Attributes of digital natives as predictors of ... - Wiley Online Library

British Journal of Educational Technology doi:10.1111/bjet.12451

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Attributes of digital natives as predictors of information literacy in higher education Andrej Sorgo, Tomaz Bartol, Danica Dolnicˇar and Bojana Boh Podgornik Andrej Sorgo is an Associate Professor of Biology Didactics at the Faculty of Natural Sciences and Mathematics, University of Maribor, Slovenia and a researcher at the Faculty of Electrical Engineering and Computer Sciences, University of Maribor, Slovenia. His research interest includes benefit of ICT in education, acceptability of technologies and environmental issues. Tomaz Bartol is an Associate Professor of Information Science at the Biotechnical Faculty, University of Ljubljana, Slovenia. His research interest includes information science, information literacy, bibliometric, science mapping, research evaluation, scientific databases and terminology. Danica Dolnicˇar is an Assistant for Scientific and Technical Informatics at the Faculty of Natural Sciences and Engineering, University of Ljubljana, Slovenia. Her research interest includes information sciences, multimedia systems, e-learning, scientific databases, information literacy, digital literacy, statistical methods. Bojana Boh Podgornik is a Full Professor for Scientific and Technical Informatics at the Faculty of Natural Sciences and Engineering, University of Ljubljana, Ljubljana, Slovenia. Her research interest includes scientific databases and information systems, bibliometric methods, data structuring, patent informatics, information literacy, education, e-learning, natural products chemistry, and microencapsulation. Address for correspondence: Dr Andrej Sorgo, Faculty of Natural Sciences and Mathematics, University of Maribor, Koroska 160, Maribor, Slovenia and Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, Maribor, Slovenia. Email: [email protected]; [email protected]

Abstract Digital natives are assumed to possess knowledge and skills that allow them to handle information and communication technologies (ICT) tools in a “natural” way. Accordingly, this calls for the application of different teaching/learning strategies in education. The purpose of the study was to test the predictive strength of some attributes of digital nativeness (ICT ownership, ICT experiences, internet confidence and number of ICT-rich university courses) on the information literacy (IL) of 299 Slovenian university students. Correlation and regression analysis based on survey data revealed that the attributes of digital natives are poor predictors of IL. The principal findings are: ICT experiences expressed as the sum of the use of different applications do not necessarily contribute to IL; some applications have a positive and some a negative effect; personal ownership of smartphones, portable computers and desktop computers has no direct effect on IL, while ownership of a tablet computer is actually a negative predictor; personal ownership of ICT devices has an impact on ICT experiences and Internet confidence, and, therefore, an indirect impact on IL; and ICT-rich university courses (if not designed to cultivate IL) have only a marginal impact on IL, although they may have some impact on ICT experiences and Internet confidence. The overall conclusion is that digital natives are not necessarily information literate, and that IL should be promoted with hands-on and minds-on courses based on IL standards.

Introduction Background Information and Communication Technologies (ICT) are being increasingly integrated into almost every aspect of human life as both visible and invisible technology (Carr, 2003; Mishra & C 2016 British Educational Research Association V

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Practitioner Notes What is already known about this topic • •

•

The term digital natives tags a generation born during or after the introduction of ICT into daily routines, thus distinguishing them from digital immigrants. Digital natives are supposed to possess knowledge and skills that allow them to handle ICT tools in a “natural” way. This supposedly calls for the application of different teaching-learning strategies. Terms such as digital, media and information literacy are used to describe the recent and future knowledge and skills required to allow an individual to navigate media- and information-rich environments.

What this paper adds • •

Attributes of digital natives are poor predictors of information literacy. Ownership of digital devices and frequency of its use do not directly influence information literacy.

Implications for practice • •

IL standards must be consciously and deliberately incorporated into the educational process. ICT-rich university courses (if not designed to cultivate information literacy) have only a marginal impact on IL.

Koehler, 2006). Statistics provided by the International Telecommunication Union (ITU, 2015) indicate that seven billion mobile devices will be owned by the end of this year. The estimated number of Internet users in the world is 3.2 billion, which is nearly half of the human population. On the background of these figures, the term digital natives tags a generation born during or after the introduction of ICT into daily routines, thus distinguishing them from digital immigrants (Prensky, 2001a,b; Wang, Myers, & Sundaram, 2013). Digital natives are assumed to possess knowledge and skills that allow them to handle ICT tools in a “natural” way, which calls for the application of different teaching-learning strategies (Bennett, Maton, & Kervin, 2008; Kivunja, 2014; Prensky, 2001a, b; OECD, 2008), a claim challenged by many, with some critics even suggesting that the concept of digital natives is a myth (Jones & Czerniewicz, 2010; Magrino & Sorrell, 2014; Margaryan, Littlejohn, & Vojt, 2011; Selwyn, 2009). Digital natives are believed to possess the skills required to retrieve, select and analyse available data (documents), and to behave ethically and securely in cyberspace (eg, Cabra-Torres & Marciales-Vivas, 2009; JangJaccard & Nepal, 2014; Selwyn, 2009). Their digital skills are, however, overestimated by instructors (Magrino & Sorrell, 2014). For some time now, researchers have detected that there is little correlation between proficiency in academic research and the computer skills of students (Jenson, 2004), and that students have an unrealistic perception of their competencies in this area (Messineo & DeOllos, 2005). Terms such as digital, media and information literacy are used to describe the current and future knowledge and skills required to allow an individual to navigate media- and information-rich environments. These terms have not yet been well defined and have been subject to substantial changes through the development of technologies over the years (Lee & So, 2014; Mackey & Jacobson, 2011). Digital natives’ skills largely overlap with information literacy skills and competencies. According to the Information Literacy Competency Standards for Higher Education (ALA, 2000), an information literate individual is able to (1) determine the extent of information C 2016 British Educational Research Association V

