Concepts, Assessments, Subversions
Edited By Matteo Stocchetti
Digital Inequality in Primary and Secondary Education: Findings from a Systematic Literature Review
During the last couple of decades there has been a global interest in unequal access to and use of information and communication technologies (ICTs). Without a clear state of its actual origin, the concept digital divide started to appear frequently in the public debate in the mid-1990s in efforts to describe and analyse disparities in ICT access. Since the mid-2000s increasing numbers of scholars have changed their research interest from a dichotomous view of digital divides – you either have or have not access – to more qualitative and contextualized notions such as digital inclusion or exclusion. This systematic literature review offers an overview of this latter, more qualitative and contextualized turn of research. It does so by looking into a specific area of research, namely research concerning digital inclusion and exclusion in the context of primary and secondary education. The literature review maps what studies have been conducted and what empirical evidence is currently available regarding digital inequality among children in primary and secondary school contexts. The review makes obvious that digital inequalities exist in several developed countries among pupils in primary and secondary education. Inequalities can most often be related to socioeconomic status, gender and ethnicity. As a conclusion, this means that any ambition to increase digital equality among young people has to struggle against well-known societal structures.
During the last couple of decades there has been global interest in unequal access to and use of information and communication technologies (ICTs). Although its origin is unclear, the concept of a digital divide began to frequently appear in public debate in the mid-1990s as part of the efforts to describe and analyse disparities in ICT access. Within the research field, the concept later became the very centre of ICT debates—these debates analysed how divides were delineated within social and cultural structures such as class, gender, ethnicity, and level of education (cf. Norris, 2001; Servon, 2002; Warschauer, 2003).
The early debates were often influenced by diffusion theory (Rogers, 1995), which pays specific interest to people’s varying willingness to adopt innovations. As a consequence, initial digital divide research argued ‘the acquisition of and access to computers and internet equipment is a fundamental for overcoming divides’ (Tsatsou, 2011, p. 321) and focused mainly on access issues—i.e., who does and who does not have access to ICTs?
Since the mid-2000s, however, an increasing number of scholars have shifted their research interest from a dichotomous view of digital divides—i.e., access ← 41 | 42 → or no access—to more qualitative and contextualised notions, such as digital inclusion or exclusion. Helsper (2008), for instance, asks for research that examines not only access to ICTs but also how ‘motivation, knowledge and skills’ (p. 23) are variously distributed among people.
This systematic literature review offers an overview of this latter, more qualitative and contextualised shift. It does so by investigating a specific area of research: research concerning digital inclusion and exclusion in the context of primary and secondary education.
Digital inequality could be understood ‘as a hierarchy of access to various forms of technology in various contexts, resulting in differing levels of engagement and consequences’ (Selwyn, 2004: 351). This statement highlights the complexity as well as the need for contextualization to be able to evaluate and understand the phenomena. Digital inequality could be viewed as both an expression and a reproducer of social inequality (Mori, 2010).
The interest in digital divide research has not only changed its focus since 2005; it has also increased. Wang, McLee and Kuo (2011) analysed references from 852 documents published between 2000 and 2009, which they found using the key word “Digital divide.” They found that the number of cited documents and authors increased between the periods 2000–2004 and 2005–2009. During both periods, the same studies and authors dominate the reference lists. The ten most cited authors from the later period were (in order of citation frequency) Eszter Hargittai, Pippa Norris, Mark Warschauer, Manuel Castells, Susannah Fox, Jan A. G. M. van Dijk, Paul DiMaggio, Neil Selwyn, Sonia Livingstone and Amanda Lenhart. Castells, Norris and Warschauer were also among the ten most cited authors in the earlier period. Wang et al. found that medical journals, followed by information society and communication journals, were the most sited journals. Educational journals, however, are conspicuously absent from those most cited, although some of the most cited authors show at least some interest in educational issues.
Access to ICTs for socio-economically advantaged children versus disadvantaged children differs by only a few percentage points in Western countries, such as the Netherlands, Norway, Finland, Denmark, Iceland, Sweden, Switzerland and the United Kingdom (OECD, 2011). According to the OECD, across its countries, home Internet access increased by an average of 54 percent among disadvantaged students between 2000 and 2009. Meanwhile, there have been considerable investments in ICT resources in all 25 OECD countries. This development could ← 42 | 43 → be seen in the light of Yu’s (2006) second category of studies, which focuses on digital divides as an economic concern and perceives governmental interference as a means to close the divides.
