girls' success in science, career prospects for the future are indicated as being particularly traditional among Finnish 15-year-olds; boys were over four times more likely than girls to expect a science-related career as an engineer, scientist or architect while Finnish girls were more than three times more likely than boys to expect a career as a healthcare professionals (OECD 2016, 117). This is in concordance with research evidence indicating that in advanced industrial societies, dispositions toward mathematics or technology tend to be more male dominant. Therefore, the gender segregation of educational fields and labour market tend to reinforce the existing gender stereotypes especially in developed countries (e.g., Charles & Bradley 2009, 960.) Age Divide A popular assumption is that the digital skills of younger users are superior to skills of older adults. There are, of course, several studies that support this assumption. For example, van Deursen and van Dijk (2011, 905) found that older age decreased technical competency, namely basic skills in using technology and navigating the Internet, but this did not influence to the level of accessed information nor their strategic skills. Based on more recent results of van Deursen and van Dijk (2015, 388), age appears to have a strong effect on material access and medium-related digital skills, but only a minor effect on content-related skills and diversity of usage. Based on the results of PIAAC (OECD 2016), young adults (ages 16 to 24) in Finland are more proficient in technology-related problem-solving than rest of the Finnish adult population. However, overall, the evidence on the association between age and skills is not clear cut. For example, based on the results of Eszter Hargittai (2010, 92), the digital skills of young people vary and young people are 29 not universally savvy with digital technology. Moreover, according to Eszter Hargittai and Kerry Dobransky (2017, 207), the skills of older adults are also much more diverse than expected and not all elderly people suffer from poor digital abilities. In the comparative study of the variance in technology usage in five highly developed countries (New Zealand, Sweden, the United States, Switzerland, and the United Kingdom), it was found that social interaction and entertainment related online activities decline with age, whereas the decline is less pronounced in information seeking and commercial transactions (Büchi, Just & Latzer 2015, 2715). In fact, most of the variation of the social interaction was explained by age, as young people (under 16 years) engage in social interaction-related activities much more than older groups (Büchi et al. 2015, 2715). The same kind of observation is made in the study conducted by van Deursen and van Dijk (2014, 516), the most prominent variable causing differences in Internet usage being age, the youngest age group (16–29) being more active in every usage factor than the older participants. However, according to Hargittai and Dobransky (2017, 195), when skills are controlled, older adults with higher socio-economic status are more likely to engage with diverse types of capital-enriching online activities. Therefore, despite the correlation between age and use, age as a single variable may not be sufficient to explain the differences in the online engagement of individuals. The age divide is usually understood as a transient phenomenon; the digitalisation of everyday life requires digital skills from every adult so that they are able to run their daily affairs and are successful at work. Due to this, the weak technological skills of older generations are considered as a generational issue, which is assumed to disappear as older non-users pass away (e.g., Wagner, Hassanein & Head 2010; Gilleard & Higgs 2008). However, among elderly people, the digital inequality is not just an age issue. Friemel (2016) shows that many senior citizens are not using the Internet due to disabilities such as limited eyesight or hearing. In these cases, the real cause of digital inequality is the loss of the ability to use technology, even if the same individuals have previously been active online participants. (Friemel 2016, 325.) Therefore, overcoming these disability issues requires technological development, such as improved usability, which eliminates common age-related barriers affecting senior' technology usage (Lee, Chen & Hewitt 2011, 1236), as opposed to merely waiting for these problems disappearing on their own, as the older generations are gradually replaced by a new, more technological savvy generation of people. 30 More evidence on this issue, that digital inequality is not a transient age group concern, comes from Sweden. The study of Ellen Helsper and Bianca Reisdorf (2017, 1265) demonstrates that in Swedish society, digital exclusion is attached to the most vulnerable individuals, as social exclusion and economic disadvantage have become the main predictors of digital exclusion. This is interpreted as a sign of the emergence of a digital underclass. Therefore, Helsper and Reisdorf (2017) warn that despite the early experiences of younger generations with digital technology and the Internet, the next few generations in Sweden will include a small but severely excluded group of individuals which will be relatively more marginalised than those in the current generations and the problem of digital exclusion will revolve even more around the most socially vulnerable individuals than it does today. Educational Polarisation As mentioned already, the relationship between digital skills and socio-economic status, most typically dominated by the level of education, has been recognised in several empirical studies. In studying sample of 18 to 29 -year-olds, Correa (2016) detected that digital skills increase with the level of education. Similarly, based on the results of Hargittai and Dobransky (2017, 207), the skills of older adults are also strongly associated with education, as higher education level and income are associated with higher levels of digital skills among elderly people. In fact, according to van Deursen and van Dijk (2015, 387), education influences not only digital skills, but also the diversity of usage of digital technology. This increases the importance of education for digital engagement. When examining the relationship between education and usage, the research evidence demonstrates that lower educated individuals tend to use the Internet more frequently than their higher educated counterparts (Tsetsi & Rains 2017; Correa 2016; van Deursen & van Dijk 2014). This is due to the fact that less educated people prefer such forms of digital usage that take a lot of time. For example, van Deursen and van Dijk (2014) noted that higher educated participants use digital technology in more beneficial ways; individuals with medium or high levels of education, in particular, use the Internet more for participating together with usage related to information, news and personal development, whereas individuals with low level of education engage more often with gaming and social interaction, which both are seen as time-consuming digital activities. (Van Deursen & van Dijk 2014, 520–521.) 31 According to Correa (2016), although the frequency of usage of lower educated individuals is high, they consume less information and news and produce less mobilising information online, all of which have been said to produce more meaningful user outcomes. Lower educated young people in particular tend to use social media more frequently than others. (Correa 2016, 1102–1104.) According to Eric Tsetsi and Stephen Rains (2017, 251–251), low levels of education, along with some other disadvantageous socio-economic factors, tends to be associated with smartphone-dependency which refers to the situation where individual’s only means of accessing the Internet is via a smartphone. As smartphone-dependency tends to reduce the versatility of Internet usage, it threatens to limit individuals potential for engaging beneficial online activities. In summary, the effect of education level on digital engagement is evidently significant. Van Deursen and van Dijk (2014, 521) assume that the variation in digital skills and usage habits produced by differences in education levels are more evident than differences produced by age and gender. This makes these differences also relatively permanent. Particularly in the domains such as economic commerce, institutional government, and educational outcomes the empirical evidence indicates that higher educated people gain more benefits from digital usage than their lower educated counterparts. In this way, despite the fact that more and more people have gained access to digital technology and the Internet, the use of technology offers the most to the higher socio-economic groups. (van Deursen & Helsper 2015, 46–47.) Therefore, it is important to study the link between education and digital inequality and to seek to refine the results obtained in previous studies in order to provide a better understanding of the phenomenon. 32 4 Research Questions In this study, the digital technology in education is seen primarily through digital affordances which open up chances for individuals in education and more wider in life. Due to the fact that affordances are not similarly open to everyone, it is important to consider digital inequality as a relational issue relating to individual’ abilities to make use of these affordances. Therefore, this inequality is the actual object of the empirical part of this study. According to Helsper (2012, 412), access, skills, and positive attitudes toward digital technology and the Internet are important, but not a sufficient conditions of beneficial use. The most important factor is the way in which individuals engage with technology, operationalised as the types and levels of usage. Here digital inequality is understood as an intertwined combination of inequalities in digital skills and usage, and a certain type of engagement is expected to lead to more profitable activities that are more likely to expose individuals to potentially beneficial outcomes, while others do not. However, in order to explore digital engagement, it is important to analyse its components and therefore focus on examining digital skills and digital usage. This dissertation thesis seeks to increase understanding about digital inequality among young people in context of education in Finland by scrutinising the differences in digital engagement of the Finns aged 12–22 by combining the results of five original articles. The work aims to identify social hierarchies producing unequal distribution of skills and usage among young Finns. The articles included in this dissertation focus on the variables that, based on previous research (see e.g., van Deursen & van Dijk 2014), most strongly divide digital inequality, namely gender, age and education. In the case of education, the focus is not only on education levels, but also on educational stratification known as horizontal segregation which refers to the unequal distribution of education in a way that is not hierarchical. Hierarchical segregation, also known as vertical segregation, refers to the unequal positioning of genders in the occupational hierarchies, leading to differences in prestige and income levels (e.g., Charles 2003, 270; Blackburn, Jarman & Brooks 2000, 129). In comparison, horizontal segregation refers to the 33 tendency of men and women to orientate in gender-specific occupations within the same level of education (e.g., Triventi, Skopek, Kosyakova, Buchholz & Blossfeld 2015, 31; Charles & Bradley 2009, 930). Occupational specificity, which is typical of education systems which are divided into vocational and general education, links education and occupations strongly together and increases the likelihood of horizontal gender segregation (Triventi et al. 2015, 33). For this reason, it can be assumed that there occurs considerable horizontal segregation also in Finland, where there is a wide-scale vocational training option alongside general upper secondary education. The horizontal segregation is, at least to some extent, said to be rooted in stereotypical beliefs that tasks involving personal service, nurturance or interpersonal interaction are more suited to females, whereas tasks involving strenuousness, physicality and interaction with things are more prototypical for men (e.g., Charles 2003, 269). Maria Charles and Karen Bradley (2009, 930 and 959) have reminded that this kind of segregation is in fact more pronounced in advanced industrial societies than in developing countries, where educational choices have a more crucial role for individuals’ economic success and overall survival for both genders. The study also investigates the ways certain individuals come to have high level digital skills and to exhibit high levels of profitable usage. To be more specific, to pose this in a form of question, this study asks to what extent digital engagement among Finnish students has a compound and sequential nature? According to van Deursen et al. (2017, 468), compound exclusion refers to a cumulative disadvantage, i.e. that the lack of a particular skill will also likely lead to the lack of other skills and a lack of participation in some areas also likely results in a lack of involvement in other areas. The sequential exclusion, in turn, refers to the dependency between different types of digital exclusion: lower level of digital skills are associated with lower level of digital usage, resulting in a fewer chances for beneficial outcomes. However, it should be noted that individuals achieving benefits in one domain do not necessarily gain positive outcomes in another. (Van Deursen et al. 2017, 453 & 468.) Compoundness and sequentiality are important aspects to examine as they promote understanding of the features of digital inequality and the functions through which it affects individuals. Thereby concepts of compoundness and sequentiality of digital inequality contribute to a reaching adequate understanding for apt interventions in education. The five original articles included in this dissertation thesis deal with above- mentioned central themes giving comprehensive understanding of digital 34 engagement among Finnish lower and upper secondary school students. The research questions structuring the empirical part of this dissertation are as follows: 1) To what extent gender makes a difference in students’ digital engagement in Finnish lower and upper secondary schools? 2) How does the age of Finnish upper secondary school students affect their digital engagement? 3) How are gender-segregated fields of education and future intentions associated with digital engagement of 12–22 -year-old Finns? 4) To what extent and in what ways does digital engagement accumulate, as exhibited by certain individuals more than others among Finnish lower and upper secondary school students? 