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needed, (2) access the required information effectively and efficiently, (3) evaluate information and its sources critically, and incorporate selected information into their knowledge base, (4) use information effectively to accomplish a specific purpose and (5) understand the economic, legal and social issues surrounding the use of information, and access and use information ethically and legally. For a review of various definitions and standards of information literacy, see Boh Podgornik, Dolnicˇar, Sorgo and Bartol (2015). An individual can learn in different ways, mainly through self-learning and learning scaffolded by various formal and informal educators. If digital nativeness exists, and information literacy is a part of it, the question then arises: Which part of knowledge and which skills are expected to be taught by informal sources (eg, family, peers), which by the formal education system (eg, schools, universities), and which are left to self-learning? The borders between these divisions in educational processes are blurred and are largely influenced by cultural and socioeconomic factors. Differences exist in the use of digital devices (computer or digital literacy) and information (information literacy), thus widening the gap between users and nonusers, a pattern that has been recognised by many as the digital divide. The closing of the digital divide due to the saturation and wider availability of ICT devices introduces a usage divide (Livingstone & Helsper, 2007; van Dijk & Hacker, 2003) as well as achievement gaps. As quoted in Vigdor, Ladd and Martinez (2014, p. 1103): “Evidence suggests that providing universal access to home computers and high-speed Internet access would broaden, rather than narrow, math and reading achievement gaps.” Digital nativeness and the development of information literacy cannot be recognised solely as something “natural” and self-evident, as they are influenced by a number of socioeconomic and societal factors, as well as by personal preferences and the motivation of learners. For example, it is evident that many children use computers or cellular smartphones even before they can read, but that others from the same generation only have their first contact with these technologies when they are forced to use them, eg, in school, which may influence their future competencies and skills (Bavelier, Green, & Dye, 2010; Li & Atkins, 2004). Whether a person works as a professional or researcher, one such competency is a suitable level of information literacy (Binkley et al., 2012). Information literacy (IL) is a prerequisite for the fulfilment of the demands of lifelong learning in the future workplace, as well as for the realisation of personal choices. As such, information literacy can be regarded as both work-specific and generic. The latter allows public participation, informed decision making and the nourishing of personal interests, while also helping in professional activities beyond the primary field of study. Purpose of the study The authors of the present study are university educators who wish to provide students with an opportunity to fully develop their information competencies as a part of 21st century skills (Binkley et al., 2012). The objective is thus to identify and outline the chief factors that affect the information literacy of university students who are believed to be digital natives. The study focuses particularly on aspects that can be influenced by our actions at the institutions of our affiliation, but the experiences thus gained could be extended to a wider audience at related schools. A tentative assumption is that the information literacy of students is affected by ICT experience, the possession of ICT devices, the quantity of ICT-supported university courses and personal confidence in various aspects of Internet use. Sociometric and demographic data were excluded from the predictions, even in the knowledge that they can contribute to differences in various aspects of ICT proficiency (eg, Bimber, 2000; Demirbilek, 2014; Mahmood, 2013; Haight, Quan-Haase, & Corbett, 2014). From the large array of personality factors affecting information literacy (Stokes & Urquhart, 2011), only Internet confidence, as one attribute of digital natives, C 2016 British Educational Research Association V

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Figure 1: The framework of reasoning regarding the factors affecting the information literacy of university students

was used as a manifestation of self-efficacy (Chang et al., 2014; Eastin & LaRose, 2000; Papanastasiou & Angeli, 2008). The reasoning was similar to that in the case of sociometric and demographic factors: most personality traits can be recognised, but courses cannot be designed to take each trait into account, eg, adjusted to the students’ level of openness or extraversion. On the other hand, confidence can be improved by practice or course design. Figure 1 presents the framework of reasoning in our study. Research questions The research questions and working hypotheses were based on our tentative assumptions presented in Figure 1, and were as follows: • • • •

ICT experiences contribute to information literacy directly (but different ICT applications have different weight) and indirectly through Internet confidence. Personal possession of ICT devices affects ICT experiences and Internet confidence, and thus has both a direct and indirect impact on information literacy. The number of ICT-rich university courses has both an indirect (through experiences and confidence) and direct impact on information literacy. Personal Internet confidence positively correlates with experiences; higher confidence levels influence information literacy directly.