Nevertheless, research still shows divides in the Western world. However, these divides are less apparent in regard to access to ICTs and instead are more apparent in softer, more inclusive measurements of ICT capabilities and skills. Most of these latter studies employed an empirically broad approach, establishing a generalised view of youths’ access to and use of ICTs during childhood and adolescence. Much less research, however, has analysed digital inequality within specific contexts of youths’ everyday lives—in school, at home, during leisure time, etc.
Against this backdrop, this article begins to compensate for this shortcoming as a collection and overview of existing research concerning digital inequality within one specifically vital part of young people’s everyday lives: school.
The aim of this systematic literature review is to determine what studies have been conducted and what empirical evidence is available on the phenomenon of digital inequality among children in primary and secondary school contexts. The following questions will be answered by this review:
- What is the nature of the evidence?
- Which theoretical foundations and scholars are predominant?
- In which countries are the studies situated?
- In which specific contexts are the studies set?
- What are the research outcomes?
- What similarities or differences could be found in the outcomes?
The data was obtained from the following databases during May and June 2012:
- Academic Search Elite
- Communication & Mass Media Complete
- Library, Information Science & Technology Abstract
- Science Direct
- Web of Science1 ← 43 | 44 →
The main criteria for the searches was peer-reviewed academic journal articles published since 2006 that studied digital divide issues in primary or upper secondary schools, written in English or a Scandinavian language. Grey literature, such as dissertations, conference proceedings, reports and other non-peer-reviewed research, were not included.
The research area includes concepts such as digital equality or inequality, digital inclusion or exclusion, digital divide or divides and digital stratification. Thus, the following search string was entered into ‘any field’ in EBSCO hosted databases2, ‘Title’ or ‘Topic’ in Web of Science and ‘Abstract, Title, Keywords’ in Science Direct: ((digital divide*) OR (digital inequ*) OR (digital equ*) OR (digital inclu*) OR (digital exclu*) OR (digital stratification*)) AND (school* OR educ* OR student* OR pupil*). This search resulted in a total of 1678 unique articles (Figure 1).
Criteria for Selection
The first step was to screen all 1678 titles and abstracts to exclude articles that clearly fell outside the research focus. Except for the demand for empirical data, no other limitations were put on the research design or data collection. The data were collected and coded for inclusion or exclusion by EPPI-reviewer3. Thirty articles met the inclusion criteria and were eligible for review (Appendix I, Table I).
Nature of the evidence
The majority of the studies were quantitative or combinations of quantitative and qualitative, while only five studies were solely qualitative. Consequently, most of the data were collected with questionnaires (see Appendix). Most of the studies (n=24) used only students as informants, and four studies (3; 6; 21; 28) used only teachers as informants. Two of the studies (10; 13) collected data from several different groups of informants, such as students, teachers and/or parents.
Some of the studies employed more of an evaluative approach than a research approach. There were evaluations of hardware implementations (7; 10) and software implementations (23). It must also be noted that Rosen, one author of study 23, is connected to the software company in question, according to the company’s website. In some studies, it was difficult to follow the entire research process, which resulted in uncertainty concerning the method (16) and year of data collection (9; 15; 17; 21; 27). Although these studies may lack in reliability, they were included, but are marked with an * any time conclusions are drawn from them.
Predominant theoretical foundations and scholars
To determine the predominant theoretical foundations and scholars, two different approaches were used. First, the full text of the articles were analysed, and second, a meta-analysis of the articles’ references, based on author(s) and title, was conducted. A total of 1163 references were analysed. The research field is multidisciplinary, which could be a reason for the lack of well-defined, predominant theories; regardless, references are mainly made to three different theoretical fields.
A majority (n=15) of the studies used the frameworks of different theories based on the relationship between socio-economic status (SES) and ICT access and use. Within the field of socio-economic theories, different capital theories are used, ← 45 | 46 → such as Bourdieu’s capital theory, bonding and bridging social capital theory4, and knowledge gap theory5.
The second most common theoretical foundation was studies including various takes on the concept literacy. Sometimes specified as digital literacy, information literacy, media literacy, computer literacy, or network literacy, this foundation was used in twelve studies. Gender theories were the third most common theoretical foundation; it was applied in seven studies (see Appendix II).
