Abstract
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 pub-lic 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 di-vides’ (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
or no access—to more qualitative and contextualised notions, such as digital inclusion or exclusion. Helsper (2008), for instance, asks for research that ex-amines 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 qualita-tive 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.
Background
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 repro-ducer 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 au-thors 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 disadvan-taged 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 coun-tries, home Internet access increased by an average of 54 percent among disadvan-taged students between 2000 and 2009. Meanwhile, there have been considerable investments in ICT resources in all 25 OECD countries. This development could
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 appar-ent in softer, more inclusive measuremappar-ents of ICT capabilities and skills. Most of these latter studies employed an empirically broad approach, establishing a gen-eralised view of youths’ access to and use of ICTs during childhood and adoles-cence. 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.
Aim
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 follow-ing 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?
Search Strategy
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
1 In Web of Science, limitations to the categories Education Educational Research, Commu-nication and Sociology existed.
ERICSocINDEX
The main criteria for the searches was peer-reviewed academic journal articles published since 2006 that studied digital divide issues in primary or upper second-ary 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 stu-dent* OR pupil*). This search resulted in a total of 1678 unique articles (Figure 1).
Fig. 1: The inclusion and exclusion process
2 Academic Search Elite; Communication & Mass Media Complete; Library, Information Science & Technology Abstract; ERIC and SocINDEX.
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. Thir-ty 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 dif-ferent groups of informants, such as students, teachers and/or parents.
Some of the studies employed more of an evaluative approach than a re-search 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; regard-less, 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,
3 http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=1913.
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).
Table 1: Predominant scholars in the reviewed articles
Author Total
references Reviewed articles
referring to the author (n) Unique publications in articles by author (n)
Hargittai, Ezter 31 12 18
Warschauer, Mark 20 11 8
Van Dijk, Jan 17 9 7
Livingstone, Sonia 13 10 7
Prensky, Marc 12 6 3
Selwyn, Neil 11 9 6
boyd, danah 11 5 8
Lenhart, Amanda 9 8 6
Knobel, Michele 9 8 4
Smith, Aaron 9 5 5
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 cita-tions (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
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.
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 resi-dential 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 ad-vantaged or disadad-vantaged 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
Advantaged
Moderate-high SES (4; 8) High educational achievers (4) Disadvantaged
High poverty rates or low SES, including low socio-economic suburbs (3; 7;
13; 22; 23)
6 Belgium, Czech Republic, Denmark, Finland, Germany, Hungary, Italy, Japan, Korea, New Zealand, Poland, Portugal, Slovak Republic, Sweden, Switzerland and Uruguay.
Ethnic minorities (7; 12) Low educational achievers (12)
Comparative studies with predefined groups according to Socio-economic status (29)
Residential area (16; 29) School forms7 (5; 17; 19) School status8 (6)
Research outcomes
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 qualita-tive studies highlight the influence of SES on the use of ICTs. Such digital in-equalities related to SES are found in twenty different countries (Table 2).
Table 2: Digital inequality related to Socioeconomic Status10 Statistically
proven N Objective
Ahn (2012) Yes 701 Students’ use of social network sites Calvani et al. Yes 1.056 Students’ digital competence Chapman et al. Yes 6.230 Teachers’ ICT skills and use Gui & Argentin Yes 980 Students’ digital skills Hohlfeld et al. Yes 2.345 Students’ use of ICTs
Lebens et al. Yes 60 Students’ computer attitudes
Liao & Chang* Yes 1.200 Students’ information literacy Mertens & D’Haenens Yes 1.005 Students’ ICT use and ownership
North et al. No 25 Students’ use of ICTs
Parycek et al. Yes 379 Students’ internet use
Reinhart et al.* Yes 94 Teachers’ ICT use for instruction
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.
Statistically
proven N Objective
Robinson No >300 Students’ information channel preferences Tondeur et al. Yes 1.241 Students’ ICT use, competence and
attitudes
Wood & Howley Yes 514 Teachers’ view regarding students computer use
Zhao Yes 432 Students’ use of different social
networking services
Zhong Yes 87.562 Students’ digital skills
Digital inequality could also be related to gender, as ten studies showed statisti-cally 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 bet-ter 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).
Table 3: Digital inequality related to gender Statistically
proved N Objective
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
Statistically
proved N Objective
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
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 with-in the same country (1; 2), as well as between countries (26).
Table 4: Digital inequality related to ethnicity Statistically
proved N Objective
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 reconsid-ered. 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
11 Pre- and post-tests after using electronic (threaded) discussion during the school year.
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 opportu-nity 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 as-signments—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 pop-ulation. 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 socioeco-nomic 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
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