16 SES 11 B, ICT Competencies
The Internet has become an essential part of our everyday life and a matter of course, especially for children and teenagers. Data from PISA shows that the time students spent online increased in the past years in all OECD countries. In PISA 2012, the students spent 105 minutes online outside school on a typical weekday on OECD average; in 2015 this number has increased to 138 minutes (OECD 2017a, p. 476). Moreover, the data shows that an increasing number of students spend a rather high amount of time on the internet. On average, about 7% of students in OECD countries reported in 2012 that they spent more than six hours online every day outside of school. In 2015, 16.2 % of the students reported to be online more than 6 hours a day. Those students, who are called extreme internet users by the OECD (OECD 2016, p. 4; 2017a, p. 227) are the main interest of this paper in which we examine the relationship between Internet use and student achievement over time in selected European countries. This paper uses data from PISA 2015 to examine possible effects of internet use on student competencies in mathematics, science and reading.
In contrast to previous studies, the focus of this paper does not lie on the frequency of Internet use, but on the daily time students spend online. In addition, possible differences in performance depending on the type of internet use and on the learning time at home are examined. The theoretical framework referring to the time spent online focuses on Walberg’s theory of educational productivity (Walberg, 1984), especially on the aspect of learning time. The weekly learning time as a possible explanation for the connection between Internet use and performance has not yet been considered in previous studies. In the context of the different types of internet use we refer to Jonassen et al. (1998), who described computers as cognitive tools.
The analysis is based on data from PISA 2015. We use student achievement data from all three PISA subjects (Reading, mathematics, science) as dependent variables. All the other variables come from the PISA student questionnaires. For the internet usage time we used the question in which the students were asked for how long they use the Internet outside of school during a typical weekday and on a typical weekend day. The seven categories range from “no time” to “more than 6 hours per day”. Referring to the OECD (OECD 2016, p. 3) we set the cut for the extreme internet users at more than 6 hours per day. The variable “extreme user” includes all students who are more than 6 hours online on both weekday and weekend. The Internet usage types are based on the question of how often the students use the computer outside school for certain activities. A two-stage procedure was used to investigate the factor structure of the types of use (DeVellis 2016, p. 113f). The following variables are used as control variables: PISA index of economic, social and cultural status (ESCS), index of highest educational level of parents (PARED), home possessions (HOMEPOS), index of immigrant background (IMMIG), relative grade index (GRADE) and ICT use outside of school for schoolwork (HOMESCH) (OECD 2017b).
First results showed that extreme internet use (over six hours per day) is associated with lower student competencies in mathematics (OECD, 2016). It is expected that the results for Austria and also for other European countries show excessive Internet usage of 15-year-olds is accompanied by significantly lower performance in mathematics, reading and science, even when controlling for individual and family background. We also expected that internet usage for information search and for school tasks is positively correlated with student achievement, while Internet usage for entertainment and gaming is accompanied with poorer student performance. Further it is expected that that shorter learning time is not decisive for the lower performance of extreme Internet users by using a mediator analyses.
DeVellis, R. F. (2016). Scale Development: Theory and Applications: SAGE Publications. Biagi, F., & Loi, M. (2013). Measuring ICT Use and Learning Outcomes: evidence from recent econometric studies. European Journal of Education 48, 28-42. Jonassen, D. H. (1996). Computers in the classroom. Mindtools for critical thinking. Englewood Cliffs, NJ: Prentice Hall. Jonassen, D. H., Carr, C., & Yueh, H.-P. (1998). Computers as mindtools for engaging learners in critical thinking. TechTrends 43, 24-32. OECD. (2017a). PISA 2015 Results (Volume III). Students' Well-Being. Paris: OECD Publishing. OECD. (2017b). PISA 2015 Technical Report. Paris: OECD Publishing. OECD. (2016). Does it matter how much time students spend on line outside of school? PISA in Focus, 2016/01. DOI: http://dx.doi.org/10.1787/5jm5fb7cq0zp-en Senkbeil, M., & Wittwer, J. (2013). Relationship between Computer Use and Educational Achievement. In L. Rutowski, M. von Davier & D. Rutowski (Hrsg.), A handbook of international large-scale assessment: Background, technical issues, and methods of data analysis (S. 287-298). London: Chapman & Hall. Walberg, H. J. (2006). Improving educational productivity. An assessment of extant research. In R. F. Subotnik & H. J. Walberg (Hrsg.), The Scientific Basis of Educational Productivity (S. 103-160). Greenwich, CT: Information Age Publishing. Walberg, H. J. (1984). Improving the Productivity of America's Schools. Educational Leadership 41, 19-27.
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