Materials and methods Student sample and sampling The sample included 299 university students who, in addition to the Information Literacy Test (Boh Podgornik et al., 2015), responded to several additional scales. The students attended C 2016 British Educational Research Association V


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courses at six faculties of the University of Ljubljana (N 5 221; 73.9%) and the University of Maribor (N 5 78; 26.1%), which are the two main higher education institutions in Slovenia. In the data analysis, all of the students were treated as a unique population and were not divided into subgroups based on demographics, as the examination of such characteristics was not an objective of the study. Although, as educators, we acknowledge such differences, they do not constitute a basis for the design of curricula (eg, the arrangement of groups based on gender or wealth). The supervised survey was conducted electronically in computer classrooms in spring 2014. University teachers, authors of the research, provided instructions for access through temporary passwords. The online questionnaire was prepared in the Slovenian 1ka open access survey system (https://www.1ka.si/). There were no specific time constraints, but respondents typically needed about 30 minutes to complete the survey. Prior to the data collecting, a unified introductory protocol was applied, including clarification of the purpose and instructions, explanation of voluntary participation and anonymity, and expression of acknowledgment for participation. No additional benefits or penalties were foreseen for students who participated in or declined the data collection. The first version of the questionnaire was preliminarily tested on a group of 45 students of science and technology. Subsequent discussion and reassessment of the results led to style and language optimisation, final formatting and improved design (Boh Podgornik et al., 2015). Structure and scales of the instrument The instrument used in our study was made up of a number of independent modules. This modular structure allows an independent statistical analysis of each module, as well as the potential exploration of connections between entire modules or individual items. The scales were assembled to allow their reuse in further studies in other contexts. The modules were as follows: (a) (b) (c) (d) (e)

IL test ICT experiences scale ICT-rich courses scale Internet confidence scale ICT ownership scale

IL test IL was assessed by a 40-item multiple choice test developed by the authors of the study (Boh Podgornik et al., 2015), with four answers to each question listed in alphabetical order. The test was designed and structured with the intention of providing an insight into the following four areas of information literacy: Information sources and databases (9 items)—assessment of knowledge about sources and databases, evaluation of reliability of sources; Search strategies (9 items)—assessment of knowledge about usage of key words, operators and the formation of search profiles; Intellectual property and ethics (10 items)—assessment of knowledge about referencing, citations, fair use of sources and ethical principles; Heuristics and critical evaluation (12 items)—assessment of heuristics methods, and synthesis of different aspects of information literacy.

The reliability of the test for our sample was at 0.71, calculated as Cronbach’s alpha. This can be recognised as acceptable for the purposes of such a study (Field, 2009). Interested readers can C 2016 British Educational Research Association V

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Searching for information (e.g., Google) Using social networks, forums and blogs (e.g., Facebook, Twitter) Watching videos (e.g., YouTube) Communication (email, MSN, Skype) Using e-learning materials and e-textbooks Reading newspapers and daily news on Internet portals Working with office tools (MS Office, Open Office) Using web maps (e.g., Google Maps) Preparing essays and project reports Using bibliographic databases (e.g., Scopus, Web of Science, EBSCO) Reading e-books and scientific papers. Playing games Editing and processing photos (e.g., Picasa, Photoshop) Editing and processing videos and animations Programming Designing web pages

ICT application

2.27 2.14 1.89 1.49 1.48 1.18

2.82 2.56 2.44 2.34

3.82 3.73 3.34 3.22

4.46 4.26

M

.90 1.10 .82 .69 .88 .51

.99 .80 .69 .78

.99 .99 .95 1.12

.68 .95

SD

2 2 2 1 1 1

3 2 2 2

4 3 3 3

5 5

.143 2.020 .744 .711 .086 .208

.411 .359 .594 .249

.120 .173 2.225 .161

.003 2.078

.038 2.158 .118 .011 .035 .102

.159 .516 .108 2.116

.304 .678 .453 .346

.626 .440

Communication and information

Writing and editing

Mode

0.478

0.640

alpha

11.066

22.091

F2

% var

F1

.672 2.066 .008 .117 .160 .104

.447 .177 .335 .696

.110 2.138 .576 .290

.115 2.194

Study

0.583

8.919

F3

.297 .190 .296 .331 .810 .788

.007 2.016 2.143 .056

2.014 .240 .203 2.193

.007 2.043

Advanced use

0.639

7.084

F4

.215 .664 .203 .058 .101 .007

2.050 .125 2.173 2.089

.701 2.128 2.134 .334

.166 .479

Entertainment

0.451

6.365

F5


11 12 13 14 15 16

7 8 9 10

3 4 5 6

1 2

Item #

Factor

Table 1: Frequency of use of ICT applications with factor loadings

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Table 2: Personal ownership of ICT devices and their frequency of use expressed as number and % of students, Mean (M) and Standard Deviation (SD) Item # 1 2 3 4

ICT device/frequency of use Smart phone

N 299

Less than Several times Almost More than Never once a week a week every day once a day M

43 3 14.4% 1.0% Portable computer 298 19 13 (laptop/notebook) 6.4% 4.3% Desktop computer (PC) 298 98 102 32.9% 34.2% Tablet computer or reader 297 201 39 (e.g., iPad, Kindle) 67.7% 13.1%

