An emerging digital divide in urban school children's information literacy: Challenging equity in the Norwegian school system
First Monday

An emerging digital divide in urban school children's information literacy: Challenging equity in the Norwegian school system
by Ove Edvard Hatlevik and Greta Bjork Gudmundsdottir

The emergence of information and communication technology (ICT) has been influencing our society, including the educational sector. In this paper we explore students’ information literacy at the completion of lower secondary school in Norway. Our aim is to measure students’ information literacy at the end of Grade 10, and to identify factors explaining the variations observed. Factors relating to the digital divide, e.g., books at home, language spoken at home and academic aspirations are explored in this study. The sample consists of 3,727 students from 50 lower secondary schools located in a Norwegian city with relatively high immigration rate and various ethnicities. Through statistical multilevel analysis our findings indicate that the number of books at home, the language spoken at home and the students’ academic aspirations explain a very large proportion of the variation in information literacy between schools, and a considerable part of the variation between students–within–schools.


Introduction and contextual background
Theoretical framework/Perspectives
Digital divide related to family background, language integration and cultural capital
Digital divide in terms of academic aspirations
Hypothesis and research questions
Discussion of findings



Introduction and contextual background

In 2004 the Norwegian Ministry of Education and Research decided to emphasize the following five themes as key competencies in Norwegian schools: Reading, writing, mathematics, the ability to express oneself orally, and the ability to use information and communication technology (ICT). The national curriculum based on the Knowledge promotion reform of 2006 consists of competency goals at different levels in primary and secondary school, and is grounded in these five competencies (Egeberg, et al., 2012). The 2006 curriculum reform was among the first in the world to include students’ ability to use ICT as one of the five core competencies (Balanskat and Gertsch, 2010; Krumsvik, 2008; Søby, 2007) in students’ knowledge promotion across disciplines. Digital competence is thus not defined as a distinct subject, but as the ability to use ICT integrated into all subjects in compulsory education (Grades 1–10), as well as in upper secondary education. The knowledge promotion reform displays a comprehensive shift towards an emphasis on ICT competence in Norwegian education policy (Søby and Egeberg, 2010).

The aim of this paper is to explore students’ information literacy, and the factors that predict students’ ability to use ICT in school. We focus on factors that explain the variations in students’ information literacy. We claim that urban students’ with minority cultural backgrounds are more prone to digital divides than students from affluent groups.



Theoretical framework/Perspectives

Information literacy

Various definitions are used to define competence in relation to ICT use in education. The trend has been to move away from a focus on basic skills to a wider focus on competency, or information literacy. That means a focus on how the use is conducted, rather than on pure access or basic proficiencies. Information literacy does not only refer to the use of ICT in the processing of information, but it has a broad scope including the use of and attitudes towards information and sources of information. As Loveless and Ellis [1] state “the term ICT is also accompanied by a set of conceptual understandings that relate to a notion of capability, literacy or ‘how to deal’ with information using technology”. According to the Association of College and Research Libraries (1989), a division of the American Library Association (ALA), information literacy is a key competence in the knowledge society. It refers to the ability to identify the need for information, to locate, critically evaluate, and effectively use information. These capabilities are becoming increasingly important, because it has been an explosion of information sources through the development of the Internet and Web 2.0. This gives students and teachers an almost unlimited access to information, but it also creates challenges when it comes to identifying and assessing what information is valid and relevant to the subject, school, work and everyday life. Eshet–Alkalai and Amichai–Hamburger (2004) define information literacy and focus on the quality and validity of information. They claim that information literacy is a required competence for all learners. The core of information literacy is that the use of Internet at school gives learners the opportunity “to identify important questions, locate information, critically evaluate the usefulness of that information, synthesize information to answer those questions, and then communicate the answers to others” [2]. When using ICT at school, learners must recognise what they do not know while identifying and locating appropriate information sources. Moreover, learners need to be capable of examining the quality and value of the information and sources they find.

We view the institutionalised teaching and learning practises in school as society’s formal transmission of relevant knowledge and competence to future generations, information literacy being part of that. However we acknowledge that learning is increasingly viewed as lifelong practise (Dinevski and Kokol, 2004; Levin, 2003), belonging to different contexts and institutions beyond school. In current Organisation for Economic Co–operation and Development (OECD) usage, lifelong learning encompasses all learning activity undertaken throughout life and does not only refer to recurrent or adult education (OECD, 2004).

In this article we will focus on students in their final year of compulsory schooling (Grade 10) and their information literacy practises. Consequently we will use a scholastic definition of information literacy in alignment with how the concept is described in policy documents in Norway.

