Exploring the effects of information and communication technology (ICT) on the educational achievements of pupils, represent a unique challenge, because learning outcomes are influenced by several interrelated factors and processes. The use of ICT for teaching and learning is often associated with factors such as method of teaching and learning, student characteristics, characteristics of school leadership, assessment features, etc. Therefore in analyzing the effects of ICT in pupils’ achievement it is crucial to consider ICT as a factor in conjunction with other factors and with understanding of the relationship of ICT and other factors. For instance in measuring the impact of computer use on student achievement a causal relationship can be expressed as the basic linear regression model; however, the pupils’ achievement in addition to computer use is also influenced by other factors not included in the model. Also it should be realized that the relationships between variables are not always linear, in relationship between variables may be a loop - for example, the use of ICT impacts greater motivation for learning, which improves achievement, and the return affect of higher achievement could be increased motivation to use ICT. In order to better explain the relationship between the dependent (pupils’ achievement) and independent (ICT indicators, sociodemographic variables, student characteristics...) variables, we will use linear structural modeling, which combines regression and factor analysis.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 31011421The report shows a cross national comparison amongst 25 EU counries accros main risks on the internet (meeting strangers, pornography, sexting, cyberbullying).
F.35 Other
This talk discusses country differences in opportunities and risks associated with children’s internet use. First, it outlines the logic of cross country comparisons where survey data from 25 countries is used to analyse the country level differences in the patterns of opportunities and risks. Secondly, it reviews findings regarding the patterns of opportunities and risks in European children’s use of the internet. These reveal that children in wealthier countries (measured by GDP) encounter more online risk but, arguably, these countries are also well placed to provide more accessible and user‐friendly safety resources for children and parents. Also, countries with more press freedom, such as Nordic and Baltic countries, are more likely to have children who encounter online risk – this may be because of lower internet regulation and strategies that ensure safety without introducing censorship are thus needed. At the country level, there is no systematic relation between level of parental filtering in acountry and children’s risk experiences, although there is a small relationship at the individual level – children whose parents use a filter are less likely to have encountered sexual content, suggesting filters can play a useful role. Degree of broadband penetration, and length of time in which most people have had internet access, are associated with greater online risks, but not greater online activities among children – this suggests that, while children are motivated to use the internet everywhere in Europe, higher quality access is bringing more risks than are adequately dealt with by policymakers (whether industry, state or education). In countries with 15+ years of schooling on average, children are more likely to have better digital skills, as are children from countries where more schools use computers in the classroom. Education clearly has a positive role to play in supporting digital skills, literacies and citizenship, and should be supported across all countries.
B.04 Guest lecture