35 5 Research Methodology The Measurements Various methods have been used to study the skills required for digital technology and the Internet. Eden Litt (2013, 615–617) has classified such assessment methods into survey/self-report measures, performance/observation measures, and combined/unique assessments. Surveys and self-reports have been the most dominant methods for quantitative studies assessing digital skills. In self- assessments participants have to respond to a question or a set of questions about their own competence levels or evaluate their ability to perform specific tasks on the Internet (e.g., Bunz 2009). According to Litt (2013, 618), the qualitative studies have preferred observation-based measures or interviews, which incorporate ethnographic practices. These types of studies focus on, for example, observing a person’s actions during information search tasks (e.g., Kiili, Laurinen & Marttunen 2008). Interviews, in turn, typically consist of open-ended questions like what online services people use, what type of sites they visit, and whether they feel their skills are adequate (e.g., Smith & Caruso 109–110), whereas performance-based online tests consist of practical tasks related to the use of digital technology, applications and the Internet, and their utilisation in various practical situations (e.g., Aesaert & van Braak 2015). Self-assessment surveys have been criticised for significant validity problems (e.g., van Deursen, Helsper & Eynon 2016, 804–805; van Deursen & van Dijk 2010b, 892; Hargittai 2005, 376), conceptual ambiguity and over-simplification of the phenomenon in question (van Deursen et al. 2016, 804). The problematic nature of self-assessments is related to the tendency of over- or under-estimating one’s own knowledge (e.g., Porat, Blau & Barak 2018, 23; McCourt Larres, Ballantine & Whittington 2010, 97) and how biased estimations are more common among males (e.g., Hargittai & Shafer 2006, 444). The main problems of performance-based tests are their time consuming nature and high development costs. They are also more difficult to replicate and utilise in studies investigating 36 large samples. (Aesaert & van Braak 2015, 9; Litt 2013, 618–619.) On the other hand, in several studies, two or more types of measures are combined which, according to Hargittai (2002b, 1243), lead to rich data allowing the examination of not only diverse usage and skills, but also the underlying social factors. In this way it is possible to reduce the limitations of methods. An important developmental aim was to create a test application for large samples. One of the key ideas of the test development work has also been to overcome the problems of self-assessment to achieve more objective results. These needs lead to the development of the ICT Skill Test in the Research Unit for Sociology of Education, University of Turku. The author of this dissertation thesis has been responsible for the development work covering both the technical and the content development of the test. The test was developed in two phases; the original test in 2013 and the renewed test in 2016. Due to the curriculum reform in basic education (FNBE 2016a), substantial changes had to be made to the test contents in year 2016. Furthermore, further adjustments were made to the test contents based on the experiences gained during the pilot stage of the original test. The development of the second version was assisted by a steering group (related to the project funding which enabled the second sample of this dissertation) led by the Finnish Ministry of Education and Culture. The steering group provided feedback on test contents, particularly in relation to the curriculum objectives of the renewed curricula for information and communication technology learning goals in basic education. The ease of use, reliability, scalability and automation of routine administrative tasks have been the key objectives for the test instrument that combines a performance-based test with a more traditional online survey. The implemented test application is a web application, written in PHP and JavaScript programming languages together with TinyMVC- and Bootstrap-frameworks. The application utilises PostgreSQL/MariaDB databases for data storage needs. The application is bilingual as Finnish and Swedish are official languages of Finland. The test application contains four kinds of user roles: student, teacher, organisation, and administrator. Each of these roles has a different set of actions and views available. Data protection issues were addressed with diligence due to the enactment of General Data Protection regulation ((EU) 2016/679) which became applicable as of May 25th, 2018. For example, in the case of the student-role, no login was required (no directly identifiable personal data was collected from minors) and at the beginning of the test, data subject’s consent for research was requested (the data subject was able to withdraw the consent at any time during the test session). In addition, when the test was done and the user closed the test, all the test data was 37 gathered into a separate research database, and deleted from the application’s database which was used for data storage only while the test session was active. In development work the key aspect has been to separate the test contents from the technical test environment and the types of tasks it enables. This allows the use of the same application in different studies by simply changing the content. That is why the test content (tasks and the specific surveys in each study) were included in the test application as easily changeable Extensible Markup Language (XML) files. In this particular research, the contents of the test instrument were closely related to the curricula objectives of basic and secondary education especially due to the curricula renewal in year 2014 which brought digital skills to a more prominent role in education in Finland. The test contents utilised in this study are therefore based mainly on the definitions and goals of ICT competencies of the Finnish national core curriculum for basic education (see FNBE 2016a; 2004). The original ICT Skill Test was developed mainly on the basis of the year 2004 Finnish National core curriculum for basic education (FNBE 2004). In this previous core curriculum the objectives were to offer understanding of technology and its evolution and impact, to use technology responsibly and to learn to use equipment, programs and networks (FNBE 2004, 41). Information technology was also one of the optional subjects which students could choose to participate during the last two grades (FNBE 2004, 254). In turn, the renewed ICT Skill Test is based on the renewed national core curriculum where ICT competence is one of the seven transversal competence studies integrated into all subjects. In the renewed curriculum ICT competence is considered to be an essential factor of civic competence and is seen both as an object and an instrument of learning. In practice, the goal is to offer understanding of the basic operations and concepts of ICT, knowledge to use ICT in a responsible, safe and ergonomic manner and skills to use ICT as a tool in information management, creative work, social communication and networking. (FNBE 2016a, 24.) In secondary education, the curricula were reformed at the same time, and, in general upper secondary schools in particular, the renewed objectives are in line with the goals of basic education and strives to strengthen the knowledge produced by the previous educational level (FNBE 2016b). The 17 test items in the original ICT Skill Test were classified with factor analysis (Article I) to basic digital skills (word processing, spreadsheet, social networking, information seeking, presentation, basic use of computers, image processing, and web content creation), advanced technical skills (operating system installation and initialisation, maintenance and updating, software installation and initialisation, information security, and information networks) and professional 38 ICT skills (server environments, database operations, digital technology, and programming). As already mentioned in the introductory chapter, this study utilises a framework of digital skills, where the skills are divided into medium- and content-related skills because it helps to overcome the problems of the operationalisation differences of the different test versions, at least to some extent. Van Dijk and van Deursen (2014) divide digital skills into operational skills, formal skills, information skills, communication skills, content creation skills, and strategic skills. Furthermore, van Dijk and van Deursen (2014, 6–7) separate medium-related digital skills which concern the technical aspects (i.e., operational and formal skills) and content-related digital skills (i.e., information, communication, content creation, and strategic skills) concerning the substances. In the case of the 18 items of the renewed ICT Skill Test, the following items were classified as medium-related skills: basic operations, information networks, installations and updates, and functionalities of word processing, spreadsheet, and presentation software. In turn, the following items were seen as content-related skills: information seeking, communication, video- and audio processing, cloud services and publishing, image processing, social networking, information security, and software purchasing. In addition, because the renewed curricula in Finnish basic education includes also programming, the renewed ICT Skill Test also measures four kinds of programming related sub-skills: elementary programming, database operations, web programming and programming which, however, were only briefly examined in the fifth article. Since the purpose of the both versions of the ICT Skill Test is to measure participants’ digital skills, their content deals with educational and partly psychological testing and therefore requires validation through the methods used in these sciences. Classical item analysis relays on test level statistics which are targeted to measure the quality of the test (i.e., reliability and validity) and the item level statistic (e.g., item difficulty and discrimination power) (e.g., Kaplan & Saccuzzo 2017, 135–146 and 173–186; Considine, Botti & Thomas 2005, 21–23.) The contents of the both test versions together with the estimates of their reliability and validity measures are attached to this dissertation (Appendixes 1 and 3), but they are also covered and discussed in original articles. Between the two core curricula (FNBE 2004 and FNBE 2016a), there can be seen a clear shift from medium-related skills or computer literacy to the broader importance of citizens’ information society skills and concept of digital competence. Thereby also the characteristics of the ICT Skill Test changed as it was transformed from a more technically oriented test into a tool that provides a much broader view of digital skills. In agreement with Fazilat Siddiq et al. (2016, 78), even though some kind of core digital competencies may stay relatively stable 39 over time, the content of these competencies and the environment in which they are utilised is undergoing changes due to rapid technological innovations. In fact, operationalisation of digital skills change over time due to technological advances and changes in the availability of technology (e.g., Erstad 2006). These changes force the assessment instruments to be based on continuous development (concerning both, content and their technical implementation) when aiming to meet the requirements of the fast changing technological milieu. For this reason, alongside the test content renewal, also the test application experienced a thorough change in year 2016; the overhaul of the test application included modern responsive and accessible user interface and an extension of the possibilities for interaction. Technical reforms are necessary from time to time in order to keep the test instruments up to date and to avoid distorting the results, for example due to an obsolete user interface or difficulty of use. The ICT Skill Test was developed to enable sample-specific surveys. The application presents these survey questions to the participants at the beginning of the test immediately after the study description and the consent form. For purposes of this research, both test versions consisted of background variable and technology usage habits questionnaires. In the original ICT test the background variables included: gender, age, education level, and whether the secondary education level student was studying in a general or a vocational upper secondary school. The technology usage habit questionnaire collected information about how frequently the participants used different kinds of digital devices and how frequently they used digital technology for different purposes. The renewed ICT Skill Test collected participants’ age, gender and education level, but also current educational choices (general upper secondary school or vocational institution, whether the student was a general upper secondary school student participating in a basic or advanced syllabus in mathematics, and if the test-taker came from a vocational upper secondary school, participant’s field of education), and the participants’ future intentions, i.e. the field in which they desired to study or work after graduating from their current education. Similarly to the original test, the renewed test’s usage habit survey gathered information about how frequently the students used different kinds of digital devices and how frequently they were used for different purposes. However, in a newer version of the test the usage habit survey is shortened as it was not originally intended to examine individual online activities, as opposed to more general purposes of use. Responses of both usage habit surveys in both test versions were categorised according to Helsper's (2012) classification, whereby different types of digital usage are classified as economic, cultural, social and personal use. The 40 contents of the surveys and their classification for usage domains are attached to this dissertation (Appendixes 2 and 4). Figure 1. The Education System in Finland with the ISCED Classification and Duration in Years (MEC & FNBE 2017, 3). Figure 1 represents the Finnish education system in relation to the international standard classification of education (ISCED) which is maintained by the United 41 Nations Educational, Scientific and Cultural Organisation (UNESCO). This study focuses on the parts of the education system described in the figure on a white background. At basic education level, this study focuses on grades 7–9, the so called lower secondary school level. At secondary level, the study covers both general and vocational upper secondary schools. In Finland, after common nine year basic education, over 90 per cent of each age group starts general or vocational upper secondary education, both of which give students eligibility to continue to higher education level (MEC & FNBE 2017, 17). In general, approximately half of the age group continues in general upper secondary education in Finland, although the number of general upper secondary school students has fallen in the 2000s due to the declining size of the age group and an increase in the attractiveness of vocational education options (FNBE 2018, 11). The educational level in this study is defined in accordance with the ISCED 2011 classification. The 7th to 9th grade students are on the lower secondary level of Finnish basic education, i.e. on the second stage of basic education (ISCED level 2). The general and vocational upper secondary school students are on the upper secondary level (ISCED level 3) (UIS 2012). In