The most predominant scholar was Ezter Hargittai in terms of both number of references and unique publications (Table 1).
The predominance of some scholars could be explained by their clear focus on digital divide and by single articles that, despite their early publication dates, are considered key works in the research field (Appendix I, Table II). The articles refer to 537 unique scholarly journals, but the single most cited journal is Computers & Education, followed by New Media & Society, which had only half as many citations (Appendix I, Table III).
Most of the studies are conducted in one single country; the only exceptions are the studies by Tømte and Hatlevik (2011) and Zhong (2011), who compared two ← 46 | 47 → and sixteen6 countries, respectively. Both these studies also used data from the OECD’s Programme for International Student Assessment (PISA) studies. Other countries represented in this review are the following:
- United States (1; 2; 3; 6; 7; 12; 14; 21; 22; 27; 28; 29)
- Italy (5; 8; 11)
- Israel (4; 23)
- Germany (9; 15)
- Belgium (18; 25)
- Australia (13; 19)
- Spain (10)
- Austria (20)
- Taiwan (16)
- Korea (17)
- Sweden (24)
Specific contexts of the studies
Digital inclusion and exclusion is often related to demographic factors, such as ethnicity, gender, socio-economic status (SES), educational orientation and residential area. The studies addressed these factors using three different sample orientations: (a) the informants constitute a representative sample or convenient sample without predefined groups, (b) the informants constitute predefined advantaged or disadvantaged groups, and (c) the informants constitute predefined groups to make comparisons.
Representative samples or convenient samples without predefined groups
- Nationally or regionally representative samples (1; 11; 14; 15; 18; 20; 24; 25; 26; 28; 30)
- Convenient, not nationally representative groups (2; 9; 10; 21; 22; 27)
Predefined advantaged or disadvantaged groups
- Moderate-high SES (4; 8)
- High educational achievers (4)
- High poverty rates or low SES, including low socio-economic suburbs (3; 7; 13; 22; 23) ← 47 | 48 →
- Ethnic minorities (7; 12)
- Low educational achievers (12)
Comparative studies with predefined groups according to
Overall, socioeconomic status (SES) is a significant factor of the use of ICTs—the higher the SES, the more advanced and advantageous the use9. Fourteen studies show statistically proven existing digital inequality due to SES, and two qualitative studies highlight the influence of SES on the use of ICTs. Such digital inequalities related to SES are found in twenty different countries (Table 2).
Digital inequality could also be related to gender, as ten studies showed statistically proven gender differences in twenty countries (Table 3). Gender differences are identified in ways of use, competence, attitudes, preferences and self-efficacy. Computer ownership is higher among boys than girls (25); boys are also more frequent users than girls (24) and score higher in general ICT interest (9). They are also less interested in social network sites than girls (1, 2), but socialise by going to Internet cafés and playing games together (17; 24). Boys are also more self-confident (9), have a more positive computer attitude (25) and perform better than girls in theoretical ICT skills (11) as well as score higher on self-reported ICT-skills (30). In regard to the effect of ICT as a tool for enhancing learning, boys tend to evaluate the improvement more favourably than girls (27). Girls are as skilled as boys in routine activities online (11), but are less interested and skilled in the technical aspects (9; 11). They prefer standard applications (9) and use ICTs for communication and socialisation (9; 17; 24).
|Ahn (2011)||Yes||700||Students’ use of social network sites|
|Ahn (2012)||Yes||701||Students’ use of social network sites|
|Ertl & Helling*||Yes||90||Students’ gender differences in skills and attitudes|
|Gui & Argentin||Yes||980||Students’ digital skills|
|Lim & Meier*||Yes||673||Students’ ICT use|
|Parycek et al.||Yes||379||Students’ Internet use|
|Samuelsson||Yes||256||Students’ ICT use and skills|
|Tondeur et al.||Yes||1.241||Students’ ICT use, competence and attitudes|
|Tømte & Hatlevik||Yes||≈9400||Students’ gender differences in self-efficacy in ICT|
|Wolsey & Grisham*||Yes||67||Students’ perception of themselves as writers11|
|Zhong||Yes||87.562||Students’ digital skills ← 49 | 50 →|
Ethnicity—meaning, groups with a shared cultural heritage—is another divider for digital inequality. Three large quantitative studies found differences in use and self-efficacy in relation to ethnicity (Table 4). Ethnic differences were found within the same country (1; 2), as well as between countries (26).
|Ahn (2011)||Yes||700||Students use of social network sites|
|Ahn (2012)||Yes||701||Students use of social network sites|
|Tømte & Hatlevik||Yes||≈9400||Students differences in Self-efficacy in ICT|
However, several of the studies (e.g., 2; 11; 20; 25; 26) found that SES, gender, ethnicity and other factors interact, and stereotypical assumptions must be reconsidered. This ‘underpin[s] the existence of multi-facetted perspectives’ (26, p. 1422).