3 1.0% 15 5.0% 41 13.8% 25 8.4%

24 8.0% 69 23.2% 30 10.1% 15 5.1%

226 75.6% 182 60.9% 27 9.1% 17 5.7%

SD

4.29 1.42 4.28 1.15 2.28 1.27 1.68 2.55

obtain detailed information on metrics and subscales, as well as the full text of the Information Literacy Test for Higher Education, in Boh Podgornik et al. (2015). ICT experiences scale ICT experiences were assessed by a list of 16 ICT applications ranging from programming to playing games, as listed in Table 1. A 5-point Likert-type scale was used to identify the frequency of use of each application with the following definitions: 1—not at all, 2—less than once a week, 3—several times a week, 4—practically every day, 5—more than once a day. Thus, those who selected the use of each ICT application “more than once a day” collected a total of 80 points (16 3 5), and those who never used applications from the list received 16 points (16 3 1). Cronbach’s alpha of the ICT experience scale was calculated at 0.725. With the exclusion of the items “playing games” and “using social networks,” it would even be possible to raise alpha to 0.748; however, the initial design was retained in order to obtain more complete information about the use of ICT applications. ICT ownership scale For the population of students participating in the study, numerous computers are typically accessible in libraries and computer workrooms, as well as in communal spaces such as study rooms, entrance halls, corridors or similar. These devices provide access to IP-protected information resources such as databases and full-text journals. In university buildings, students also have broadband access to information resources by using personal ICT devices, either through the Eduroam service (https://www. eduroam.org/) or the password-controlled Wi-Fi networks of the respective institutions, as well as via the standard system of access through mobile phone operators. In order to access restricted resources through personal devices, a personal password is required. Password-protected access to restricted subscription-linked information resources is also possible from home. Access to information and to subscribed full-text papers is also possible through a number of databases (eg, Web of Science, Scopus, EBSCO), which are in addition used in the systematic evaluation of Slovenian researchers in the Slovenian current research information system (SICRIS) and the associated national catalogue and bibliographic database COBISS (Bartol, Budimir, Dekleva-Smrekar, Pusnik, & Juznic, 2014). Ownership of four classes of personal devices (smartphones, portable computers (laptops/notebooks), desktop computers (PC) and tablet computers or readers (eg, iPad, Kindle)) and frequency of use was assessed on a 5-point Likert-type scale. The scale values were defined as: 1—never; 2—less than once in a week; 3—several times in a week; 4—almost every day; and 5—more than once a day. The results are presented in Table 2. Thus, those who selected use of every ICT C 2016 British Educational Research Association V

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Table 3: The number of study courses in which the use of ICT tools to perform individual tasks was required during the current study year Item # 1 2

3

4

5

ICT supported courses

M

SD

Mode

Med

Total number of study courses in the current study year. Number of study courses in which you have used ICT in class (e.g., simulations, animations, computer-supported measurements – data-logging, programming). Number of courses in which at least a part of your assignments has been conducted on the Internet in interaction with the lecturer (e.g., Internet classrooms, Moodle). Number of courses in which you were asked to prepare a paper or similar assignment with a requirement to search for information on the Internet. Number of courses in which you were expected to search for information in specialised databases.

10.46 3.03

2.64 3.25

10 1

11 2

4.27

3.98

1

3

4.69

3.36

2

4

2.73

2.97

0

2

device they possess “more than once a day” collected a total of 20 points (4 3 5), and those who do not possess any of the ICT devices from the list received 4 points (4 3 1). ICT-rich courses scale The students were asked to provide information on the total number of courses they were attending in the current study year (two semesters), and the number of courses in which they gained hands-on experience with ICT either: (a) via lab work, (b) by using virtual classrooms, (c) preparing essay-type assignments using the Internet or (d) preparing assignments requiring specialised database searches. The students were instructed not to include routine uses of ICT such as those by lecturers, eg, as tools for presentation purposes. Descriptive statistics was used to describe trends. The results are presented in Table 3. Internet confidence scale As a model for assessment of the students’ personal Internet confidence scale, questionnaires used by Eastin and LaRose (2000) and Papanastasiou and Angeli (2008) were adapted for our purposes. Confidence was measured by a 10-item questionnaire using a 5-point Likert scale, in which the initial statement “I feel confident” was followed by statements describing different situations connected with search strategies and Internet use (Table 4). The answers ranged on a scale from totally agree (5) to totally disagree (1). Thus, those who totally agreed with all of the items provided collected a total of 50 points (10 3 5), and those who totally disagreed with all of the statements from a list received 10 points (10 3 1). The reliability of the Internet confidence scale, expressed as Cronbach’s alpha, was 0.783, which is satisfactory. Statistical procedures The study was descriptive in nature and followed established standard statistical methods for this type of exploratory study (eg, Balog, Pribeanu, Lamanauskas, & Slekiene, 2013; Eastin & LaRose, 2000; Papanastasiou & Angeli, 2008; Sumak, Hericˇko, & Pusnik, 2011). No prior interventions were made to influence outcomes. Statistical methods were used in an exploratory way, in order to reveal correlations, causalities and components. The statistical procedures employed were as follows: a. Descriptive statistics: prior to statistical analysis, variables were checked for normality. Means, standard deviations and frequencies are mostly reported. Where data are skewed, mode and median are reported as well. The reliability of the scales was explored by the C 2016 British Educational Research Association V

10

9

8

3 4 5 6 7

2

1

Item #

using information search strategies on web search engines such as Google, Yahoo, Bing, etc. because I know that I can find any information on the web without the help of others. using search strategies in local e-libraries and bibliographic databases. when communicating in online communities and social networks. when I must learn new software skills. in solving problems that can emerge when working on the Internet. because I know that I can solve problems by seeking help in online discussion forums when I need to present my own solutions and opinions to others on the Internet. by participating in online forums and communities with professional/ scientific content. using search strategies in international bibliographic databases, such as Web of Science and Scopus

I feel confident. . . (web use)