the case of vocational upper secondary school students the fields of education are defined as the fields of study for which the vocational qualifications are classified in Finland, consisting the following eight fields: culture, natural sciences (ICT), natural resources and environment, tourism, catering and domestic service, social services, health and sports, technology, communication and transport, and social sciences, business and administration. However, the students’ future educational or occupational intentions are defined in accordance with the international standard classification of fields of education and training, ISCED-F 2013 (UIS 2014), including the following fields: education, social sciences, journalism and information, business, administration and law, natural sciences, mathematics and statistics, information and communication technologies, engineering, manufacturing and construction, agriculture, forestry, fisheries and veterinary, health and welfare, and services. The international classification of education was used instead of the international standard classification of occupations (ISCO-08) (see ILO 2012) as it was expected to be more familiar to the participants, although the examples of both further studies and occupations were attached to options. Participants of the Tests This study examines the digital skills and digital technology usage of altogether 11,820 Finnish lower and upper secondary school students. Figure 2 illustrates the division of participants into two separate samples in relation to the Finnish education system. In both samples, the sampling took place by geographical areas (by six Regional State Administrative Agencies) and municipalities. At the school- 42 level, however, individual schools could choose not to participate in the study. Further, within the participating schools not all classes were enrolled in the study, but the entire participating class was tested at a time to prevent individual-level selection in the study. All in all, it is clear that neither sample in this study satisfies the requirements of randomness for a multistage sampling. Nevertheless, the relatively large sample size of this study reduces the likelihood of a sampling process error compared to similar case studies in the context of education, which are typically based on a much smaller sample size. This is due to the general fact that as the sample size increases, it approximates the size of the target population, and therefore, inevitably approaches its characteristics. For this reason, it can be assumed that, the samples of this work provide a valid starting point for the scientific analysis. Nevertheless, articles based on these samples are not intended to generalise any actual statistical models with effect sizes from sample parameters to the target population level, as there is no methodological basis for this. Instead, the samples in this study are intended to provide a window through which to study the phenomenon, the factors behind it, and the relationships between these factors on the basis of a relatively large sample. Figure 2. Participants in the Samples I and II in Relation to the Finnish Education System. Sample I The sample I data was collected during a pilot study in Finland during years 2014 and 2015 from 41 secondary (grades 7–9/9) and upper secondary level schools (study years 1–3/3). Altogether, 3,159 students were tested; 52% were male students, and 48% were female students. The age of the students ranged from 12 through 22 and their mean age was 15.9. Furthermore, 40% (N = 1,261) came from 43 the basic education (lower secondary level), and 60% (N = 1,898) came from secondary education (upper secondary level). Of those upper secondary level students who participated in this study, 54% came from general upper secondary schools, while 46% came from vocational upper secondary schools. In the case of sample I the sampling procedure based on convenience sampling that is a type of non-probability sampling involving the sample being drawn from the population that is either close or otherwise easily available (Gorard 2013, 83–84). The problems and restrictions of this kind of potentially biased sampling are recognised, as noted above. However, this kind of sampling is said to be appropriate in the case of pilot testing (e.g., Gorard 2013, 84) which was the main purpose with the original ICT Skill Test. The sample covers the area of three Finnish Regional State Administrative Agencies: Southern Finland, South-western Finland and Western and Inland Finland. Sample II The sample II data was collected in Finland during the year 2017. Altogether, this sample consisted of 8,661 adolescents divided into two subsets – lower and upper secondary education students. The data from the lower secondary school students (grade 9/9) were collected as part of a project (Comprehensive school in the digital age) financed by the Finnish Prime Minister’s Office (funding provided for Government analysis, assessment, and research activities). The participants came from 65 municipalities (149 schools) around the country, chosen by using a geographically representative sample of Finnish municipalities and their schools, as determined for the project by the Finnish Education Evaluation Centre. The sampling was based on stratified sampling strategy, which began at the regional- level, proceeding to the municipality-level within each regions, and resulted to a representative sample of Finnish municipalities. The sample covers the area of all Finnish Regional State Administrative Agencies: Southern Finland, Eastern Finland, South-western Finland, Western and Inland Finland, Northern Finland, and Lapland, proportional to the population of the regions. Altogether, this subset consists of 5,455 9th graders, aged 15 to 17. The data set for the upper secondary school was collected as part of a project (Occupational restructuring challenges competencies) financed by the Strategic Research Council (SRC) at the Academy of Finland. The participants for this subset came from 43 municipalities (88 educational institutions) around the country and consisted of 3,206 secondary level students ages 15 to 22. Of the participants, 69% come from general upper secondary schools, and 31% from 44 vocational upper secondary schools. The sample was constructed on the basis of the previous sample so that same size municipalities were selected from the area of same six Regional State Administrative Agencies for this new sample. Since many small municipalities in Finland provide upper secondary level education together with their neighbours, the number of municipalities and participating schools in this sample was lower than in the sample of the lower secondary education subset. The sampling still retains the same proportion in relation to the population of the regions as in the basic education subset. Analysis Data preparation In the case of both test versions, the research variables had to be created using a structural query language (SQL) from the raw data which was stored in the test applications’ relational database. Through this process, the raw data including transactions, choices and responses from database was converted to a two- dimensional research data matrix with variables and values that enabled the processing with statistical programming language (Python) and software (SPSS). The preparation phase included also the analysis of missing data. The examination of missing values is important for several reasons (Kwak & Kim 2017, 407), namely to avoid reducing the available data, compromising the statistical power of the study, and disputing the reliability of its results by causing a significant bias and degrading the efficiency of the data. Emphasis was put on preventing the problem of missing data in advance and the missing values which still passed this sieve were carefully examined. Missing data causes two types of problems; bias and error. While bias causes an external validity problems, error causes defects in the hypothesis testing. (Newman 2014, 377–378.) According to Roderick Little and Donald Rubin (1990, 294), it is common in social sciences to use imputation, weighting and direct analysis of the incomplete data. Imputation could sound attractive, but it has serious pitfalls and should only be used with caution. Weighting, instead, is applicable only with monotone patterns of missing values as it ignores the missing cases and gives each of the involved cases a new weight to compensate for the missing cases. (Little & Rubin 1990, 294–296.) Newman (2014, 387) recommends that in the case of construct-level missingness, missing values are imputed applying a maximum likelihood or multiple imputation if 10% or more of the sample is made up of construct-level partial respondents. 45 The test application was designed to prevent the missing values. Therefore, the test phases and items and sub-tasks were mandatory (an empty value prevented proceeding). In those cases where participants dropped out before the test ended, their data was not saved to the research database. This automatically blocked the collections of incomplete response sets to the database. In some schools old and outdated web browser which did not support input validation functionality were still in use. This led to missing values in survey answers being included in the data despite the efforts made to prevent them. In this case the missingness refers to the construct-level issue as the missingness of the values is not associated with observed values. However, it does depend on other missing values and the missingness is not random as the missing values concentrate on certain respondents (see e.g., Newman 2014, 375). Missingness did not impact specific schools, as the old browser versions were still in use in a large number of Finnish schools during data collection. Nonetheless, in the end, only less than 1% of the participants had missing values. Thus, in this study, due to the large sample size and only a small proportion of missing data, the missing values in usage habit survey responses were left untreated. Instead, if there occurred missingness in the background information (such as gender, age or education) this led to the exclusion of a particular participant out of the data. Another preparatory analysis concerned outliers. Outliers are extreme or incorrect values, which lie outside the overall distribution or pattern of variables (Gordon 2015, 422). Outliers can significantly influence the statistical evaluation (like distorting the mean and standard deviation of a sample), resulting either in overestimation or underestimation of the values. (Kwak & Kim 2017, 407.) Traditional regression models, in particular, have been said to be sensitive to outliers (e.g., Huang & Tzeng 2008, 14). Outliers may originate in data errors caused by faults in data entry or management or be correct values suggesting the need of subgroup analysis or demonstrate the inapplicability of the applied methods. (Gordon 2015, 422–424.) According to Kwak and Kim (2017, 410), there are three methods for treating outliers: trimming (i.e., excluding), winsorisation (i.e., modifying) and robust estimation. Before the actual analyses, the outlier values in the respondents' background information were examined and the respondents who did not belong to the target group or had deliberately misused the test application were removed from the data. There were two causes that led to the removal of the respondents from the data. Firstly, if time used in the original test was less than 6 minutes and in the renewed test less than 9 minutes (as the short execution time indicated giving up or messing with the test system), the person was excluded from the data. Secondly, if the 46 respondent’s age was under 12 or higher than 22 (the lower values were interpreted as mistakes or misleading actions and the higher values were removed due to their rareness), the person was not included in the data. Since the intention was to apply regression analysis to analyse the data, before analysis all the variables included in the regression analyses were standardised with min-max normalisation (e.g., Suarez-Alvarez, Pham, Prostov & Prostov 2012) to range between 0 and 1 and the influence of outlying values was examined during the analysis, for example, by examining regression residuals and possible influence of rare observations on the particular results. Description of Multivariate Analysis Except for the analysis in the first and third articles, which focus exclusively on the existence and magnitude of gender and educational differences exploiting bivariate analysis and thus serve as preliminary studies to confirm the need for further examination, analysis in the original articles utilises mainly multivariate statistical analysis. Multivariate models suit for social sciences, since the social life consists of multiple intertwined factors (e.g., Baur & Lamnek 2007, 3120). Lee Cronbach and Richard Snow (1977, 116) have reminded that learning and skills are to the greatest extent multivariate as every performance of an individual can be represented by a set of values describing the aspects of the performance. They have addressed that performance is measurable through multiple indices like errors, latencies and resistance which are often just moderately correlated and may not necessarily evolve simultaneously. As in social sciences in general, issues related to education are typically characterised by the relationship between individuals and society, as individuals interact with the social conditions to which they belong. The individuals and the social conditions are understood as a hierarchical system of individuals nested within social groups, which allows this system to be observed at different levels and to define variables at each level. Therefore, research into the relationships between individuals’ descriptors and social contextual variables, i.e. the impact of group-level characteristics on individual-level outcomes, is called a multilevel research. (See e.g., Hox 2010, 1; Asparouhov & Muthen 2006, 2718.) Although such design is nowadays a popular approach in education-related social science research, this study is not particularly interested in describing the differences within and between schools, as digital engagement is expected to be more closely related to young people's extracurricular activities than to characteristics of schools. In contrast, digital engagement of young people is presumably related to 47 regional factors linked to school neighborhood, but the available data did not allow an analysis of such characteristics. For this reason, the analyses of the articles in this study are limited to multivariate methods. The multivariate analysis involves, as the term suggests, more than two variables. In fact, according to a strict definition, the multivariate analysis involves at least two dependent and at least two independent variables. Most multivariate methods, like the regression analysis and the analysis of variance which were mainly conducted in articles of this dissertation, are special cases of general linear methods (GLM), used to generate numerical solutions to differential equation. (e.g., Baur & Lamnek 2007, 3120–3121.) The multiple regression analysis, which was applied in the second and fourth articles of this dissertation, is an extension of simple linear regression and produces an equation that predicts the dependent variable from independent variables. The model for the multiple linear regression is formulated as: y =β0+β1 x1+ β2 x2+. . .