While computer technology permeates all spheres of life in modern society, the outcomes of using technology are different for different groups. The “digital divide” is probably one of the most undefined terms in educational research. One of the most pressing challenges is the definition of digital divide itself. Many of the contemporary research papers still focus on the gaps in access/possession of technology while this issue is not as relevant as it used to be. Most contemporary research papers simply do not even provide a definition of the construct they investigate. Those who target higher levels of the digital divide do not define the strand they focus on or use different interchangeable terms to name it. This article reviews contemporary literature critically and clarifies basic terms related to the digital divide with a specific focus on computer and information literacy. It also provides a definition of the digital divide that shifts focus from the traditional understanding of its levels and focuses on computer and information literacy as its core, as this construct encompasses all “new literacies” and is crucial for functioning and achieving outcomes in the information society.

This paper presents the R Analyzer for Large-Scale Assessments (), a newly developed package for analyzing data from studies using complex sampling and assessment designs. Such studies are, for example, the IEA’s Trends in International Mathematics and Science Study and the OECD’s Programme for International Student Assessment. The package covers all cycles from a broad range of studies. The paper presents the architecture of the package, the overall workflow and illustrates some basic analyses using it. The package is open-source and free of charge. Other software packages for analyzing large-scale assessment data exist, some of them are proprietary, others are open-source. However, is the first comprehensive package, designed for the user experience and has some distinctive features. One innovation is that the package can convert SPSS data from large scale assessments into native data sets. It can also do so for PISA data from cycles prior to 2015, where the data is provided in tab-delimited text files along with SPSS control syntax files. Another feature is the availability of a graphical user interface, which is also written in and operates in any operating system where a full copy of can be installed. The output from any analysis function is written into an MS Excel workbook with multiple sheets for the estimates, model statistics, analysis information and the calling syntax itself for reproducing the analysis in future. The flexible design of allows for the quick addition of new studies, analysis types and features to the existing ones.

This article analyses educational inequalities related to socioeconomic status (SES) in 12 Eastern European countries that participated in the International Reading Literacy Study (PIRLS) 2006. Economies and educational systems of these countries have undergone critical transformations since the fall of communism. The authors' analyses, using data collected almost 20 years after this period, help explain how these transformations affected the equity and quality of educational outcomes in the region. For each country, overall inequalities as well as inequalities between schools and within schools are estimated with regression models and represented graphically with socioeconomic gradient lines. A possible trade-off between equity and quality of outcomes is explored, identifying countries that have been relatively successful at attaining both educational goals. The extent to which the school SES explains achievement gaps between rural and urban schools is analysed. The results point to country groupings that are reasonably consistent with regional classifications of educational systems postulated in the literature.

International large-scale student assessments (ILSAs) in education represent a valuable source of information for policy-makers, not only on student achievements, but also on their relationship with different contextual factors. The results are partly described in the official studies’ reports; more can be derived from the publicly released data sets. However, league tables are often the only evidence used in policy debates and decisions on education. Indeed, the comparison of student achievement across the participating educational systems is a legitimate proxy for estimating countries’ development and productivity, but the use of league tables more often turns into ‘horse-ranking’, ignoring the contexts of teaching and learning. This is often supported by the media, turning the use of results into their abuse. The purpose of this paper is to discuss the use and misuse of league tables in reporting ILSA results, vs. the use of data for in-depth analysis in order to make informed decisions.