A multifaceted perspective
The interaction of several factors on digital divides is highlighted in different ways. There could be an ethnic dimension in gender differences, as Finnish boys report a higher level of self-efficacy than Finnish girls, but Norwegian boys report a higher level of self-efficacy than Norwegian girls in only one of two areas (26). Additionally, boys’ digital skills are more affected by parental education than those of girls (11).
Two U.S. studies (1; 12) claim to have found no digital divide due to SES. However, inequality due to ethnicity was found; more specifically, ‘Black students were more likely to participate in social network sites [SNS] than their White ← 50 | 51 → peers’ (1, p. 159). Furthermore it was concluded that off-line social divides predict the use of different SNS’s, such as Myspace and Facebook (2). Low SES Latino students were found to have the same access, confidence and use of ICTs as other American millennials, but their educational setting does not provide the opportunity to develop higher order information skills (12).
Two studies employed an approach that differs from the others. One study (10) was based on the assumption that socio-educational inequalities existed among the students and found that they could be reduced by the implementation of tablet PCs. Another study (4) found that students’ use of ICTs on school related assignments was strongly dependent on traditional school practices and their valuation of the assignments—less important assignments could be completed with the help of ICTs, while more important assignments were completed using books and lesson notes.
The school factor
As presented above, several studies are made with reference to predefined groups that relate to previous research concerning advantaged and/or disadvantaged living conditions. In some studies schools with different socioeconomic status, educational orientation, location and/or governmental interference only serve as a research population. The school context itself is not used as a dependent factor for data analyses in these studies. As a result, many studies lack in deeper information about the status of the school as well as in information about the use of ICTs in relation to other schools in the country. These studies often refer to the students’ individual socioeconomic status as the favoring or disfavoring factor for digital inequality.
However, almost one third of the studies (5, 6, 14, 20, 21, 28, 30) refer to characteristics of the schools as valid variables, or determinant factors, for digital inequality. Students from high schools preparing for academic studies have higher average scores on a digital competence test than students from technical institutes (5, 20). Students from schools with different educational orientation also differ in use of ICTs and software (20), something that also could be related to high and low SES schools (14). One of the studies (28) identified a specific school characteristic that was especially favorable for students’ digital experience and skills: suburban affluent schools. Schools in other locations, despite the amount of poor or minority students, often fared worse. A factor strongly related to the impact of the school is its teachers. Two studies (6, 21) highlight that students at low SES schools tend to meet teachers with lower ICT competence than students at high SES schools. The conclusion is that investments in technological equipment will make little difference if the teachers themselves lack competence and technology facilitators. ← 51 | 52 →
The last study using school characteristics as a variable (30) is based on the international PISA survey12. While the other studies mainly drew conclusions from a single country, or even a single region or school, this study included 16 different countries with public as well as private schools. Several hypotheses regarding factors influencing students’ self-reported digital skills were tested. According to the statistical testing there was no difference in digital skills between students from private or public schools, neither was the ICT penetration rate of the country positively related to students’ digital skills. On the contrary, ICT access in school was positively related to students’ digital skills, but in comparison with home ICT access it was weaker. In the 2006 data set there was a weak but significant negative relation between the ICT penetration rate at country level and school ICT access effect on the self-reported digital skills. Another hypothesis proposed that the effect of home ICT access on students self-reported digital skill would be “stronger for students studying at schools with scarce ICT access than for those studying at schools with sufficient ICT access” (p. 739). This was not supported in the data. In summary, this study shows “that the family works as a more powerful predictor of adolescents’ self-reported digital skills than schools do” (p. 744).
Discussion and conclusion
This review clearly shows that digital inequalities exist among pupils in primary and secondary education in several developed countries. Inequalities are most often related to socioeconomic status, gender and ethnicity. As a result, this indicates that any effort to increase digital equality among young people must struggle against well-established structural divides. The multifaceted patterns and interactions between different factors in this finding demonstrate the need for a more complex and sociologically orientated theoretical foundation in digital divide research. The use of sociological theory in digital divide research is also strongly supported by predominant scholars, such as Selwyn13.