β p xp+ε where y is the dependent variable, x is the independent variable, β0 is the constant or the intercept, β1represent the slope (beta coefficient) for x1 etc., and ε is an error term meaning an unexplained variation, treated as a random variable, in the dependent variable. The model parameters β0, β1, β2, … β p and ε needs to be estimated from the data. The multiple regression analysis allows an analysis of the relationships between one continuous dependent variable and two or more independent variables, but the association within variables does not necessarily imply causation. (Nathans, Oswald & Nimon 2012, 1–2; Yan & Su 2009, 1–3). Because this study does not seek causal explanations, but is rather concerned with the degree and the nature of the association between the analysed variables, multiple regression analysis is applicable for the purposes of this study. According to Amanda Fairchild and David MacKinnon (2009), the relations between variables are often more complex than simple bivariate relations between a dependent and an independent variable and the relationships can be modified by a third variable acting as suppressor, confounder, covariate, mediator or moderator. Moderation refers to a situation, where the prediction of a dependent variable from an independent variable(s) differs across the levels of a moderator variable. A moderator can influence on the strength and/or the direction of the relationship by increasing, decreasing, or changing the impact of the predictor variable. (Fairchild & MacKinnon 2009, 87 & 91). In the second article, the moderator nature of gender is analysed running the multiple regression analysis separately for both 48 genders. The basis for separate tests arise from the difference of the equations. According to Gordon (2015), the Chow test measures whether two linear regressions are equal, i.e. whether an entire regression model differs within subgroups. The Chow test statistic is described as: (Sc − (S1+S2)) /k (S1+S2 )/ ( N1+N 2− 2k ) where Sc is the sum of squared residuals from the combined data, S1 and S2 are the sum of squared residuals from the first and second separately run group, N1 and N2 are the number of observations in these groups and k is the total number of parameters. The Chow test statistic follows the F distribution with k and degrees of freedom being N1+N 2− 2k . (Gordon 2015, 315–320 and 348.) The Chow test is applicable to determine whether the independent variable has different impact on different subgroups of the sample. In the case of gender in the second article, the Chow test statistics indicates that gender acts as a moderator variable for both dependent variable (skill) and independent variables (usage). In the case of regression analysis, it is important to evaluate the statistical significance of the estimated parameters in regression models along with the goodness-of-fit of the model and the measures of the model’s predictive power. The F-test (analysis of variance) of the overall significance of the regression model is a specific form of the F-test measuring a model with no predictors to the specified model; the rejection of the null hypothesis means that the fit of the intercept-only model is significantly reduced compared to specified model. The F- test also enables to compare different models and to decide which model best fits to the sample. The t-test, instead, measures the significance of individual coefficients within each model. (e.g., Gordon 2015, 184–185; Montgomery, Peck & Vining 2015, 25–29.) The predictive power refers to the R-squared (R²) value, i.e. the basic measure of the proportion of the variance in the dependent variable that is explained by the predictors. The value of R² range between 0 and 1; values closer to 0 represent a poor fit and value 1, a perfect fit. It should be noted that the value of R² increases as additional predictors are added into the model. An adjusted R- squared is thus more suited as it deals with this issue by reducing the degrees of freedom concurrently with added variables. (e.g., Gordon 2015, 199–200; Montgomery et al. 2015, 48; Yan & Su 2009, 166.) The Durbin-Watson test is the test for confirming the critical assumption of independence, i.e. detecting the presence of autocorrelation in linear regression (Montgomery et al. 2015, 475–477; 49 Yan & Su 2009, 235). In addition, the normality of the residuals and their distribution, as well as the potential impact of the outlying observations and their influence on the results, should be evaluated in the case of all types of regression models (Gordon 2015, 425–426; Mood 2010, 80–81). The model fit estimates described above are reported in original articles. In the fifth article the dependent variable (students’ intention to study or work in the ICT field in the future) is dichotomous (intended or not) and therefore it requires the utilisation of logistic regression. According to Chao-Ying Peng and Tak-Shing So (2002, 35) logistic regression is suited for examining the relationships between a dichotomous, for example qualitative, dependent variable and one or more independent predictors. In its basic form, logistic regression applies a logistic function to model a binary dependent variable. Possible values of the dependent variable are either 0 (i.e., indicating non existence) or 1 (i.e., indicating existence). The difference between the linear and the logistic regression is that the logistic regression transforms the mean of the dependent variable by applying a logit link function, whereas in the linear regression dependent variable is left untransformed. The reason for the logit transformation is that the categorical outcome variables are meant to approximate the probability of observations falling into these possible categories causing the relationship between the covariates and the dependent variable to be s-shaped, not linear. In the logistic model, the logarithm of the odds (the log-odds) for the existence (value 1) is a linear combination of one or more independent variables. The independent variables can be either categorical or continuous variables. (Mood 2010, 68; Peng & So 2002, 35; Pampel 2000, 10–18.) Having multiple independent variables construct a complex logistic regression described as (Mood 2010, 68): ln ( π1 − π )=β0+ β1 x1+β2 x2+.. .+ βk xk+ε where π is the probability that the dependent variable y = 1. For interpretation purposes the logit is usually transformed to odds; the odds that y i= 1 is obtained by exp(logit), and the probability by: exp ( logit ) (1+exp (logit ) ) 50 Therefore, the logit varies between -∞ and ∞, but translates to probability which ranges from 0 to 1. (Sperandei 2014, 15; Mood 2010, 68.) Usually the results of logistic regression are presented either in terms of odds ratios (OR) or log-odds ratios (LnOR) (Mood 2010, 68). The evaluation of the goodness-of-fit of the logistic regression differs from the evaluation of ordinary linear regression. Instead of F-test, the overall model significance for the logistic regression is examined using the Chi-square test. Unlike in the case of the traditional R-squared, the value of the Nagelkerke R- squared is commonly used to examine the proportion of variance explained by the independent variables. In addition, the Hosmer-Lemeshow test is used for evaluating the goodness-of-fit for logistic regression models measuring, whether the observed values match the expected values in the subgroups of the model population. (Hosmer & Lemeshow 2000, 143–166; Nagelkerke 1991.) As with other types of regression analysis used in the original articles, the above measures are reported in more detail in the fifth article which applies logistic regression. Carina Mood (2010, 79) has raised an important point regarding the logistical regression: because the coefficients of logistic regression are dependent on both the effect size and the magnitude of undetected heterogeneity, coefficients between models or samples cannot be straightforwardly interpreted or compared which is a usual practise with linear regression models. Although these issues should be known by sociologists applying quantitative methodology, they are typically ignored. In the fifth article, this is taken into account and such comparisons are deliberately avoided. Long-term Preservation of the Data and the Instruments The FAIR Principles seek to foster findability, accessibility and reusability of research data and to further scientific data management and stewardship (Wilkinson et al. 2016). However, these principles do not obscure the principles of research ethics or other regulations, such as the national guidelines for investigating minors. According to the Finnish National Advisory Board on Research Ethics (TENK 2009, 6), scientific research taking place in educational institutions can be conducted as part of the normal school day, and the guardian's permission is not required if the head of the school has assessed the study to provide useful information for the school. Research licenses have therefore been requested from school leaders or from other authorities depending on the regulations of each participating municipality. Due to the terms of these research licenses concerning under-aged participants and the consents requested from the 51 participating students, the utilised data cannot be opened and shared openly. However, in accordance with the principles of proper data management, the data is stored in digital long-term storage at Zenodo. Zenodo is a general-purpose open- access repository under the European OpenAIRE program, operated by the European Organization for Nuclear Research (CERN), allowing the deposit of data sets, software and any other research related digital artefacts. The stored research data consists of the following data sets: 1. Kaarakainen, M.-T. (2019). The original ICT skill test data [Data set]. Zenodo. DOI: 10.5281/zenodo.2605006 2. Kaarakainen, M.-T. (2019). The renewed ICT skill test data; basic education level subset [Data set]. Zenodo. DOI: 10.5281/zenodo.2605515 3. Kaarakainen, M.-T. (2019). The renewed ICT skill test data; secondary education level subset [Data set]. Zenodo. DOI: 10.5281/zenodo.2605513 The source code of the applications used for the research are also stored in Zenodo for long-term preservation. Unlike datasets, source codes have been opened under restricted access. On request and for a valid reason, these research applications can also be used in other studies, although they need to be upgraded to better match the current digital environment and visual look of applications. The test applications should also be upgraded to run with the currently supported PHP and database versions. The language of the research applications is Finnish and in the renewed test also Swedish, limiting their re-usability. However, the renewed test application supports multilingualism, so other language files can be added to the application, although this necessitates some further development. The stored source codes consist of the following repositories: 1. Kaarakainen, M.-T. (2019). The source code of the original ICT Skill Test application [Software]. Zenodo. DOI: 10.5281/zenodo.2621283 2. Kaarakainen, M.-T. (2019). The source code of the renewed ICT Skill Test application; Finnish version [Software]. Zenodo. DOI: 10.5281/zenodo.2621306 52 3. Kaarakainen, M.-T. (2019). The source code of the renewed ICT Skill Test application; Swedish version [Software]. Zenodo. DOI: 10.5281/zenodo.2621321 53 6 Results This dissertation thesis provides answers to four research question. The first question, To what extent the gender makes a difference in digital engagement of students in Finnish lower and upper secondary schools?, is discussed in detail in the article Differences between the genders in ICT skills for Finnish upper comprehensive school students: Does gender matter? (Article III). This article examines the differences between genders in digital skills among lower secondary education students. The gender-related topic is also addressed in other articles even though they focus on other topics. The theme of the next question, How does the age of Finnish upper secondary school students affects their digital engagement?, is central to the original article: Seeking adequate competencies for the Future: Digital skills of Finnish upper secondary school students (Article IV). This article concentrates on examining the digital skills of Finnish upper secondary school students and how these skills are associated with students’ educational choices, future educational and occupational intentions and the age of the participants during the secondary education studies. Similarly to how gender is featured in the other articles, age is also an important variable when examining digital skills and usage in the other articles, although they are more focused on other factors. The third question, How are gender-segregated fields of education and future intentions associated with digital engagement of 12–22 -year-old Finns?, is discussed in three of the original articles. The issues related to education level are central in the first article (Performance-based testing for ICT skills assessing: a case study of students and teachers’ ICT skills in Finnish schools) and the second article (Information skills of Finnish basic and secondary education students: The role of age, gender, education level, self-efficacy and technology usage). The first article focuses on the measurement of digital skills and the classification of such skills, but also on the differences in digital skills between lower and upper secondary school students, as well as between general and vocational secondary education students. The second article analyses especially one of the areas of digital skills, information skills that refers to the ability to use digital technologies 54 for searching, selecting, processing, and evaluating information. In addition, the second article analyses the usage habits of digital technologies and the relationship of usage habits and information skills among students. In the fourth article the role of gender-segregated fields of education and the future intentions of young people are the main focus of interest. The last question, To what extent and in what ways does digital engagement accumulate, as exhibited by certain individuals more than others among Finnish lower and upper secondary school students?, is particularly relevant to the second and fifth articles. The fifth article, Digital abilities and ICT intentions of future labor market entrants in Finland, examines the digital abilities of Finnish upper secondary education students concentrating especially on students’ intentions to study or work in the ICT field in future. Although the accumulation of skills and usage and their interrelationships are central to these two articles, as a theme it is relevant to all of the original articles included in this dissertation thesis. Digital Engagement by Gender The role of gender in relation to digital skills is a central theme particularly in the third article (Differences between the genders in ICT skills for Finnish upper comprehensive school students: Does