Factors

2.86

2.93

3.09

3.97 3.90 3.56 3.35 3.21

4.02

4.17

M

1.16

1.03

1.04

.92 .94 1.05 1.06 1.08

.88

.82

SD

3

3

3

4 4 4 3 3

4

5

.453

.765

.726

2.054 .281 .632 .655 .601

.185

.047

2.151

.060

.178

.315 .695 .055 .224 .359

.741

.775

Googling

Problem solving

Mod

0.669

0.759

alpha

19.736

26.262

F2

% var

F1

Table 4: Level of students’ personal confidence regarding different situations when working on the Internet

.660

.113

-.054

.835 -.058 .334 .061 .046

.068

.196

Information literacy

0.444

13.126

F3

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calculation of Cronbach’s alpha, and by further analysis with the “alpha if item deleted” procedure in order to foresee possible improvements of the scales. All of the scales fall within the range above 0.7, which is satisfactory. In order to preserve the breadth of the provided scales, no items were deleted from a pool, even if an increase in alpha was predicted. b. Factorial analysis: Principal Component Analysis with Varimax rotation was used to explore the factorial structure of the scales. Prior to the analyses, Kaiser-Meyer-Olkin (KMO) and Barlett’s test were performed, with all of the scales falling into the range in which further analyses are permitted. Factors with Eigenfactors above 1, and items with loadings above the 0.4 level, were considered significant. Direct Oblimin and Promax rotation did not produce stricter factor structures, so results from Varimax rotation are retained. c. Correlations: Correlations were checked as parts of analyses provided by Factorial and Regression procedures. Pearson’s correlation coefficients were calculated; coefficients below the 0.05 level (two-tailed) were considered significant. d. Regression analysis: Linear regression analysis was performed with the enter and casewise deletion option; variables below the 0.05 level (two-tailed) were considered significant. R statistical package was used according to procedures suggested by Field (2009). The SPSS 21V

Results and discussion Information literacy The results of the IL test, presented in Figure 2, show slightly skewed normal distribution in favour of higher achievements. The minimum was 9 points and the maximum was 36 points, with central values at: Mean 5 25.99, SD 5 4.74, Median 5 26 and Mode 5 27. Good outcomes of the cutting point for the lowest quartile at 23 points, for the median at 26, and for the upper quartile at 29 points of the total 40 possible points indicate that most students exhibit information literacy of a good basic level or even above. ICT experiences The results presented in Table 1 are ordered by decreasing mean. They reveal that the highest scores are obtained for applications connected with the use of the Internet—searching for

Figure 2: Frequency distribution of scores of the information literacy questionnaire (N 5 299) C 2016 British Educational Research Association V


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information, using social networks, watching videos and communication—that are performed on a daily basis. All of the top four applications belong to activities that are characteristic of digital natives and can be easily performed on smartphones. At the lower end of the table, we find applications (programming and designing web pages) where special skills are needed, and which are skipped by the majority of students (mode 5 1 [never]). Five factors (F1–F5) explaining 55.52% of variance are revealed (Table 1). Cronbach’s alpha for the entire scale was 0.725, but the values of individual factors calculated with the inclusion of the highest loading items in the procedure are lower because of the diversity of the items under observation. Additionally, even the rotated factor loadings of some items contribute to more than one factor at the >0.3 level, indicating the interconnectedness of some applications. The factors revealed are: •

• • •

•

Writing and editing factor—this factor links image and video editing with the preparation of student papers, essays or reports. From the answers about the number of ICT-rich university courses in combination with the item “Preparing essays and project reports,” we can conclude that most students had been asked to write at least one paper in the current study year. In general, students do not engage in these activities very frequently. Only 15 students (5.1%) report editing movies on a weekly or daily basis, and 44 students (14.7%) edit pictures. Communication and information factor—connects direct personal communication with the use of the Internet as a source of information. Study factor—connects items related to studying or the learning process and information literacy. Advanced use factor—relates to the advanced use of computers, such as programming and webpage design. The two items have the lowest activity frequencies and cannot be perceived as attributes of digital natives. Entertainment factor—connects activities where ICT is used for entertainment and social activities.

Only 16% of variance could be explained with regression analysis, demonstrating the complexity of the factors contributing to the development of IL. Regression analysis (B 5 23.103, t 5 9.943, p < .001, R2 5 0.161) reveals that only “Working with office tools” (beta 5 0.271, t 5 4.246) and “Using bibliographic databases” (beta 5 0.244, t 5 3.833) statistically significantly correlate with the IL score (at p < .001). “Using e-learning materials and e-textbooks” (beta 5 20.168, t 5 20.735, p 5 .007) and “Using web maps” (beta 5 20.138; t 5 22.166, p 5 .031) have negative coefficients, which is unusual and cannot be explained at this point. “Preparing seminar and project work” (beta 5 20.103, t 5 21.640) is only marginally significant. All other applications do not exhibit statistical significance at p < .1 levels. From the results of regression analysis, we can conclude that traditional assignments in the form of papers, essays or project work do not in themselves sufficiently promote students’ IL. Additionally, we can conclude that self-learned and generic skills attributed to digital natives do not lead to sufficient proficiency in information literacy in higher education. Therefore, in order to increase IL, the successful completion of student assignments should go beyond Googling and copy-paste procedures, and should include a broad spectrum of IL competencies and skills. Only in this way will it promote higher cognitive levels of thinking and more systematic learning. ICT ownership Smartphones included, all of the students under survey own at least one ICT device (Table 2). Around three quarters use smartphones (75.6%) and portable computers (71%) more than once a day. The prevalence of tablet computers and readers is still low, and around two thirds (67.7%) C 2016 British Educational Research Association V