Among all 1678 articles that addressed the digital divide, less than 2 percent met the selection criteria. While Wang et al. (2011) found that specific scholarly journals devoted to digital divide research have appeared and that the research has ‘gained the reputation as a legitimate academic field’ (p. 323), this review cannot offer a similar conclusion. Instead, it seems that articles on digital inequality ← 52 | 53 → in educational contexts are published in scholarly journals from various research fields, such as education, media and communication, sociology and human behaviour. This could be interpreted in at least two different ways: (1) as a reflection of the interdisciplinary character of the research area or (2) as an expression of little interest in the subject among education researchers. This may also explain the lack of in-depth studies on the school context’s impact on digital inequality. The educational setting is mainly a framing context, not a valid variable in itself.
As digital inequalities are strongly related to current technological and societal development, it is noteworthy that several studies have been published with no indication of the time of data collection. Furthermore, most of the reviewed articles were largely based on extensive quantitative studies. However, even if they focused on qualitative aspects of digital inequality, they still lack a deeper understanding of the phenomenon. This is one of the challenges for future research.
This is arguably an important challenge for researchers within the field: to sufficiently develop and refine the theory so that research can contribute to an understanding of the phenomenon of digital inequality in not only disadvantaged groups but also advantaged groups and, particularly, in the context of compulsory education, where actual teaching efforts can be made to compensate for the divide.
Ahn, J. (2011). Digital Divides and Social Network Sites: Which Students Participate in Social Media? Journal of Educational Computing Research, 45(2), 147–163.
Ahn, J. (2012). Teenagers and social network sites: Do off-line inequalities predict their online social networks? First Monday, 17(1), 1–1.
Banister, S., & Reinhart, R. V., (2011). TPCK for Impact: Classroom Teaching Practices That Promote Social Justice and Narrow the Digital Divide in an Urban Middle School. Computers in the Schools, 28(1), 5–26.
Ben-David Kolikant, Yifat. (2012). Using ICT for school purposes: Is there a student-school disconnect? Computers & Education, 59(3), 907–914.
Calvani, A., Fini A., Ranieri, M., & Picci, P. (2012). Are young generations in secondary school digitally competent? A study on Italian teenagers. Computers & Education, 58(2), 797–807.
Chapman, L., Masters J., & Pedulla, J. (2010). Do digital divisions still persist in schools? Access to technology and technical skills of teachers in high needs schools in the United States of America. Journal of Education for Teaching, 36(2), 239–249.
Cotten, Shelia R., Hale, Timothy M., Moroney, Michael Howell, O’Neal, LaToya, & Borch, Casey. (2011). Using affordable technology to decrease digital inequality. Information, Communication & Society, 14(4), 424–444.
Ertl, Bernhard, & Helling, Kathrin. (2011). Promoting Gender Equality in Digital Literacy. Journal of Educational Computing Research, 45(4), 477–503.
Ferrer, Ferran, Belvís, Esther, & Pàmies, Jordi. (2011). Tablet PCs, academic results and educational inequalities. Computers & Education, 56(1), 280–288.
Gui, Marco, & Argentin, Gianluca. (2011). Digital skills of internet natives: Different forms of digital literacy in a random sample of northern Italian high school students. New Media & Society, 13(6), 963–980.
Haras, Catherine. (2011). Information behaviors of Latinos attending high school in East Los Angeles. Library & Information Science Research, 33(1), 34–40.
Hattie, John A. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London: Routledge.
Helsper, Ellen. (2008). Digital inclusion: an analysis of social disadvantage and the information society. Department for Communities and Local Government, London, UK.
Henderson, Robyn, & Honan, Eileen. (2008). Digital literacies in two low socioeconomic classrooms: Snapshots of practice. English Teaching-Practice and Critique, 7(2), 85–98.
Hohlfeld, Tina N., Ritzhaupt, Albert D., Barron, Ann E., & Kemker, Kate. (2008). Examining the digital divide in K-12 public schools: Four-year trends for supporting ICT literacy in Florida. Computers & Education, 51(4), 1648–1663.
