Is there a gap? A Meta-analytic Review of the Gender and SES Effects on Students’ ICT Literacy

Author(s):

Fazilat Siddiq(presenting / submitting)Ronny Scherer(presenting)

Conference:

ECER 2017

Network:

16. ICT in Education and Training

Format:

Paper

Session Information

16 SES 01 A, Research on K-12 Students' Digital Competence

Paper Session

Time:

2017-08-22

13:15-14:45

Room:

W4.23

Chair:

Anders D. Olofsson

Contribution

The increasing presence of technology in many areas of our lives has affected and changed the way we work and interact with others. Due to these rapid changes, the educational systems in many countries have included information and communication technology (ICT) as a part of the national curricula, and labeled the competent use of it as “ICT literacy”. ICT literacy has been outlined in several national and international frameworks (Claro et al., 2012; Ferrari, 2013), and typically comprises competence areas such as information, content-creation, communication, safety, and problem solving. At the same time, inequalities in students’ levels of ICT literacy – that is the so-called “digital divide” (van Dijk, 2006) – has gained considerable attention. Differences in ICT literacy across students’ socio-economic status (SES) and gender have been pointed out as two sources of the digital divide. In fact, students’ socio-economic background, including the language spoken at home, the number of books at home, the parents’ educational level, household income, access to ICT, and academic aspirations, has been identified as a significant predictor of ICT literacy (Hatlevik & Gudmundsdottir, 2013), independent of students’ access to and frequency of ICT use.

Even though, overall positive effects of SES on students’ ICT literacy have been identified across studies, there is little knowledge about the size and variations in these effects. Furthermore, research on gender differences abounds with conflicting findings. A number of studies identified differences in favor of girls (Fraillon et al., 2014), while others have reported differences in favor of boys (Calvani, Fini, Ranieri, & Picci, 2012). Moreover, there are also studies on assessment of ICT literacy which could not identify gender differences (Claro et al., 2012). These inconsistent findings warrant in-depth examinations of the relations between students’ ICT literacy achievement and their SES and gender in order to understand inequalities in ICT literacy.

In a recently published systematic review, Siddiq and colleagues (2016) examined ICT literacy assessments and found that these assessments, by and large, measure only specific aspects of ICT literacy, yet not all the competence areas of existing ICT literacy frameworks (Ferrari, 2013). This study also revealed that the competence area information is most frequently measured, whereas, there are few tests which measure students’ digital communication, collaboration, safety, and problem solving competences. Hence, the opposing findings on gender and ICT literacy score may be due to the reporting of ICT literacy score as a composite scale, even though it consists of several competence areas.

The present meta-analysis consequently investigates the gender differences and the effect of SES on students’ ICT literacy by taking a detailed view on the construct. To our knowledge, a synthesis of this kind is still lacking in the existing body of research, and could provide more detailed insights into relevant issues related to the digital divide, that otherwise would not be readily available or obvious from individual studies (Fan & Chen, 2001). In particular, the study addresses the following research questions:

What is the effect of SES on the competence areas within the ICT literacy framework? Are there discrepancies between the effects of SES on the competence areas versus ICT literacy reported as a composite scale?
How can the gender differences in students’ achievement on the competence areas within the ICT literacy framework be described? Do these findings differ significantly from gender differences reported on ICT literacy as a composite scale?

Method

We extended the recent systematic review on ICT literacy assessments (Siddiq et al., 2016) by updating the search applying the same search procedures and selection criteria. We identified five new studies which were published after our last search that ended November 2014. These studies were coded using the coding scheme described in the systematic review (Siddiq et al., 2016). In total, 42 studies were selected for further meta-analytic inquiries. The relevant effect sizes were extracted from each study, and analyses were conducted on studies that reported the correlations between the ICT literacy score and SES, and/or effect sizes for gender in the six competence areas as described in Siddiq et al. (2016). To understand what might have contributed to the inconsistent gender differences in the studies, a number of moderator variables were selected on the basis of previous knowledge in the research domain: (1) publication year; (2) age group (primary, lower secondary or upper secondary); (3) nationality; (4) publication type (e.g., journal article, dissertation). Univariate meta-analysis was conducted individually for each of the six competence areas (i.e., Information, Communication, Content Creation, Safety, Problem Solving, and Technical Operational Skills). We also performed heterogeneity analysis for each competence area by computing I2 index to quantify the extent of heterogeneity in each univariate analysis (Huedo-Medina, Sánchez-Meca, Marín-Martínez, & Botella, 2006). In order to synthesize the effect sizes and correlations across studies, a random-effects model (Borenstein, Hedges, Higgins, & Rothstein, 2009) that explicitly estimates variance between studies was applied. This model will be further extended to a mixed-effects model by including relevant moderator variables. In order to ensure that our findings are not subject to selection or publication bias, we will perform sensitivity analyses and further bias checks. For instance, analysis of publication bias will be conducted by use of funnel plots (Borenstein et al., 2009). Test results of funnel plot asymmetries are obtained as an indication of publication bias using standard error as the moderator, and are often used as one approach to test the validity of the meta-anlaysis. Furthermore, Rosenthal’s fail-safe number approach (Borenstein et al., 2009) will be taken to evaluate whether publication bias (if it exists) can be safely ignored. The fail-safe number procedure calculates how many insignificant effect sizes of unpublished studies would be required to reduce the total effect size to the insignificant level.