gender matter?). The article analyses the data concerning only the lower secondary education students in the sample II, applying the renewed ICT Skill Test. The results of this article shows that there is only a small, but statistically significant difference between the genders, in favour of female students, when analysing students' performance in the ICT Skill Test at the total score level. A more detailed item-level analysis, however, reveals significant differences between the genders in digital skills according to the subject matter of the test item (see table 1 of article III) as male students tend to get higher scores from more technical-oriented items than females, and female students score higher in school work -oriented and social interaction -related items than male students. Therefore, the differences in digital skills between genders tend to relate to the subject matter of the test, implying that these differences are rooted in more profound gendered preferences and attitudes toward technology. The first article (Performance-based testing for ICT skills assessing: a case study of students and teachers’ ICT skills in Finnish schools), based on sample I (both lower and upper secondary school students), where gender differences are just one of the topics to be considered, confirms the above-mentioned results. The results of this article, based on the version of the ICT skill test emphasising computer literacy at the expense of wider digital skills, show that the overall 55 performance of male students is slightly better than that of female students (table 3 of article I). However, the more substantial gender divide appears in the advanced and professional technical skills, in which male students clearly outperform the female students. These results are consistent with the results of the third article, as presented in the previous paragraph, and with the traditional understanding of males being more technically-oriented than females. However, along with this, the results of the first article emphasises that the more technical the tasks, the smaller the number of students who master them and the greater the differences between the genders became. Thus, male students clearly dominate technical skills, but this dominance in the most technical tasks is in fact caused by a relatively small group of technically competent males. The fifth article (Digital abilities and ICT intentions of future labor market entrants in Finland) focusing on the upper secondary school students in sample II and applying the renewed ICT Skill Test, confirms (table 2 of article V) that the medium-related digital skills, necessitating abilities to use the functionalities of the devices, software and the Internet, and especially programming skills, consisting of logical reasoning, web programming and knowledge of databases, tend to be especially male-dominated areas of expertise. In the fifth article, gender also stands out as an influential factor for students’ intentions to apply to study or work in the ICT field in the future (table 4 of article V), leading to the assumption that the majority of the possible future ICT applicants will continue to be consist of mainly male students in the future. The second article, Information skills of Finnish basic and secondary education students: The role of age, gender, education level, self-efficacy and technology usage, applies the original ICT Skill Test examining information skills and the digital technology usage habits of participants from both lower and upper secondary schools. Based on the results of this article, gender serves as moderator variable, as both the skills and the usage of digital technology between males and females are different in terms of the areas of expertise and the types of usage. The results (see table 4 of article II) shows that social use is the most frequent type of digital usage among both genders, followed by personal use. Social use includes the usage related to communication, maintaining social relationships, networking, sharing one’s own digital content in networking sites and playing multiplayer games. Personal use, instead, includes individual game playing and the usage of different kind of digital entertainment services. The results highlight the importance of social relations and recreational leisure in the daily, digitalised life of young people in Finland. Economic use, including public and commercial services and learning- and information-oriented usage, is far less frequent among 56 young Finns than social use and personal use. Cultural use, such as creating one’s own digital content and sharing digital contents on different kinds of online services, is the least frequently cultivated type of usage. The results show that male students are more active in economic use and personal use than female students, whereas female students are more active in cultural use compared to males. Moreover, the results of the second article show that male students are more versatile users of digital technology than female students. However, the distribution among male students is wider than among female students as both the most restricted and the most diverse users of digital technology are male students. In summary, gender appears as a social category that produces differences in young people's digital engagement. Both the digital skills of the students and their digital usage differ between male and female students especially with regard to their type and domain, rather than in terms of quantity of usage or level of skills. This fact indicates that gender differences in digital engagement are largely domain-specific and related to gendered preferences and interests, in other words tendencies towards the ways of experiencing digital technology and available digital afforcances. Because the patterns of these preferences appear clearly in the data concerning lower and upper secondary school students, they are most likely to develop during the early years of childhood and youth. Digital Engagement by Age The fourth article, Seeking adequate competencies for the Future: Digital skills of Finnish upper secondary school students, examines students of upper secondary schools (sample II) and applies the renewed ICT Skill Test. The results (see table 2 of article IV) indicate that age as an explanatory variable has a significant impact on students’ digital skills among 15–19 year-old upper secondary school students in Finland. The skill-increasing effect of age is stronger among those Finnish students who study in vocational upper secondary schools than among those who study in general upper secondary schools. Based on cross-sectional data, vocational school students seem to improve their skills during the upper secondary education studies to the extent that they manage to close the skills gap that is clearly visible between general and vocational upper secondary school students at the beginning of upper secondary level studies. As commented on the article, this is an interesting and contradictory observation, as vocational training has not been considered as effective as academic education in the development of such digital problem-solving capabilities. It is evident that the curricula and the learning objectives in vocational education seem to be more oriented towards occupational skills’ requirements and 57 students adult life as a citizens of the information society, when compared to the curricula in general education which, in turn, focuses more on the use of digital technology in learning-related contexts. This is assumed to be the cause of the positive impact on the digital skills of vocational upper secondary school students. The fifth article, Digital abilities and ICT intentions of future labor market entrants in Finland, examining the upper secondary education students from the sample II and applying the renewed ICT Skill Test, brings out the importance of age for young people's digital usage. With regard to the students’ digital usage (see table 2 of article V), with increasing age of the students, economic use of technology turns out to be more frequent and in fact the most popular usage domain among the oldest participating students. This is most likely explained by the economic independence of older young Finns in the sample and the curricula requirements in upper secondary education as these two together increase usage related to public and commercial services, as well as learning- and information- related usage. Unlike economic use, age as an independent variable is not seen affecting on the frequency of social, personal or cultural use of digital technology of 15 to 22 year-olds. Overall, the original articles show that age, even among young people, has an impact on both digital skills and digital usage habits of digital technology. The importance of age as an independent variable among young people is explained, in particular, by the increasing versatility of technology use with age. On the contrary, based on the results of the fifth article, age is not considered to be a relevant factor for the likelihood of students expressing their desire to study or work in the ICT field in future. Thus, age is unrelated to the emergence of gendered educational preferences or at least these preferences evolve at an earlier stage of childhood and therefore are not scrutinisable with the available data. Educational Choices and Digital Engagement The age-related results suggest that education could have a central role in digital skills as the digital engagement increased with the age of the students. Next, this aspect will be discussed in more detail from the point of view of the level and the field of education. The first article shows (table 5 of article I) that there are considerable differences in skills between lower and upper secondary school students. In particular, there are large differences in tasks that require basic technical know-how. When examining the tasks belonging to the advanced or professional levels, the difference between students from different education levels remains more minor even though upper secondary school students are still 58 performing significantly better than lower secondary school students. The second article gives evidence that education level is, in fact, the most influential factor in digital skills among the examined factor variables (gender, age and education). The results (see table 6 of article II) also suggest that the effect of education is even more significant among male students than female students as the difference between lower and upper secondary school students’ skills is more wider in scale among male than female students. In the second article, the level of education is also linked with the usage of digital technology among students. In fact, the relationship between the education level and usage is more evident than the similar relation of usage and age (see table 5 of article II). Education level increases the students' overall use of devices and their digital engagement in domains of economic, social, personal and cultural use and the versatility of their digital usage. Particularly, on the upper secondary education level, students’ digital usage related to economic purposes increases. Together with economic use, social use and the versatility of students’ overall usage of digital technology are notably higher among upper secondary school students than among lower secondary school students. On the contrary, the increasing effect of education level on personal use and cultural use domains is only minor. Furthermore, students' overall digital engagement and versatility of usage increase together with the increase in the education level. The increase in versatility is most likely due to the fact that the information- and learning-oriented use becomes more abundant with the upper secondary level studies as nowadays in Finland the studies in upper secondary schools require the regular use of digital devices for learning. As has already indicated in relation to age, economic use also increases as the result of abundant use of different public and commercial online services among older youth. Both of these have a diversifying effect, maturing students' daily use of digital technology. In addition to the level of education, it is important to examine the other aspects of education as well in order to achieve a comprehensive picture of the importance of education in digital engagement. The first article indicates that besides the differences between the education levels, there are also differences within levels of education as general and vocational upper secondary school students differ from each other in terms of their digital skills (see table 5 of article I). Here it should be remembered that in Finland the vocational education covers altogether eight fields of education and includes more than 50 vocational qualifications. Therefore, the differences within upper secondary education level, constituting from a diverse set of educational choices instead of a simply general/vocational division, needs to be 59 taken into account. This is especially the purpose of the fourth article as it aims to take into account the multidisciplinary nature of Finnish secondary education. The fourth article shows (figure 3 of article IV) that educational choices in general upper secondary education (i.e., did the student take part in the advanced or basic syllabus in mathematics) and especially in vocational upper secondary education (i.e., the field of study the student participated) are of considerable importance. In fact, the difference in the digital skills between the worst and the best performing group (i.e., educational choice) is more than double. The best performing students are students from vocational upper secondary schools studying either in a field of natural sciences (qualification in ICT, specialisation in software development) and in a field of culture (qualification in audio-visual communication), and students from the general upper secondary schools studying advanced syllabus in mathematics. In contrast, the weakest performing students are those vocational upper secondary school students studying in a fields of natural resources and environment; tourism, catering and domestic services; and social services, health and sports. The importance of the different fields of education in digital competence are not only related to the current educational choices but also to the students' future educational and occupational intentions. In this dissertation thesis, the future intention is based on the students' own announcement of the field in which they are planning to apply for further studies or to work at the end of their current education, as operationalised on the basis of the international standard classification of the fields of education and training (ISCED-F). As indicated in the fourth article (see table 5 of article IV), digital skills vary greatly among students depending on in which fields students intend to apply for further study or work in the future. The students with the best digital skills report their future intention to be information and communication technology (ICT) or natural sciences, mathematics and statistics. In contrast, students with the least digital skills report favouring the fields of agriculture, forestry, fisheries and veterinary, services and education (vocational