In the so called Programme for Digital Competence, 2004–2008, the Ministry of Education and Research in Norway uses the following definition: “Digital competence is what bridges the gap between skills such as reading, writing and mathematics, and the skills required for the use of new digital tools and media in a creative and critical manner” [3]. The Norwegian Directorate for Education and Training (2006; 2011) claims that the use of digital media in the different subjects of the curriculum will contribute to variations in the use of learning strategies and venues. Currently, the ability to use ICT is operationalized through four themes: 1) to acquire and process digital information; 2) to produce and manipulate new information; 3) digital communication; and, 4) e–safety and digital judgement. Further, the theme of acquiring and processing digital information is concerned with how learners gather information, conduct search, how they evaluate sources and how they integrate and use knowledge from different sources. However, the content of this theme depends on, and is adapted to, the grade level of the students. There are, for example, higher expectations for students in Grade 10 compared with those in Grade 8. For the purposes of this paper we identify the above–mentioned dimensions as central to the competency aims for the lower secondary school. Recognising that learners have different points of departure in relation to these skills, the concept of the digital divide is used to explain their variations in access, use and skills.

Digital divide

The differentiated access and use of ICT between groups, countries or continents, has been identified as the digital divide. The digital divide can also be viewed from various other perspectives (Gudmundsdottir, 2011). There are competing definitions and understandings of the term, but a common way to explore the digital divide is to view access to ICT and connectivity capability, and to connect this to socio–economic status, class, education level, age or geographical location. Studies from the U.S. and Europe point out that socio–economic status, or class, plays an important role in computer access (Compaine, 2001; European Commission, 2002; Katz and Rice, 2002). Typically, these studies show that those with the least access are those who are less educated, live in rural areas, groups with minority background, or are senior citizens. When exploring the digital divide within the context of a country, as opposed to a global context it can thus be viewed from different perspectives. A gender perspective focuses on how girls and boys access and use ICT differently (Banerjee, et al., 2005; Sutton, 1991), location or geographical perspectives focus on the rural — urban divide and ethnicity (Agarwal, et al., 2009) while minority perspectives focus on the divide between disadvantaged and affluent groups (Cotten and Jelenewicz, 2006; Langa, et al., 2006). Zhang, et al. (2010) explore the digital divide in how households conduct their Internet information search. The digital divide is furthermore increasingly connected to existing social divides and class, including the socio–economic background of the users, such as household income, education level and home access to ICTs (Gudmundsdottir, 2011; McLaren and Zappala, 2002; Warschauer, 2002, 2003, 2004).

In Norway there is both public and political acceptance for a uniform school system. The pedagogical backbone of this thinking is that all students in compulsory education, regardless of socio–economic background, should be integrated into the same school system, meaning that every student gets the same opportunity to receive primary and secondary education. However, being a society where students come from various backgrounds, there are implementation challenges. In the municipality of Oslo for example, 40 percent of the students in primary and lower secondary schools have a minority language background which influences the equity aspects of such a policy (Oslo kommune. Utdanningsetaten, 2011). In certain areas, the majority of the students have a minority background. Taking this into consideration, we want to explore selected features in students’ backgrounds and how these influence the students’ achievements in information literacy.



Digital divide related to family background, language integration and cultural capital

Family background

Analysis of results from national tests in Norway indicate a significant, positive correlation between students’ grades and students’ family backgrounds (Norwegian Ministry of Education and Research, 2004a). Analysis of the PISA 2009 data shows that students’ socio–economic backgrounds have an impact on their performance in reading, mathematics and science. Furthermore, several international studies of ICT use at school also show how important it is to identify and understand the background and home environment of the students (Centre for Educational Research and Innovation, 2010; Pedró, 2007). In the data presented in this paper, books at home (cultural goods), home language (minority or dominant language), parents’ birthplace (ethnic background), education level and occupation, are used in understanding the students’ family backgrounds.



Digital divide in terms of academic aspirations

The Norwegian school system is based on 10 years of compulsory education. The curriculum is based on core subjects, but schools can also offer subjects adapted to local conditions and the needs of the students. However, these extra subjects involve only a small proportion of the weekly hours. After completing lower secondary school, all students have the right, and the opportunity, to start their upper secondary education. At the upper secondary level, there is a vocational track as well as a general track. The study program for general studies extends over three years, and prepares students for university admission, or higher education. The vocational educational track normally consists of two years of theory and one to two years of practice, or in–service training, in a special craft.