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of the students report never using them. Only one in five (20%) students reports daily use of desktop computers, and 32.9% never use them. The correlation between the use of portable and desktop computers is negative (r 5 20.469; p < .001), showing that portable computers have in many cases replaced desktop devices. The higher use of desktop devices correlates with: (i) programming (r 5 0.225, p < .001), (ii) playing games (r 5 0.171, p < .001) and (iii) web page design (r 5 0.155, p < .001), all of which pertain to activities that require greater computer power, but that do not significantly relate to better IL. In addition, there is a weak correlation between the possession and frequency of use of smartphones and tablets (r 5 0.131; p < .05), showing that some of the top smartphone users are also tablet users. All other correlations are lower and statistically insignificant. The possession of ICT devices alone cannot directly predict IL (R2 5 0.036), a connection hypothetically predicted by our model (Figure 1). Among the listed devices, the only statistically significant predictor of IL is the ownership of tablet computers or readers, but in a negative sense (beta 5 20.191, t 5 23.269, p 5 .001), which is surprising. We might infer that tablets are not used for study purposes, but rather for leisure activities that distract students from educationrelated applications. When we correlate tablet ownership with ICT experiences, it appears that there are statistically significant, although not high, correlations with: (i) communication (email, MSN, Skype) (r 5 0.125; p < .05), (ii) using web maps (eg, Google Maps, Google Earth; r 5 0.161; p < .01), (iii) editing and processing photos (eg, Picasa, Photoshop) (r 5 0.154; p < .01) and (iv) editing and processing videos and animations (r 5 0.153; p < .05). All correlated applications are more connected with mobility and leisure activities than with the development of IL. On the other hand, ownership of ICT devices influences frequency of use, and explains 15% of ICT experience (B 5 29.587; t 5 14.636; p < .001, and R2 5 0.149). Possession of a portable computer (beta 5 0.354, t 5 5.717) and desktop computer (beta 5 0.268, t 5 4.292) show statistical significance at p < .001, possession of a tablet computer shows marginal significance (beta 5 0.111, t 5 2.010, p 5 .045), while possession of a smartphone shows no statistical significance (p 5 .136). The pattern of mobile phones and related applications being the most frequently used digital technologies but not affecting university work is also reported by Thinyane (2010). ICT-rich courses The results presented in Table 3 reveal that the share of courses (reported as Mean, Mode and Median) with the use of ICT (for other than classroom presentations or multimedia) is rather low. Study courses requiring the use of ICT in class (eg, simulations, animations, computer-supported measurements—data-logging, programming, etc.), and courses with assignments on the Internet in interaction with the lecturer (eg, Internet classrooms, Moodle), do not correlate with IL. This tendency could perhaps be changed by more scaffolded work of lecturers with students, with the development of IL skills and competencies in mind (Boh Podgornik et al., 2015). The data also show that every student is eventually required to prepare some kind of report, essay or similar product. However, it appears that many students have never been required to use science databases (Mode 5 0) as a source for academic research; instead, they seem to rely on loosely defined Internet searching (Googling), which is perhaps also the preferred option of some educators. From the results of regression analysis (B 5 24.196, t 5 21.593, p < .001; R2 5 0.068), we can draw the counterintuitive conclusion that ICT-rich university courses do not significantly contribute to the development of IL, and that only courses with an obligation to prepare study papers, essays or similar assignments by searching for information on the Internet (beta 5 0.270, t 5 3.842, p < .001 and r 5 0.235) positively contribute to a higher IL level. The results suggest that the requirement of using specialised databases has no such effects; statistically, in fact, it was C 2016 British Educational Research Association V


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Figure 3: Relationships between the ownership of ICT devices, ICT experiences, Internet confidence, ICT reach courses and information literacy. Pearson’s correlation coefficients are reported (** 5 p < .001; * 5p < .05)

negative (beta 5 20.147, t 5 22.174, p 5 .031). While, at first glance, the positive influence of searching the Internet seems plausible, the negative correlation with searching specialised databases is counterintuitive. Interpretation of these results calls for an additional in-depth study. We assume that the negative effect of searching specialised databases on IL might be a result of students’ inability to create efficient search strategies and queries. If students transfer their Googling experiences directly to simple searching in specialised databases, they may retrieve confusing or even completely incorrect results, especially if they pay no attention to the use of search operators and field tags. Not being satisfied with search results, the students might continue to favour the Internet over more structured information systems. In addition, a series of regression analyses revealed that various ICT-supported university courses could not be used as a predictor of ICT experiences and Internet confidence at statistically significant levels, although some low correlations could be found if the sum of all courses was considered. Correlations are reported in Figure 3. Based on the findings of the study, we can only propose the “learning by doing” strategy, as well as a stronger engagement of students in study activities that involve more intensive IL-related tasks. Internet (Web) confidence scale Students’ personal confidence regarding different situations when working on the Internet is reported in Table 4. Confidence is ordered by decreasing mean. Most of the students express high summative confidence levels (M 5 35.07, SD 5 5.843; Mode 5 32 and Median 5 35, with minimum 5 20 and max 5 50). Most feel very confident in “using information search strategies on web search engines such as Google, Yahoo, Bing, etc.” (mode 5 5), and are least confident when C 2016 British Educational Research Association V