upper secondary school students) or basic syllabus in mathematics (general upper secondary school students). It can be seen that the most skilled students announce preferring the traditionally more male-dominated fields of education, whereas the students with weaker digital skills tent to favour more female-dominated fields of education and occupations. In terms of digital engagement, the skills gap is therefore clearly associated with the gender- segregated fields of education indicating that the horizontal segregation in the digital engagement of Finnish young people is an evident fact. 60 All in all, based on the results of the original articles, education is identified as the most significant single structural factor that produces differences in digital engagement among youth. Education manifests itself as a categorical social hierarchy, as the level of education increases young people's digital engagement. At the same time, the observed differences in digital engagement within the same level of education are connected to the gendered preferences and interests. Because digital engagement is most likely exhibited by students in the male-dominated fields of education, it is related to factors' that lead young people to drift into gender-segregated fields of education. As both educational choices and technology orientation are heavily gendered, they tend to reinforce each other and thus exacerbate gender differences in relation to digital engagement and the ability to take advantage of the potential digital affordances among the future citizens of the information society. Accumulation of Digital Engagement The last research question, To what extent and in what ways does digital engagement accumulate, as exhibited by certain individuals more than others among Finnish lower and upper secondary school students?, aims to draw together the themes that pass through all the five original articles, in one way or another. These themes are examined by reviewing the results of the original articles relating to the relationships within and between the digital skills and usage, focusing on, in particular, compoundness and sequentiality of digital usage and skills. Particularly, the fifth article give evidence of the compoundness of digital skills. Based on its results (see table 2 of article V), having skills in one area also increase the likelihood of having other kind of digital skills as the correlation between medium- and content-related skills is notably strong (r = .72). Figure 3 illustrates this phenomenon of the compoundness of digital skills. The figure is produced from sample II and includes both lower and upper secondary education students. As can be observed, the figure shows a clear pattern how medium-related and content-related skills correlate with each other; usually if the individual masters medium-related skills he/she also possesses content-related skills (point a). The figure also shows that the correlation between content- and medium-related skills is stronger among male students (Pearson’s r = .77) than among female students (Pearson’s r = .69) as the blue dots form a steeper curve relative to the green crosses, indicating that the compoundness of digital skills is stronger among male than female students. However, the figure also confirms that having one type of skills does not necessarily guarantee the mastery of other types 61 of skills; as can be observed, the tested students could master, for example, medium-related skills quite well (point b) without having the same level of content- related skills, and the other way round (point c). Figure 3. The Relationship Between Content- and Medium-Related Digital Skills (Pearson’s r = .72) Based on Data from Sample II (N = 8,661). The results of the fifth article provide evidence also for the compoundness of digital usage. When looking at the correlations (see table 2 of article V) between the versatility of use i.e., how many usage targets an individual has at least on occasionally basis, and activity in different usage domains, it is evident that activity in economic, cultural, social and personal usage domains correlates remarkably with the versatility of overall usage. In addition, the activity in a particular usage domain correlates with the activity in other domains. Social and economic uses, in particular, seem to increase the likelihood of being an active user in all other domains of usage as well. The least compounded features describe the personal use which correlates at moderate level only with social use while its correlations with other domains remains negligible. In contrast to compoundness, sequentiality of digital engagement relates to the relationship between digital skills and usage describing how digital engagement 62 accumulates. This question is a central theme in the second and fifth article as they examine the relationship between usage and skills. Based on the results of the second article (see table 6 of article II), the versatility of use is, in particular, associated with favourable digital skills. Results also indicate that certain kinds of usage increase skills more than other usage purposes, as economic use correlates with skills more than other usage domains. Furthermore, the skill-increasing effect of usage is not the same for both genders. The clearest example is social usage which increases male students' skills, but not the skills of female students. The more detailed examination in the second article reveals significant gender differences within this usage domain between genders, and the main distinguishing activity between male and female students within the social use domain is whether or not the activity in this domain includes multiplayer gaming. Based on the results in the second article, multiplayer video-games are a major usage purpose among social uses for males, but female students generally do not report gaming as their key usage activity. Instead, female students tend to emphasise social networking and digital communication within this usage domain. The results indicate that communication and networking activities among male students enable and support other, more exploratory and intrinsically meaningful online activities such as game playing. Instead, the importance of social interaction and companionship as such appears to be important reasons for social use among female students. For male students potential learning experiences seems to emerge as a part of their online activities within the social use domain precisely because social use among males contributes to wider exploratorial use of technology and the Internet. No similar positive effect exists among female students because maintaining social relationships and communication as usage purposes per se do not have the same skill-enhancing effect. The importance of this finding is that some usage habits, even within the same usage domain, are potentially more profitable than others. Consequently, the type of the digital usage is important for the accumulation of digital skills. The fifth article provides additional understanding about sequentiality of digital engagement. Based on its results, especially economic use and versatility of digital usage are associated with digital skills (see table 2 of article V). However, these correlations in fifth article remain rather low, indicating that the association between usage and skills is not straightforward and a certain amount of digital usage does not automatically lead to the development of useful skills for each individual. Altogether, the findings of the second and fifth articles of this dissertation thesis admittedly show that digital engagement is sequential in nature. In addition, the results of these articles indicate that some usage habits are more 63 profitable than others, and that there are noteworthy differences between genders in the patterns of the sequentiality. In summary, digital competence and usage tends to come to mark certain individuals. More precisely, concepts of compoundness and sequentiality successfully describe the nature of digital engagement among Finnish adolescents. Skills and usage are intertwined and mutually reinforcing. Nonetheless, no amount of use or level of skills guarantees sufficient talent for success in the information society because the quality and type of digital usage is relevant to its power to produce relevant digital skills. Certain usage habits are more profitable than others and enhance such digital engagement that is more likely to be beneficial, providing the abilities to identify and exploit the action potentials of the available digital affordances. 64 7 Conclusion The aim of this work is to contribute to the narrow scope discussions around digital technology in education. To be more specific, the purpose is to not only further the identification of the wide-ranging opportunities of digital technology, addressed as digital affordances, but also to raise awareness about the risks of digital exclusion because it severely reduces the opportunities for individuals in the information society in many areas of life. The results of this work confirm a number of previous research findings (e.g., Hatlevik et al. 2017; Hargittai & Shaw 2015; van Deursen & van Dijk 2014; van Deursen et al. 2011; Helsper & Eynon 2010) that have shown that gender, age and education are causing divergence in individuals' digital engagement leading to differences in individuals abilities to make use of digital affordances. This study also provides clear evidence that this kind of disparity, referred to as digital inequality, also exists in the Finnish society, as exemplified by the disparities among the lower and the upper secondary school students. In addition to confirming previous research results, this study brings out fresh facts that will enhance and diversify the understanding of the issues of digital inequality and digital technology in education. To begin with, this work provides more accurate information on the gender differences in digital skills and usage, the results of which have been contradictory in previous studies (e.g., Correa 2016; Aesaert & van Braak 2015; van Deursen & van Dijk 2015; van Dijk 2013; Correa 2010; van Deursen & van Dijk 2010a; Fuchs 2009), when indicating that male students are consistently more competent than female students in tasks that require more technical knowledge or computer literacy, whereas female students possess higher skills in tasks related to school work and social interaction with digital technology. Thus, neither gender is better than the other, but the males and females tend to orientate to different domains of interest and expertise. Results of the articles included in this dissertation thesis therefore certify that there exist domain- specific gender gaps in digital skills and the issue is far more complex than the binary concept of being skilled or unskilled implies. 65 The more versatile digital usage of males, compared to females, seem to indicate that for many male students, technology, devices, and virtual environments are objects of exploration and experience offering thus more opportunities for learning than for the majority of female students whose technology use is more task-oriented and thus limited to the fulfillment of a present goal. This resonates with Robinson's (2009) findings about young people's digital usage. Males seem to be more likely engaging with Bourdieusian 'serious play' and 'studious leisure' during their technology use, while females’ aspiration to reach a particular goal with technology prevents them from being exposed to such exploratorial usage habits. Under these circumstances, the interdependence of digital usage and digital skills increases the likelihood that beneficial experiences and digital action potentials will accumulate for males rather than for females. Consequently, the results of this dissertation thesis emphasise the significance of gendered preferences toward technology. Disparities between genders in digital skills and usage suggest that gender differences are closely intertwined with preferences and attitudes, acquired via socialisation, causing gender-oriented interest toward technology and digital engagement. This observation provides support for van Dijk’s (2013; 2005) assumptions that the unequal distribution of resources, especially the more personal ones, has a significant impact on digital inequality. The time spent by youth on digital technologies and the importance they give themselves to being online seem to be the most prominent resources that determine young peoples’ attitudes towards digital technology. In the social relations of males technical aspects are presumably more valued and thereby they tend to produce more positive stance towards technology than females. Of the factors studied in the original articles, education proves to be the most important factor that affects digital engagement. However, the link between education and digital engagement is multifaceted and does not just refer to the level of education. Specifically, the level of education has a particular impact on mastering the skills needed to use digital devices, applications or the Internet, and the skills enhancing effect of the education level is stronger among male students than among female students. Despite this, the level of education does not increase the digital skills of young people as such, but indirectly by diversifying digital usage and as a result of the requirements of upper secondary education studies. Similarly, the skill-increasing effect of age, shown in the results, is mediated through diversifying usage. In contrast, the educational choices such as the fields of study or curriculum within the same level of education emerge as factors by which digital skill differences are most clearly manifested. In general, better digital skills are noted by students studying in the male-dominated fields of education, whereas 66 the skills of students in the female-dominated fields remain significantly lower. This kind of difference in digital skills is evident in terms of current education and future educational or occupational intentions, both of which are remarkably gendered among young people in Finland according to the results of this study. Overall, the results of this study confirm the existence of clear horizontal segregation in Finnish education. Because gender-specific preferences affect the students further educational and occupational intentions, students’ gendered orientations towards technology and their educational choices tend to reinforce each other and thus have a potentially far-reaching effects on individuals' life chances. The importance of the aforementioned is further strengthened by the fact that digital technology and related capabilities are central for the information society and thereby digital abilities are increasingly causing divisions between prosperous and excluded citizens by accumulating such capital for some individuals at the expense of others. The results of this work link superior digital abilities with the male-dominated fields of education, which are generally considered to be likely to lead to well-paid professions and whose demand in the labour market is expected to increase in the future (e.g., Falk & Biagi 2017b; Lindley 2016, 173), denoting the noteworthy risks of widening skill-based divide between the genders