In total, there are twelve different study programs available for students choosing to enter upper secondary education. There are nine study programs for vocational education programmes, and three study programs for general studies. When applying for admission, the students rank three alternatives, and are guaranteed admission to one of the three alternatives.

Overall, it seems that there are more dropouts in the vocational tracks as compared with the general study tracks (Norwegian Directorate for Education and Training, 2011). Students’ prior levels of schooling, and academic aspirations, therefore predict their completion rate. However the quality of marks from lower secondary school has the strongest statistical effect on completion rate in upper secondary school (Kuczera, et al., 2008; Norwegian Directorate for Education and Training, 2007).



Hypothesis and research questions

The aim of this paper is to examine the information literacy of tenth graders. Research suggests that family background can explain variations in students’ information literacy (Ministerial Council for Education, 2010). This is also supported by the PISA 2009 reading test (OECD, 2011). Cultural capital and language integration at home are two indicators of family background. Students’ attitudes and motivation in school are also explored in connection with information literacy skills.

Our hypotheses are:

H1: Language integration predicts students’ information literacy.

H2: Cultural capital predicts students’ information literacy.

H3: Academic aspirations predict students’ information literacy.

The research question we address in this paper is whether students’ gender, family background (i.e., language at home, numbers of books at home and where their parents are born), and academic aspirations can explain variations in their information literacy?




This was a cross–sectional study consisting of 3,727 students from 50 lower secondary schools, grade 10. The total sample was 5,200 students, and the response rate was approximately 70 percent.

Information literacy was measured with a Web–based instrument. The municipality administrated the online assessment, and the upper secondary schools were responsible for carrying out the assessment at the beginning of the first semester when students started their upper secondary education.


Information literacy was measured with 27 items. A Cronbach’s alpha = 0.84 shows a rather good internal consistency for items. Four different types of tasks were used: multiple–choice, hot spot assignments, inserting text and drag–and–drop tasks. For clarification, we provided some examples of questions and types of tasks. First, students were asked about pollution, and they were given four different options. This was a multiple–choice task. Second, students were asked about the birth year of a known Norwegian person. They could use any resource on the Internet, and they had to write, or copy and paste, the birth year into a text recognition field. This is an example of an insert text task. Third, the students had to find information from a table, and they were asked to click on the right information. This is an example of a hot spot task.

Prior to the assessment the students were given examples of the types of task, however these examples were not a part of the final assessment. The tasks were given in writing, but mp3 files were embedded within the test tool in order to support poor readers.

Additionally, the students were asked about their gender, the number of books at home, language spoken at home and which study program they attended. The PISA study inspired the questions about the students’ backgrounds. The family background indicator in the questionnaire included two items: First, the students were asked about what language they speak at home. Three options were given: Norwegian language; Norwegian language and another language; or, mainly languages other than Norwegian. Second, the students were asked to estimate the number of books at home. The scale which was used was 1 = no books; 2 = 1–100; 3 = 101–500; and, 4 = 501 and more books. Third, the students answered a question about which study programme they were attending in upper secondary school. Their answer was coded as a vocational education program, or as a program for general studies (general studies = 0, vocational program = 1). Fourth, the students were asked about their gender (boy = 0, girl = 1).




The data were analysed by using the IBM SPSS version 18.

Descriptive statistics and scores

There were 1,997 boys (53.6 percent) and 1,730 girls (46.4 percent) in the sample. A total of 12.1 percent of the students had 10 books or less at home, whereas 15.3 percent had between 11 and 15 books. The majority, of 23.6 percent, had between 26 and 100 books, 16.7 percent had between 101 and 200 books, whereas 13.7 percent had 501 books or more (Table 1). The majority of the students, 58.9 percent, used only the Norwegian language at home, whereas 6.1 percent mainly used languages other than Norwegian at home. A total of 68.4 percent of the students were attending general study programmes in upper secondary school, whereas 31.6 percent were attending vocational training.


Table 1: Frequencies.
Books at home0–10 books12.1%
11–25 books15.3%
26–100 books23.6%
101–200 books16.7%
501 books and more13.7%
Language at homeOnly Norwegian language at home58.9%
Norwegian and other language at home34.9%
Mainly languages other than Norwegian at home6.1%
Academic aspirationsGeneral program68.4%
Vocational training31.6%



The correlation between information literacy and books at home is significant and positive (r = 0.43, p < 0.01). Regression analysis was used to measure the correlation between information literacy and the nominal or ordinal variables. Information literacy and gender were positively related (r = 0.09, p < 0.01), with girls performing better than boys. Further, information literacy was negatively related with language at home (r = -0.35, p < 0.01) and with the study program (r = -0.37, p < 0.01).