Lebens, Morena, Graff, Martin, & Mayer, Peter. (2009). Access, attitudes and the digital divide: children’s attitudes towards computers in a technology-rich environment. Educational Media International, 46(3), 255–266.
Liao, Chin-Hsien, & Chang, Hsueh-Sheng. (2010). Explore the influences to Taiwan students’ information literacy with the Urban-rural differences from the perspective of globalization. Procedia – Social and Behavioral Sciences, 2(2), 3866–3870.
Lim, Keol, & Meier, Ellen B. (2011). Different but similar: computer use patterns between young Korean males and females. Educational Technology Research & Development, 59(4), 575–592.
Mertens, Stefan, & D’Haenens, Leen. (2010). The digital divide among young people in Brussels: Social and cultural influences on ownership and use of digital technologies. Communications: The European Journal of Communication Research, 35(2), 187–207.
Mori, Cristina Kiomi. (2010). ‘Digital Inclusion’: Are We All Talking about the Same Thing? In Jacques Steyn, & Graeme Johanson (Eds), ICTs and Sustainable Solutions for the Digital Divide: Theory and Perspectives, 45–64. IGI Global.
Norris, Pippa. (2001). Digital divide?: civic engagement, information poverty, and the Internet worldwide. Cambridge: Cambridge University Press.
North, Sue, Snyder, Ilana, & Bulfin, Scott. (2008). DIGITAL TASTES: Social class and young people’s technology use. Information, Communication & Society, 11(7), 895–911.
OECD. (2011). PISA 2009 Results: Students on Line: Digital Technologies and Performance. (Vol. VI).
Parycek, Peter, Sachs, Michael, & Schossbock, Judith. (2011). Digital Divide among Youth: Socio-Cultural Factors and Implications. Interactive Technology and Smart Education, 8(3), 161–171.
Reinhart, Julie M., Thomas, Earl, & Toriskie, Jeanne M. (2011). K-12 Teachers: Technology Use and the Second Level Digital Divide. Journal of Instructional Psychology, 38(3), 181–193.
Robinson, Laura. (2011). Information-channel preferences and information-opportunity structures. Information, Communication & Society, 14(4), 472–494.
Rosen, Yigal, & Manny-Ikan, Edith. (2011). The Social Promise of the Time to Know Program. Journal of Interactive Online Learning, 10(3), 150–161.
Samuelsson, Ulli. (2010). ICT use among 13-year-old Swedish children. Learning, Media and Technology, 35(1), 15–30.
Selwyn, Neil. (2004). Reconsidering political and popular understandings of the digital divide. New Media & Society, 6(3), 341–62.
Servon, Lisa J. (2002). Bridging the digital divide: technology, community, and public policy. Malden, MA: Blackwell Pub.
Tondeur, Jo, Sinnaeve, Ilse, van Houtte, Mieke, & van Braak, Johan. (2011). ICT as cultural capital: The relationship between socioeconomic status and the computer-use profile of young people. New Media & Society, 13(1), 151–168.
Tsatsou, Panayiota. (2011). Digital divides revisited: what is new about divides and their research? Media, Culture & Society, 33(2), 317–331.
Tømte, Cathrine, & Hatlevik, Ove E. (2011). Gender-differences in Self-efficacy ICT related to various ICT-user profiles in Finland and Norway. How do self-efficacy, gender and ICT-user profiles relate to findings from PISA 2006. Computers & Education, 57(1), 1416–1424.
Wang, Cheng-Hua, McLee, Yender, & Kuo, Jen-Hwa. (2011). Ten Years of Digital Divide Studies: Themes, concepts and realtionships. Paper presented at the 2011 International Conference on Social Science and Humanity, Singapore.
Warschauer, Mark. (2003). Technology and social inclusion: rethinking the digital divide. Cambridge, Mass.: MIT University Press.
Wolsey, Thomas DeVere, & Grisham, Dana L. (2007). Adolescents and the New Literacies: Writing Engagement. Action in Teacher Education, 29(2), 29–38.
Wood, Lawrence, & Howley, Aimee. (2012). Dividing at an early age: the hidden digital divide in Ohio elementary schools. Learning, Media & Technology, 37(1), 20–39.
Yu, Liangzhi. (2006). Understanding information inequality: Making sense of the literature of the information and digital divides. Journal of Librarianship and Information Science, 38(4), 229–252.
Zhao, Sherry Y. (2009). Teen Adoption of MySpace and IM: Inner-City versus Suburban Differences. Cyberpsychology & Behavior, 12(1), 55–58.