in the information society. In the light of the results of this study, the horizontal segregation is particularly strong with regard to the attractiveness of the ICT field among Finnish upper secondary education students. The low share of females in digital education and workforce has long been not only typical for Finland but also a global problem (see e.g., Dass, Goodwin, Wood & Luanaigh 2015; Korte, Gareis & Hüsing 2014). The results of this study confirm the relevance of these concerns and stress the need to provide young people with intriguing information, role models and skill-related preconditions for digital education and labour market that not only increase the attractiveness of the field, but also challenge traditional gender roles and attitudes. This is important for equal opportunities so that both genders have equal opportunities in the future labor market shaped by digitalisation. However, it has proved to be problematic to find effective ways to influence to the gendered educational and occupational choices of young people (e.g, Cheryan et al. 2017). Here, alternative approaches based on a more sociological view of digital engagement can provide an untapped opportunities. Referring to Tilly (1999), inequalities build on categorical differences emerging in the social contexts to which individuals are bound. These prevalent social conditions increase susceptibility of individuals to generate certain types of tendencies to perceive and experience digital technology and the Internet. The 67 results of this study suggest that there is a special digital habitus through which individuals experience the world and their relationship with digital technology, constantly in relation to others. Habitus evolves over time as a combination of experiences and encountered circumstances. Through digital habitus, young people sense their own place with respect to others (see Bourdieu 1989) and it affects on, for example, the ways young people perceive different digital affordances meaningful for themselves. This underlines the importance of digital referents (see Helsper 2017a; 2017b) and social conditions for the evolution of young people's preferences and tendencies toward technology. For the young people, more important than the distribution of information, for example, as a part of student guidance, are the attitudes of the social reference groups to which they feel belonging to. Thus, the interventions for increasing digital capabilities or the popularity of the ICT field professions necessitate focusing on these social reference groups. Young people surrounded by other youth valuing digital engagement and technical capability are most likely to acquire the efficient skills to needed to actively participate in various online arenas and develop a positive attitude towards technology. The interventions should therefore be targeted at wider social units than just individuals. The present social reference groups are typically not covered by formal education, but rather rooted in different online communities. These are, in fact, important and largely untapped properties that digital technology have to offer for education. This provides a perspective from which digital engagement appears to be an investment and commitment to learning of valuable skills for future citizens. Online communities combine globalisation, communication and collaboration, development of new artefacts and ideas and their further innovation creating interest-based learning environments. At best, these kind of interest-based learning environments provide young people a wealth of exploratory learning experiences leading to a positive stance towards technology and learning (e.g., Steinkuehler & Squire 2014). This kind of action exposes students to affordances and action potentials of digital artefacts through experimentation. Harnessing these fundamentally collaborative resources for learning purposes would provide genuinely authentic and motivating learning environments for students with different levels of skills and motivational interests avoiding the technocratic hyper- individualisation of learning and the instrumentalisation of digital technology in education. However, the enthusiasm related to digital technology and the Internet in education includes the risk of using technology as a learning product in a way that does not promote the intended aspirations. Utilising overly ready-made and at the 68 same time too limited environments in education do not leave room for students' exploratorial and experimental activities, and neither encourage self-production of digital content. Often such learning environments have been enriched with features familiar to users from social media connections aiming to create engaging learning experiences. However, the familiarity and ease of use of learning products is a two- sided issue from the point of view of competence development, because in order to develop, skills must be challenged. Despite this, most of these consumer products are definitely suitable for learning and teaching within their limitations. However, it must be remembered that they do not, in themselves, enhance students' digital skills or digital engagement. The driving force behind the educational objectives should not be technological advances, nor the mere use of new digital resources, but skills objectives and the relevant learning content and practices associated with them. Digital skills deserve their own learning objectives and pedagogical approaches aimed at achieving them. As stated, many popular learning products promote independent learning instead of collaboration and dialogue. Such technology can have unpredictable and detrimental effects on students’ learning and commitment to education. The risk of skill-based division in the information society, mentioned earlier in this chapter, is likely to be escalated by this kind of hyper-individualisation of learning. From the point of view of digital inequality, one of the central problems of this trend is that all too often in schools, students are expected to master the use of digital tools as a result of their digital leisure activities (e.g., van Dijk & van Deursen 2014, 156). Although admittedly young people have a lot of digital activities outside of school, there are limitations in self-learning of digital skills; do-it-yourself or trial-and- error learning is not enough to guarantee adequate digital skills to every individual (see Matzat & Sadowski 2011, 1). As the results of this study show, young people's digital skills are, above all, diverse. Therefore, there is a current and relevant concern relating to rapid digitalisation of education that the inadequate digital skills, at their worst, endanger the learning of some students. Formal education should be able to recognise students' shortcomings in digital capabilities and contribute to students' digital engagement so that learning these vital skills of the future does not remain the responsibility of the young people themselves. This presupposes that digital technology is not perceived as just a learning tool, but digital skills are seen as an important topic in itself, which is not abruptly internalised by all young people alongside other activities. Education should play a key role in moderating the disparities present among differently skilled young people in order to reduce inequalities in education and more widely in society (see 69 also Pagani, Argentin, Gui & Stanca 2015, 157). Obviously, this requires preparedness and competency from the education system. It should be remembered that both digital abilities and inequality are relational properties. As the requirements for digital engagement are constantly rising due to advancements in the surrounding technological milieu (e.g., van Dijk 2005; van Dijk & Hacker 2003) individuals are exposed to a constant risk of being left behind in various areas of the information society (see Facer & Furlong 2010). Understood as a relative matter, digital inequality is not a transient phenomenon and will not disappear as the younger generations of today grow older. Instead, the importance of digital inequality is expected to increase in future societies due to the increasing importance of digital capabilities in different areas of life. Therefore, the education system must not only offer adequate digital capabilities, but also to equip future citizens the abilities to maintain and further develop their own digital abilities in changing situations. However, it is equally important to realise that a society penetrated by digital technology is characterised by the diversification of opportunities that the digital engagement has to offer for citizens (see e.g., van Deursen & Helsper 2015, 47). The digital prospects enabled by digital technology for the individuals, such as future citizens or labour market entrants, are more versatile than ever before. Paradoxically, increasing digital prospects inevitably increase the threats to equal opportunities. Emerging digital affordances are therefore both a challenge and an opportunity for education system, when ensuring equal preconditions for children and adolescents to seize these opportunities. Overall, the results of the articles included in this dissertation thesis emphasise that the compound and sequential dimensions exemplarily describe digital engagement among Finnish students. They are therefore apt concepts for describing accumulation of profitable digital engagement; skills of one kind and usage in some area are linked with increased engagement in other areas as well. The phenomenon also has a more negative side, as lack of capability or experience in some area increases the likelihood of falling behind in digital capability also more generally. Digital engagement also proves to be sequential in the sense that more versatile digital usage tends to be associated with advanced digital skills. In addition, some types of usages are more skill-enhancing than others, which makes the quality of usage more important than the quantity of usage as such. While compoundness cumulates digital engagement, as exhibited more by certain individuals than others, sequentiality of digital engagement increases the likelihood that those individuals also benefit the most from available digital affordances. Thus, in its extreme cases, the sequentiality of digital engagement describes the path either to the digital prosperity or exclusion, making it an important 70 educational policy issue. The compound and sequential nature describing the digital engagement of Finnish students further implies that the existence of the third-level digital divide, referring to gaps in individuals’ capacity to translate their digital engagement into beneficial outcomes in their life (see van Deursen & Helsper 2015), is observable in present Finnish society. The negative effects of this development should be identified and prevented through education policy-setting. It should be noted that active and versatile digital engagement increases the likelihood of encountering not only the benefits of digital technology, but also technology-related harms and negative or abusive Internet contents (see Blank & Lutz 2018). Various negative issues related to the Internet, such as spread of false information, sexual grooming of children, identity theft or other privacy issues, have recently received a lot of space in headlines in Finland and more widely around the world. Such concerns have sometimes encouraged opinions that the use of digital technology in education should be viewed critically and to consider restrictions on it. However, the negative aspects related to technology and the Internet rather require the fostering of digital well-being skills referring to individuals' abilities to cope with the negative aspects of the Internet and to control their activities and privacy while engaging in various beneficial activities through digital technologies (see Gui, Fasoli & Carradore 2017). Such skills are in fact emerging as a key component of digital skills and are not just about threat prevention but also about managing information or message overload and multi- tasking. The inability to cope with risks and harms associated with the use of digital technology and Internet threaten the most digitally inexperienced and unskilled individuals, and in particular, according to recent results (Scheerder, van Deursen & van Dijk 2019), less educated individuals and families. Therefore, education must recognise the importance of digital well-being and equip students with the ability to protect themselves from digital abuse and overuse. Promoting such abilities in education would also help to reduce concerns that children and young people today make considerable use of digital technology in their leisure and school activities. The digital activity of young people and digitality in general should not be seen as a passing craze, but a more permanent change that individuals have to learn to cope with. This dissertation thesis also raises questions for future research. First of all, future research should scrutinise more closely the various manifestations of digital affordances and the factors that promote, or limit, individuals’ ability to exploit them successfully in their lives. This requires longitudinal, but also more qualitative approaches that delve into the experiences of individuals. The original articles of this dissertation study focus on the relationship between social structures 71 and digital inequality. One of the topics for further research is to find out what kind of interaction processes between structural factors and individual experiences lead to digital inequalities and at what stage of life these could be effectively influenced by interventions. An interesting subject on its own for further research is also the gendered patterns of engagement, manifested in Bourdieusian notions of ‘serious play’ and ‘studious leisure’ in relation to digital gaming, as they appear to be characteristic of successful technology learning for certain young men, but only rarely for young women. Further, although previous studies (e.g., van Deursen & van Dijk 2014, 520) have found other structural factors, such as socio-economic background and residence, to be less relevant to digital inequality than the factors considered in this study, future research should investigate the role of socio- economic factors in the digital inequality of young Finns. In particular, the link between socio-economic background and residence, and the combined effect of these factors on the digital engagement of Finnish students needs to be the subject of future research as urban inequalities are considered a topical issue in Finland (e.g., Hyötyläinen 2016), referring to regional differentiation of welfare in urban areas. This is most likely to be related to the digital engagement of young people, differentiating their future exposure to digital affordances. Despite the importance of the factor, in this dissertation study this issue could not be analysed due to the deficiency of the data available. Therefore, in this dissertation thesis, the major flaws in the data, and thus in the study as a whole, are in fact related to the lack of available socio-economic background information. Addressing this shortcoming would also allow for multilevel modelling. For this reason, resolving this issue