Multilevel analysis

A Mixed Models procedure of IBM SPSS was used to run multilevel analyses. Students represented level 1 and schools represented level 2. All students were nested within schools.

Data analysis and analysed models

The analysis consisted of 4 different models:

Model 0: This was the unconditional, baseline model.

Model 1: Language integration was put into the model.

Model 2: Cultural capital was added to Model 1.

Model 3: Academic aspirations and gender, a control variable, were added to Model 2.

All variables were on the individual level, and the models only consisted of fixed factors on level 1. There was no theoretical basis for including interactions between the factors.

Null model

In the null model the schools accounted for a proportion of variance in information literacy. A total of 11.50 percent of the variance was attributed to differences between schools, whereas other factors account for 88.50 percent of the variance. Multilevel analysis is recommended if the variance between schools exceeds five percent (Bickel, 2007).

Model 1

Language integration was added to Model 1. The estimated coefficient was significant (p < 0.05).

Model 2

Cultural capital was added in Model 2. All the estimated coefficients were significant (p < 0.01). To measure model fit and change in model fit between different models the ‘-2 Log likelihood’ was used. Model improvement by moving from Model 1 to Model 2 was significant.

Model 3

Academic aspirations were added to Model 2, together with gender as a control variable. Significant model improvement in the model fit index was achieved by moving from Model 2 to Model 3.

The results in Table 2 show that most estimated coefficients were significant for all the variables in Model 3. Two of the hypotheses are supported; H2, Cultural capital predicts students’ information literacy and H3, Academic aspirations predict students’ information literacy. The first hypothesis, language integration, is partly supported. The results revealed a significant difference in information literacy between students using only Norwegian at home, as compared with students not using Norwegian at home. However, there was no difference in information literacy between students speaking mainly languages other than Norwegian at home, when compared with students speaking both Norwegian and other languages at home.

When inserting language integration, cultural capital, gender and academic aspirations into the regression equation, the between–schools variance decreased by 96.38 percent (from 2.64 to 0.01), and the between–students–within–class variance decreased by 22.07 percent (from 20.33 to 15.85). This means that these three concepts together explain a large amount of the variation in information literacy at the school level, and a considerable amount of the variation in information literacy at the individual level.


Table 2: Results from multilevel analysis with information literacy as dependent variables. Language integration, cultural capital and academic aspirations are independent variables. Gender is a control variable.
Note: n.s. = non significant.
 Model 0
Model 1
Model 2
Model 3
Fixed effects 
Constant14.69 (0.24)12.87 (0.32)13.72 (0.29)16.96 (0.38)
Language integration (only Norwegian) 3.16 (0.31)1.70 (0.31)2.06 (0.28)
Language integration (Norwegian and another language) 0.14 (0.31)n.s.-0.07 (0.30)n.s.-0.25 (0.29)n.s.
Language integration (mainly languages other than Norwegian)    
Cultural capital  0.95 (0.05)0.71 (0.05)
Gender (boy = 0, girl = 1)   0.44 (0.13)
Academic aspirations (General studies = 0, Vocational training = 1)   -2.92 (0.15)
Covariance estimates 
Residual (student)20.63 (0.47)18.91 (0.44)17.54 (0.41)15.85 (0.37)
Intercept (school)2.64 (0.60)0.89 (0.25)0.23 (0.11)0.10 (0.07)
Model fit: -2 Log likelihood21 91621 60321 28520 892
Change in -2 Log likelihood 313318292




Discussion of findings

In this paper, we have tried to explore information literacy and to identify factors that can predict students’ information literacy. The results show that language at home, the number of books at home, and academic aspirations can explain the variations in students’ information literacy.

One of the goals of the Norwegian education system is to provide equal opportunities, and equal conditions in schools. The Ministry of Education and Research states that: “[e]qual opportunities to complete education are a prerequisite if we are to sustain and further develop the welfare state on the basis of the Norwegian model, with minor social differences between people”. In the strategic document Equal education in practice (Ministry of Education and Research, 2004b) the emphasis is on inclusive, equal and multicultural education. The focus is particularly on learners from ethnic and linguistic minorities, and their opportunities for learning and education. The strategy also states that the aim is not to provide all learners with the same education, but rather that all learners should be able to develop their abilities according to their own needs. Thus, equity is one of the cornerstones of the Norwegian schools model.