Expected Outcomes

This study will provide detailed information on gender differences in the competence areas of ICT literacy frameworks. This can provide researchers and educators with crucial knowledge in order to plan relevant interventions that promote the empowerment of students to use ICT for learning, and to obtain equitable access to further ICT education and career opportunities. Previous research has shown that male students use computers more frequently than females in K-12 education (Meelissen & Drent 2008). Sáinz and Eccles’ (2012) studied high school students’ ICT self-efficacy, and showed that female students’ perceptions of their own ICT abilities significantly declined over time, while male students’ perceptions increased over time. This may create an even greater gender gap, because students’ self-efficacy affects the development of their intrinsic motivation and interest in using and applying specific technologies in their own interest areas. This selection towards certain interests can in the next turn lead to differences in professional career pursuits and ICT expertise (Hohlfeld, Ritzhaupt, & Barron, 2013). Furthermore, the findings of this meta-analysis comprising the effect of SES on students’ ICT literacy will be vital, as they could inform policy and practice on the extent of the digital divide. This knowledge could be used to develop programs which aim at narrow such divide. This is crucial from a social justice perspective; since principles of fairness and equity emphasize that students should be provided with equal opportunities within and across classrooms and schools (OECD, 2001). We expect further moderator analyses to explain the variation of the effect sizes and correlations between ICT literacy achievement and gender and SES in the identified studies.

References

Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester, UK:Wiley Calvani, A., Fini, A., Ranieri, M., & Picci, P. (2012). Are young generations in secondary school digitally competent? A study on Italian teenagers. Computers & Education, 58, 797–807. doi:10.1016/j.compedu.2011.10.004 Claro, M., Preiss, D. D., San Martín, E., Jara, I., Hinostroza, J. E., Valenzuela, S., et al. (2012). Assessment of 21st-century ICT skills in Chile: Test design and results from high school level students. Computers & Education, 59, 1042–1053. Fan, X., Chen, M. (2001). Parental involvement and students’ academic achievement: A Meta-Analysis. Educational Psychology Review, 13(1), 1-22. Ferrari, A. (2013). DIGCOMP: A framework for developing and understanding digital competence in Europe. Luxembourg: Publications Office of the European Union. doi:10.2788/52966 Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (2014). Preparing for life in a digital age. The IEA International Computer and Information Literacy Study, international report. IEA: Springer Open. Griffin, P., McGaw, B., & Care, E. (2012). Assessment and Teaching of 21st Century Skills. Melbourne, Australia: Springer. doi:10.1007/978-94-007-2324-5 Hatlevik, O. E., & Gudmundsdottir, G. B. (2013). An emerging digital divide in urban school children’s information literacy: Challenging equity in the Norwegian school system. First Monday, 18, 4. doi:10.5210/fm.v18i4.4232 Hohlfeld, T. N., Ritzhaupt, A. D., & Barron, A. E. (2013). Are gender differences in perceived and demonstrated technology literacy significant? It depends on the model. Education Tech Research Dev, 61, 639–663. doi:10.1007/s11423-013-9304-7 Huedo-Medina, T. B., Sánchez-Meca, J., Marín-Martínez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychological Methods, 11, 193–206. doi:10.1037/1082-989X.11.2.193 Meelissen, M. R. M., & Drent, M. (2008). Gender differences in computer attitudes: Does the school matter? Computers in Human Behavior, 24(3), 969–985. OECD. (2001). Schooling for tomorrow. Learning to change: ICT in schools. Education and skills. Paris: OECD. Sáinz, M., & Eccles, J. (2012). Self-concept of computer and math ability: Gender implications across time and within ICT studies. Journal of Vocational Behavior, 80(2), 486–499. Siddiq, F., Hatlevik, O. E., Olsen, R. V., Throndsen, I., & Scherer, R. (2016). Taking a future perspective by learning from the past—A systematic review of assessment instruments that aim to measure primary and secondary school students’ ICT literacy. Educational Research Review, 19, 58-84. doi:10.1016/j.edurev.2016.05.002 Van Dijk, J. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4–5), 221–235. doi:10.1016/j.poetic.2006.05.004

Author Information

Fazilat Siddiq (presenting / submitting)

Nordic Institute for Studies in Innovation, Research and Education

Oslo

Ronny Scherer (presenting)

University of Oslo

Centre for Educational Measurement at the University of Oslo (CEMO)