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“using search strategies in international databases, such as Web of Science and Scopus” (mode 5 3). With the use of principal component analysis, it was possible to identify three factors explaining 59% of variance. The first factor, Problem Solving, explains 26% of variance and comprises items in which students show the greatest insecurity, such as problem solving, presentation of their own ideas, and participation in scientific forums. The second factor, Googling, explains 19.7% of variance, and comprises three items in which students show the highest levels of confidence, related to searching for information and participation in social forums. The third factor (13%) comprises two items closely connected with IL. From the structure of this factor, we can conclude that IL is clearly separated from skills needed on the Internet. While the first two factors have appropriate alphas, the third factor should be considered with some caution. Self-reported confidence levels of web use are statistically a poor predictor of IL (B 5 20,261, t 5 11.003, p < .001, R2 5 .097). Two items, “using search strategies in local e-libraries and bibliographic databases” (beta 5 0,156, t 5 2.482, p 5 .014) and “when communicating in online communities and social networks” (beta 5 0.130, t 5 1.955, p 5 .052), predict IL positively. Two other items, “by participating in online forums communities with professional/scientific content” (beta 5 20.150, t 5 22.110, p 5 .036) and “using search strategies in international bibliographic databases, such as Web of Science and Scopus’ (beta 5 20.128, t 5 21.967, p 5 .050), predict IL negatively. The latter finding is counterintuitive and calls for additional research and scrutiny in order to understand the causes. Interscale correlations Correlation analysis (Figure 3) shows that ownership of ICT devices, ICT experiences, the number of ICT-rich university courses and Internet confidence, calculated as sums, correlate between each other but not with information literacy. Regression analysis explains only a minor effect of ICT ownership, web experiences, ICT-rich university courses and web confidence on IL (B 5 24.941; t 5 11.468; p < .001; R2 5 0.072). From the results, it can be deduced that IL is only marginally affected by students’ ICT experience and personal confidence, and negatively affected by the possession and use of ICT devices, which are all attributes of digital nativeness. The only thing that really seems to promote IL are university courses in which IL-enriched tasks are embedded in the study assignments. The findings challenge the digital native concept in the sense of Prensky (2001a, b), and are closer to the digital fluency model proposed by Wang et al. (2013). However, digital natives may exhibit less anxiety in relation to ICT and the Internet, a condition reported in many previous studies (Powell, 2013; Venkatesh, 2000). This is evidenced in the present study by high self-reported confidence levels that are closely connected to the daily use of a variety of applications, dominated by the use of Internet applications and communication. On the other hand, ownership of ICT devices and ICT experiences outside university courses do not contribute to a higher IL level. The gap between the daily use of computer applications, on the one hand, and certain known benchmarks of digital literacy in higher education, on the other hand, is wide and correlations are weak, a pattern already identified in previous studies (Magrino & Sorrell, 2013; Thompson, 2013). Thus, university educators cannot rely on information competencies gained in secondary schools or through informal channels (Korobili, Malliari, & Zapounidou, 2011; Li & Ranieri, 2010; van Deursen & van Diepen, 2013). In conclusion, it can be stated that the mere use of computers at universities, even if fully adopted by university educators and students, does not contribute to better information literacy (Jensen, 2004; Messineo & DeOllos, 2005). IL must be taught and practised in specially designed courses, or thoughtfully infused into university programmes (Greene, Yu, & Copeland, 2014; Magrino & Sorrell, 2013, 2014; Ng, 2012). Findings from our study are in agreement with Magrino and Sorell (2014), who state that: “This is not to say that today’s students are not proficient in the C 2016 British Educational Research Association V


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personal use of social media but rather that they need guidance as to how to make their relationship with social media into a professional one” (p. 79), and that “. . .the capacity for a more productive use of technology is present in today’s students—it is the shift in worldview from personal to professional that needs to be effected” (p.79). It is obvious that we cannot change students’ personal characteristics, demographic status or previous learning history, but we can change the content of university courses, teaching methodology and teaching styles, thus gradually influencing students’ attitudes and raising awareness to support IL skills, knowledge and competencies, as well as transforming their informal knowledge into professional knowledge. While our study can be recognised as representative for the population of Science and Technology oriented students, generalisation of results to students from other study streams, such as Social Sciences and Humanities, is possible, but should be supported by additional research. Given that the intention of this study was not to search for causation of IL in the general population, we propose follow-up studies pursuing the same reasoning but with population-specific modified survey instruments. Furthermore, the study was not international, and with the understanding that findings from one culture cannot, without reserve, be transferred to other cultures, follow-up studies would reveal whether our findings are valid only locally, or globally as well. The findings of our study indicate that digital natives are not necessarily information literate, and that their daily ICT activities only marginally correlate with course work leading to the advancement of academic and professional competencies. These findings are in line with recent research of other authors. The “common knowledge” is that instruction should be tailored to the skills of existing digital natives (OECD, 2008). We can agree that digital natives need different forms of instruction (Bennett et al, 2008; OECD, 2008; Prensky, 2001, a, b), but not in a different direction, at least not at the university and college levels, where higher level digital skills are expected. Attributes of digital natives (Prensky 2001a, b; Teo, 2013, 2015; Thompson, 2015) reflect their living culture, but these attributes are not necessarily in line with academic and professional work, nor can they be used as a basis for instruction without being subject to examination (Kirschner & van Merri€enboer, 2013). According to Spangler, Rodi, DeLorenzo, and Kohun (2015, p.28), “digital native culture is lacking in software, hardware, and overall knowledge about sophisticated computer programs and programming.” Loh and Kanai (2015, p. 1) report that: “Growing up with Internet technologies, digital natives gravitate toward shallow information processing behaviours, characterized by rapid attention shifting and reduced deliberations. They engage in increased multitasking behaviours that are linked to increased distractibility and poor executive control abilities.” According to Carrier, Rosen, Cheever, and Lim (2015, p. 1.), “Empirical research shows that studying, doing homework, learning during lectures, learning from other sources, grades, and GPA [grade-point average] likely are all negatively affected by concurrent multitasking with technology.” A study by Teo (2015, p. 12) “provides empirical evidence to dispel the popular belief of digital natives to be necessarily more technologically proficient”. Recently, some authors have combined digital skills with information literacy through a blended learning approach (Liou, Yu, Tsai, & Cheng, 2015), or attempted to develop higher level digital skills by using a technology enhanced discursive approach (Fitzgerald & Henderson-Martin, 2015), employed as a vehicle to learn with technology (Skiba, Connors, & Jeffries, 2008). In addition, introductory courses to fill the missing gaps should be provided on transition from secondary to tertiary education (Brown, 2015). Conclusions The main objective of our study was to identify factors affecting the information literacy of university students, which is an essential outcome of higher education. The results suggest that C 2016 British Educational Research Association V