Despite the equity implicit in policy documents, our findings confirm that there is a clear divide in learners’ information literacy practises and skills. Measured by access to books at home (cultural capital), language spoken at home, and learners’ choice of educational programme in upper secondary school, there are clear differences in literacy levels. A small proportion of the variance in information literacy is related to the school (11.5 percent). Books at home, language spoken and choice of educational programme can explain almost all the variation evident between schools. However, it seems that the number of books at home reduces the influence of language at home. In addition, the information literacy divide does not appear to be primarily based on ethnicity, but rather on the broad–based socio–economic background of the learners. These findings are coherent with similar studies outside of Norway, for example from Australia (Ministerial Council for Education, 2010), Italy (Calvani, et al., 2012) and the writings of scholars such as Warschauer (Warschauer, 2002; 2003; 2004) and van Dijk and Hacker (2003).

We consider the implications of these findings as being twofold: Firstly, there is a need for continuous research on information literacy practises within the classroom context. There is a need for studies that provide knowledge on how best to support schools and learners that score low on information literacy indicators. There is also an on–going need to identify the challenges and possibilities for teachers and school leaders (school owners), and how they can best foster and promote digital practises and information literacy skills in schools in order to address the equity issue.

Further implications for classroom practise are the challenges of addressing the different skills levels of the students. Whereas educational policies aim at equity, it may be necessary to use different measures within the various schools in order to achieve greater equity. The schools with low information literacy scores need different ways of working to achieve good learning outcomes and information literacy practises than do those schools with higher information literacy scores. It is furthermore important to implement clear procedures, especially in schools where students have weak literacy skills.

One possibility to strengthen digital literacy practises and skills is to have a clear focus on the continuous professional development of teachers. Teachers in Europe are asking for increased in–service training opportunities in the field of ICT (OECD, 2008). The opportunity to establish and to conduct staff development through peer mentoring is one of the measures that could be of help. To establish peer–mentoring relationships between teachers and school leaders in different schools can stimulate discussion and awareness of useful actions. The advantages of such an approach are that teachers respect their colleagues, do not feel threatened by their guidance, and are not de–motivated by top–down instructions. Peer mentoring could make a significant difference in overcoming the barriers connected to information literacy practises in the classroom. However peer–mentoring is only one way of addressing the digital divide in the implementation and use of ICT in the classroom. The Norwegian Centre for ICT in Education, together with colleagues in the European Schoolnet (2012) is supporting the development of in–service courses, aimed at teachers and their pedagogical use of ICT in the classroom, as opposed to focusing only on the technology itself [4].

Seen from a comparative perspective (Frønes, et al., 2011; OECD, 2011) Norwegian students’ are well equipped when it comes to ICT in schools, and equity is a quality which is highly valued within the Norwegian school policy. Despite this, there are still differences and places where digital divides can be discovered. The empirical data in the present study applies to a large Norwegian municipality, and we suggest that it also provides indicators as to the situation in other towns of Norway. The findings indicate that the digital divide is thus not only an ethnic/minority issue, but also one of access to cultural capital, and the aspirations and opportunities of the students. Supported by Selwyn (2009) when he states that the students’ socio–economic backgrounds seem to explain which groups will prosper from the use of technology, we therefore argue that the digital divides are closely related to the social divides among the students. End of article


About the authors

Ove Edvard Hatlevik is working with R&D at the Norwegian Centre for ICT in Education which is directly under the Ministry of Education in Norway.
E–mail: o [dot] e [dot] hatlevik [at] iktsenteret [dot] no

Greta Björk Gudmundsdottir is working with R&D at the Norwegian Centre for ICT in Education which is directly under the Ministry of Education in Norway.
E–mail: gbg [at] iktsenteret [dot] no



1. Loveless and Ellis, 2005, p. 2.

2. Leu, et al., 2004, p. 1,572.

3. Ministry of Education and Research, 2004c, p. 5.




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Editorial history

Received 4 September 2012; revised 7 November 2012; accepted 15 February 2013.

“An emerging digital divide in urban school children’s digital literacy: Challenging equity in the Norwegian school system” av Ove Edvard Hatlevik and Greta Björk Gudmundsdottir er lisensiert under en Creative Commons Navngivelse–Ikkekommersiell–IngenBearbeidelse 3.0 Unported Lisens.

An emerging digital divide in urban school children’s information literacy: Challenging equity in the Norwegian school system
by Ove Edvard Hatlevik and Greta Björk Gudmundsdottir
First Monday, Volume 18, Number 4 - 1 April 2013

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