Zhong, Zhi-Jin. (2011). From access to usage: The divide of self-reported digital skills among adolescents. Computers & Education, 56(3), 736–746.
Research designs and methodology
|Data collection||Analyse||Year of Data collection|
|1||Ahn (2011)||SES14, Gender||X||X||700||2007|
|2||Ahn (2012)||SES, Gender, Literacy, social capital||X||X||701||2009|
|3||Banister & Reinhart||SES, Social justice, PCK15, TCPK||X||X||95316||2008|
|5||Calvani et al.||Literacy, Cognition and Socio-cultural||X||X||1.056||2009/2010|
|6||Chapman et al.||SES||X||X||6.230||2006–2008|
|7||Cotten et al.||SES, Literacy, Technology attitude||X||X||1.202||2008/2009|
|8||Delfino||Literacy||X||X||X||X||21||2008/2009 ← 59 | 60 →|
|9||Ertl & Helling||Literacy, Gender||X||X||X||X||90||> 2005|
|10||Ferrer et al.||SES||X||X||Debate forums||X||X||11.14317||2008/2009|
|11||Gui & Argentin||X||X||980||2007|
|13||Henderson & Honan||SES, Literacy||X||X||X||≈50+2||2007|
|14||Hohlfeld et al.||SES, Literacy, Diffusion of innovation||X||X||2.345||2003–2007|
|15||Lebens et al.||SES, Computer attitude||X||X||60||NA|
|16||Liao & Chang||Literacy, Globalization||C18||C||1.200||2005 ← 60 | 61 →|
|17||Lim & Meier||Gender, Socialization||X||X||X||X||673||NA|
|18||Mertens & D’Haenens||Capital, Deprivation, Differentiation||X||X||1.005||2007|
|19||North et al.||Capital||X||Media diaries, photographs||X||25||2006|
|20||Parycek et al.||Literacy||X||X||379||2009/2010|
|21||Reinhart et al.||Factors influencing SLDD19||X||X||94||NA|
|22||Robinson||Motivation, Information, Capital||X||X||>300||2006–2011|
|23||Rosen & Manny-Ikan||SES, Motivaton, Social constructivism||X||X||49+42||2007 ← 61 | 62 →|
|25||Tondeur et al.||SES, Capital||X||X||1.241||2007/2008|
|26||Tømte & Hatlevik||Gender, Cognition, Self-efficacy||X||X||≈9.400||2006|
|27||Wolsey & Grisham||Literacy, Gender||X||X||X||Analysis of artefacts||X||X||67||NA|
|28||Wood & Howley||SES, Motivation20||X||X||514||2009|
|30||Zhong||SES, Gender, Literacy, Information, Diffusion Theory||X||X||87.56221||2003+2006 ← 62 | 63 →|
1. In Web of Science, limitations to the categories Education Educational Research, Communication and Sociology existed.
2. Academic Search Elite; Communication & Mass Media Complete; Library, Information Science & Technology Abstract; ERIC and SocINDEX.
4. Williams (2006). “On and off the Net: Scales for social capital in an online era”. Journal of Computer–Mediated Communication, volume 11, number 2, pp. 593–628.
5. Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. Public Opinion Quarterly, 34, 159–170.
6. Belgium, Czech Republic, Denmark, Finland, Germany, Hungary, Italy, Japan, Korea, New Zealand, Poland, Portugal, Slovak Republic, Sweden, Switzerland and Uruguay.
7. Different groups, according to educational orientation.
8. High or need status.
9. Use that could lead to advantages in education and society.
10. Based on residential district, school status or individual status.
11. Pre- and post-tests after using electronic (threaded) discussion during the school year.
12. 2003 and 2006.
13. E.g. Selwyn, N. (2012). ”Making sense of young people, education and digital technology: the role of sociological theory”. Oxford Review of Education, 38, 1.
14. Socioeconomic status.
15. PCK stands for Pedagogical Content Knowledge and TCPK stands for Technological Pedagogical Content Knowledge.
16. 82 classroom observations, 871 Quick stop protocols.
17. 124 head teachers, 714 teaching staff, 5504 pupils and 4801 families representing 131 schools.
18. Conclusion drawn from the text, but not clearly stated.
19. Second Level Digital Divide.
20. Not clearly described.
21. PISA 2006.