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attributes describing digital natives are poor predictors of IL, thus refuting our hypothetical assumptions that the information literacy of students is affected by ICT experiences, possession of ICT devices, involvement in ICT-rich courses and web confidence. According to the initially defined research questions and hypotheses, the study reveals that: • • •

• • •

ICT experiences expressed as the sum of different applications and uses do not contribute to better information literacy (rejected initial hypothesis). The impact of various ICT applications on information literacy ranges from negative to positive (hypothesis confirmed). Personal ownership of smartphones, mobile computers and desktop computers has no direct effect on IL. Most interestingly, possession of a tablet computer is a negative predictor of IL (hypothesis mostly rejected). Personal ownership of ICT devices has an impact on ICT experiences and web confidence, and, therefore, has an indirect impact on information literacy (hypothesis confirmed). ICT-rich university courses have only a marginal impact on IL (hypothesis mostly rejected). ICT-rich university courses have an impact on ICT experiences and student confidence (hypothesis confirmed).

From the findings outlined above, the overall conclusion is that digital natives are not necessarily information literate, and that we, as university educators, must prepare and introduce study courses with hands-on and minds-on activities, in which all aspects of IL, as described in the standards and summarised above, are included in the curriculum and thoroughly evaluated. Acknowledgements The work was supported by the grant J5-5535 of the Slovenian Research Agency allocated to the project “Development of information literacy of university students as a support for solving authentic science problems.” Neither the Agency nor its employees had any influence on the work in progress. The authors would also like to thank Neville Hall for proofreading the manuscript. Statements on open data, ethics and conflict of interest Primary anonymised data for secondary analyses is available on request from the authors in electronic form as an Excel file. In the case of data usage, it is expected that the publication source will be properly cited. No potential conflict of interest was reported by the authors. References ALA (2000). American Library Association. Information Literacy Competency Standards for Higher Education. Retrieved March 12, 2016, from http://www.ala.org/acrl/sites/ala.org.acrl/files/content/standards/ standards.pdf Balog, A., Pribeanu, C., Lamanauskas, V., & Slekiene, V. (2013). A multidimensional model for the exploration of negative effects of social networking websites as perceived by students. Journal of Baltic Science Education, 12(3), 378–388. Bartol, T., Budimir, G., Dekleva-Smrekar, D., Pusnik, M., & Juznic, P. (2014). Assessment of research fields in Scopus and Web of Science in the view of national research evaluation in Slovenia. Scientometrics, 98(2), 1491–1504. doi:10.1007/s11192-013-1148-8. Bavelier, D., Green, C. S., & Dye, M. W. G. (2010). Children, wired: for better and for worse. Neuron, 67(5), 692–701. Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: a critical review of the evidence. British Journal of Educational Technology, 39(5), 775–786. doi:10.1111/j.1467-8535.2007.00793.x. Bimber, B. (2000). Measuring the gender gap on the internet. Social Science Quarterly, 81(3), 868–876. C 2016 British Educational Research Association V


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Thompson, P. (2015). How digital native learners describe themselves. Education and Information Technologies, 20(3), 467–484. van Deursen, A. J. A. M., & van Diepen, S. (2013). Information and strategic internet skills of secondary students: a performance test. Computers & Education, 63, 218–226. van Dijk, J., & Hacker, K. (2003). The digital divide as a complex and dynamic phenomenon. Information Society, 19(4), 315–326. Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. Vigdor, J. L., Ladd, H. F., & Martinez, E. (2014). Scaling the digital divide: home computer technology and student achievement. Economic Inquiry, 52(3), 1103–1119. Wang, Q. E., Myers, M. D., & Sundaram, D. (2013). Digital natives and digital immigrants towards a model of digital fluency. Business & Information Systems Engineering, 5(6), 409–419.