should be one of the key objectives of future research. The more general limitations of this study are briefly outlined in this paragraph. As stated, research into digital skills, but also digital usage, has been plagued by conceptual ambiquity. This study does not make an exception here, as there is no commonly accepted definition of digital skills or digital usage. A viable practise for the researcher to remedy this issue is to formulate the research problem and describe the theoretical concepts used to achieve it in exemplary terms, so that the reader can reach the same comprehension of the use of these linguistic tools (see also Kivinen & Piiroinen 2006). The lack of a common conceptual language has a significant differentiating effect on research practices in the area of digital skills and usage. Conceptual ambiquity makes it also difficult to assess the soundness of evaluation methods as a whole, but also for a single instrument. This is the noteworthy limitation of the both ICT Skill Test versions used in this study. In addition, although performance-based evaluation eliminates the problems associated with self-evaluation, it also has certain inherent effects. The ability to 72 solve practical tasks is a measure of the individual's skills, but for some participants, the limited area of expertise covered by the tasks may result the performance-based testing failing to tell the truth about a person's level of expertise. As a result, even this type of assessment method is not valid at the individual level for all participants. However, in quantitative research, which is not intended to provide explanations of the individuals’ as such, this deficiency is remedied by the fact that individual-level measurement errors are ultimately mutually exclusive, providing thus a fairly reliable picture of group-level digital capability in large samples. At this point, it is appropriate to return to the premise of this dissertation thesis, the need for sociological research in the context of increasing digitalisation of education. This study accentuates that the issues of digital technology in education deserve to be the subject of careful sociological research in order to improve the understanding of the role of digital technology and related social actions in education and more widely in the information society. When expanding the limited views of digital technology in education, wide ranging digital affordances are opening for education, but above all for individual students. These are resources that should be of interest to educators, as they form the milieu where learning and social action take place. A comprehensive picture of digital technology in education and more wider on society allows natural pathways to integrate education into students' overall lives and provide opportunities to build personally meaningful paths towards the future of individual students, while also ensuring adequate digital capabilities for citizens of the information society. Sociological approach also reveals the existence of digital inequality in Finnish education and pressures the education system to focus on preventing its negative effects. The presence of digital affordances and inequality, and in particular their interconnectedness, requires a fundamental change in the way of thinking and in the objectives of digital technology in education, as well as in educational policies that guide school practices. 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Item Description P D r Basic digital skills Basic operations First, participants had to choose the correct options for entering special characters that were not included in the QWERTY keyboard (i.e. ®, ≤, α). In the second task, participants had to evaluate which clipboard statements were correct. .44 .74 .56 Word processing In the first task, participants had to choose from a list of options what modifications (paragraph and page formatting, header and footer) had been made to the text documents presented. In the second task, they had to choose from among a list of options how the desired modifications could be implemented (indexing, page numbering, and page break). .63 .84 .56 Spreadsheets Participants had to choose the right formula for the spreadsheet cell and, accordingly, the right function to solve the tasks presented. In addition, they had to select the appropriate formatting actions for formatting and ordering the cell content. .45 .85 .55 Presentations Participants had to select actions to achieve the desired features (how to insert background and bullets/numbering, and formatting charts and graphics) to the slideshows presented. .47 .87 .55 Information seeking Participants were required to select the best sources of information for specific situations, write an appropriate search query to a simulated web search engine for a given search situation, and evaluate and select relevant and reliable results for a given information need from the simulated ‘search engine results page’. .42 .42 .48 Social networking Participants had to choose the most appropriate and safest option for social networking cases. .44 .62 .37 Image processing Participants had to choose how to implement desired formatting on images presented (brightness and colours, cropping the picture, and/or removing elements from the image). .78 .63 .52 Web content creations In the first task, participants had to choose from five html outputs the correct match for the given html code (a simple example containing text, link, input field, and font colours). In the second task, participants had to evaluate which claims about the (Finnish) exercise of freedom of expression in the mass media were correct. .36 .78 .55 Advanced technical skills Operating system installation and initialisation Participants had to evaluate which statements about operating system installation and initialisation were true. .26 .59 .69 Software installation and initialisation Participants had to evaluate which statements about software installation were correct and to choose from given options which operations were needed during the software installation. .44 .83 .66 Maintenance and updating Participants had to evaluate which statements about maintenance and updating of software were correct. .39 .80 .67 Information security In the first task, participants had to choose the correct .33 .71 .58 98 action/conclusion in the case where it turns out that a web service stores user passwords in a clear text format. In the second task, they had to choose which options were not proper information security methods. Information networks In the first task, participants had to evaluate which statement about denial-of-service attacks was correct. In the second task, participants had to identify the information network techniques and match them with the presented network graph. .37 .77 .72 Professional ICT skills Server environments Participants had to choose the correct statements regarding logical volume management and hot swapping. .14 .52 .71 Database operations Participants had to select the correct SQL query for a given situation. In addition, they had to choose the correct description for the database schema presented. .12 .45 .69 Digital technology In the first task, participants had to choose the best match between the options and the presented graph on logic gates. In the second task, they had to choose on which area of mathematics digital technology is based on. .11 .40 .59 Programming In the programming tasks, participants had to select the correct description for a particular pseudo-code example and select the values of given variables after executing the code. .16 .49 .65 P = Item difficulty index (optimal range between .2 and .8), D = Item discrimination index (threshold value < .2), r = item-total correlation (threshold value < .2), Cronbach’s alpha of the entire scale .86 ( threshold value < .7) 99 Appendix 2. The usage habit questionnaire items from the original ICT Skill Test and their categorisations for usage domains. Item Economic use Cultural use Social use Personal use Social networking services x Video-sharing services x x Photo-sharing services x x Web blogging x x Internet discussion forums x x E-government services x Online banking x Online shopping x Online newspapers x Newsgroups x Weather services x E-mailing x x Instant messaging x Voice/video chatting x Video/computer games (in single-player mode) x Video/computer games (in multi-player mode) x x Casual gaming x Search engines/information searching x Web-mapping/route planning services x Vertical directories x Wikis x Online dictionaries x Watching TV-series online x Downloading/listening to music online x Downloading/watching films online x 100 Word processing x Spreadsheets x Presentations x Image manipulation/editing x Audio editing x Video editing x Computer graphics x Computer programming x e-learning environments x 101 Appendix 3. The renewed ICT Skill Test test items and their categorisation, description and the results of item analysis. Item Description P D r Medium-related skills: Basic operations Participants had to pair a keyboard shortcut with a correct action and select the correct type of computer memory for the particular situation. .21 .61 .44 Installation and updates In the first step, participants had to choose whether the statement refers to an installation or an upgrade, and in the second step, they had to choose whether the statement relates to an update or an upgrade. .49 .85 .58 Information networks Participants were given four network usage scenarios and had to pair them with the correct data transmission technologies and then match the correct descriptions of computer network- related concepts with given options. .18 .47 .36 Word processing Participants were asked to edit (bold, italicize, underline and highlight) the sample text presented. .54 .99 .48 Spreadsheets Participants were asked to fill in the spreadsheet with the information provided, to bold the title row, and to sort the table in ascending order. .29 .73 .52 Presentations Participants were given a general user interface view of the presentation software. The task was to match the named functions with the right parts of the image. .31 .80 .52 Content-related skills Social networking Participants had to pair the correct social networking services with the service descriptions, define the meaning of social networking service, and select the correct alternatives related to the security of social networking services. .41 .64 .60 Communications Participants had to complete the e-mail receiver fields (carbon copy and blind carbon copy) and add an attachment according to the instructions provided, and identify the types of information that can be used to identify Internet users. .46 .80 .66 Information security Participants had to choose the correct statements for secure network communications and choose from given alternatives those that related to the security of the computers in a foreign Internet cafe. .43 .74 .65 Image processing Participants had to select the correct image processing tools for cropping the image presented and making the person appearing in the image unrecognizable. Afterwards, participants had to choose the correct image processing statements from the options and select the correct file formats for vector graphics. .33 .58 .59 Video and audio processing First, participants had to choose the methods that can be used to edit video footage from a single camera, and then choose the correct answer to the question, “Which one of these alternatives is related to lossy audio compression?”. .44 .82 .64 Cloud services and publishing In the first phase, participants had to choose which statements about the cloud services were true. In the second step they had to choose the correct YouTube-video sharing option that allows .44 .90 .58 102 limited sharing even for those who do not have an account on YouTube. The third phase was the follow-up question: “Can we now be sure that the video will not spread to the rest of the Internet for outsiders to see [...]?” Software purchasing Participants had to choose what to consider when evaluating the security of mobile applications, and select the correct definition of personal data protection. .22 .52 .48 Information seeking Participants had to select the correct source/channel to look for more information on the topic presented. After this, they were presented with list of search engine results and were asked to select relevant and reliable results related to the given scenario. .63 .55 .39 Programming skills: Elementary programming Participants were required to write, per instructions (i.e., L = 90 degrees to the left, F = one step forward...), a maze traversing script that leads from the starting point to the end. After this, they were presented with a short pseudo-code and they had to write the value of a given variable after executing the given code. .09 .30 .43 Database operations Participants had to form an SQL-query based on instructions and a simple database schema provided, and then choose the correct definition for the term ‘NoSQL database’. .05 .17 .21 Web programming Participants were presented with three files (HTML, CSS and JavaScript) and the view generated by these files. Participants had to select the right answer to the questions on how to edit the simple web page view and what were the dependencies between these given files. .08 .28 .26 Programming The programming task required the participants to place Java code lines in the correct places based on the comment sections provided. .01 .04 .25 P = Item difficulty index (optimal range between .2 and .8), D = Item discrimination index (threshold value < .2), r = item-total correlation (threshold value < .2), Cronbach’s alpha of the entire scale .87 ( threshold value < .7) 103 Appendix 4. The usage habit questionnaire items from the renewed ICT Skill Test and their categorisations for usage domains. Item Economic use Cultural use Social use Personal use Maintaining social relationships x Commercial use x Following current events x Communication x Game playing x x Information seeking x Digital entertainment x Creating digital content x Sharing content online x x Learning x 104 Original Publications 105 106 Meri-Tuulia Kaarakainen, Osmo Kivinen, Teija Vainio Performance-based Testing for ICT Skills Assessing: A Case Study of Students and Teachers’ ICT Skills in Finnish Schools June 2018 107 108 109 110 111 112 113 114 115 116 117 118 119 120 Meri-Tuulia Kaarakainen, Loretta Saikkonen, Juho Savela Information Skills of Finnish Basic and Secondary Education Students: The Role of Age, Gender, Education Level, Self-efficacy and Technology Usage April 2019 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 Meri-Tuulia Kaarakainen, Antero Kivinen, Suvi-Sadetta Kaarakainen Differences Between the Genders in ICT Skills for Finnish Upper Comprehensive School Students: Does Gender Matter? October 2017 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 Meri-Tuulia Kaarakainen, Suvi-Sadetta Kaarakainen, Antero Kivinen Seeking Adequate Competencies for the Future: The Digital Skills of Finnish Upper Secondary School Students September 2018 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 Meri-Tuulia Kaarakainen ICT Intentions and Digital Abilities of Future Labor Market Entrants in Finland June 2019 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197