Table of Contents

  • Our increasingly technology-rich world raises new possibilities and new concerns for education. First, technology can provide tools for improving the teaching and learning process, thereby opening new opportunities and avenues. In particular, it can enhance the customisation of the educational process by adapting it to the particular needs of the student. Second, as education prepares students for adult life, it must provide them with the skills they need to participate in a society that increasingly requires technology-related competences. The development of these competences, which are part of the set of the so-called 21st century competences, is becoming an integral part of the goals of compulsory education. Finally, in a knowledge economy driven by technology, people who do not master these competences may suffer from a new form of digital divide that may affect their capacity to fully participate in the knowledge economy and society.

  • What is the relationship between technology use and educational performance in science? The OECD PIS A (Programme for International Student Assessment) provides a source of evidence for the analysis of this relationship. This report presents the main findings and policy implications of this analysis. This work was carried out under the umbrella of CERI ’s New Millennium Learners project. The work presented here updates the findings of a previous report (OECD, 2006) and seeks to go deeper into the determinants of technology use, both in frequency and in purpose, and into the impact on educational performance.

  • An increasingly technology-rich world is bound to have important implications for education. Accordingly, OECD countries have undertaken significant investments to enhance the role of ICT in education. This raises the question of whether investment in ICT for education is fulfilling expectations. PISA 2006 provides a wealth of comparative data to start to answer this question and to shed light on the availability and use of ICT and its benefits. The analysis of these data can also help identify potential bottlenecks that may make it difficult to achieve the desired effects and may, therefore, be of use for policy formulation.

  • There have been three main policy expectations regarding technology in education. The first was that schools would equip students with the technical skills required by an increasingly technology-pervaded economy. The second was that schools would bridge the digital divide by providing students with universal access to computers and the Internet during compulsory education. The third was that technology would improve educational productivity by making teaching and learning more effective – improving learning outcomes by changing teaching and learning strategies. In some respects, it would seem that the initial policy expectations have not been fulfilled, but a closer analysis shows the need to reframe them in light of changing societal needs. In particular, the issue of the effects of technology use on educational performance should be reviewed.

  • The analysis of students’ access to ICT takes a multidimensional approach to access to ICT resources to look not only at physical access but also at students’ opportunities to use ICT resources for educational purposes at home and at school, across and within the countries. While OECD countries have made significant progress in physical access to computers at home and at school, more efforts should be made to enrich the educational opportunities offered by ICT resources in particular regarding access to the Internet and to quality digital learning resources.

  • Examination of the frequency and patterns of ICT use provides a picture of how students are taking advantage of the opportunities made available by ICT in schools and at home. Once they have access, types of ICT use depend on variables related to students’ cognitive, cultural and socio-demographic characteristics. This chapter gives special attention to students’ gender and socio-economic status and offers for the first time an analysis of user profiles that go beyond traditional stereotypes. It also presents a detailed analysis of students’ attitudes to ICT and how they relate to performance in science.

  • This chapter explores the complex relationship between ICT use and performance based on two types of analysis. First, it takes a general look at the correlation between students’ scores in the PISA 2006 science test and four aspects of ICT use: students’ experience with computers, overall use at home and at school, types of computer use, and confidence in performing tasks on a computer. In line with the general focus of this report, this chapter gives special attention to the influence of family background on students’ results by controlling for economic, social and cultural status. Second, it analyses in more detail the influence of ICT use on student performance by controlling for other variables measured in PISA which might also affect 15-year-old students’ science/mathematics scores, such as students’ characteristics, parents’ characteristics, household characteristics and school characteristics. In this way it provides a clearer picture of the net influence of ICT use on students’ performance in the PISA science test.

  • PISA 2006 data have unveiled a number of interesting messages in terms of ICT accessibility and use by 15.year.old students. This chapter summaries the main findings and draws out some important policy recommendations particularly regarding how education can cope with the emerging second digital divide, the importance of ICT in the development of 21st century skills and, finally, the need for monitoring progress over time, particularly through dedicated indicators of educational uses of ICT.

  • Three options that were not included in the original categorisation are explored here:

    1. Moving “browsing the Internet” from “leisure use” to “educational use”;



    2. Testing the impact of frequency by using two categories (e.g. “high use” and “low use”) instead of three categories, which would make it more difficult to be included in the category “high use” than in that of “frequent use” (as in the original categorisation);



    3. Testing the impact of using two indices developed in PIS A 2006: