Advertisement

Zeitschrift für Erziehungswissenschaft

, Volume 20, Issue 4, pp 585–603 | Cite as

Digital reading proficiency in german 15-year olds: evidence from PISA 2012

  • Johannes Naumann
  • Christine Sälzer
Allgemeiner Teil

Abstract

The present study reports results on digital reading proficiency in German 15-year-old secondary students, based on the PISA 2012 computer-based assessment (N = 2785). We report mean performance in digital reading and relation with student background, availability of ICT, use of ICT, and attitudes towards ICT. With a mean score 494 points on the PISA scale, German students did not perform significantly different from the OECD average. However, their digital reading proficiency lagged significantly behind their print reading proficiency. A regression model with student background variables gender, immigrant status, and socio-economic status combined explained 13% of digital reading variance. ICT availability, use of ICT, and attitudes towards ICT combined explained 16% of variance. A regression model combining both student background and ICT availability, attitudes, and use explained 23% of variance. ICT availability and use had inversely u‑shaped relationship associations with digital reading proficiency.

Keywords

Digital reading Hypertext PISA 2012 ICT use Computer-related attitudes 

Kompetenzen 15-jähriger Schülerinnen und Schüler beim Lesen digitaler Texte in Deutschland: Befunde aus PISA 2012

Zusammenfassung

Die vorliegende Studie berichtet erstmals die Ergebnisse zum Lesen digitaler Texte aus dem computerbasierten Teil des PISA-Tests 2012 für Schülerinnen und Schüler in Deutschland (N = 2785) im Hinblick auf Performanz und Zusammenhänge mit soziodemographischen Variablen sowie Zugang zu Informations- und Kommunikationstechnologien (ICT), ICT-Nutzung und ICT-bezogenen Einstellungen. Im Mittel erreichten deutsche 15-jährige im Lesen Digitaler Texte 494 Punkte auf der PISA-Skala. Die Performanz deutscher 15‑jähriger beim Lesen digitaler Texte liegt damit im Bereich des OECD-Durchschnitts und ist signifikant niedriger als die Performanz deutscher 15-jähriger beim Lesen gedruckter Texte. Ein Regressionsmodell mit den soziodemographischen Prädiktoren Geschlecht, Migrationsstatus und sozioökonomischer Status und den ICT-bezogenen Variablen Zugang zu ICT, ICT-Nutzung und ICT-bezogene Einstellungen erklärte 23 % der Varianz im Lesen digitaler Texte. Soziodemographische Variablen allein erklärten 13 % und ICT-bezogene Variablen erklärten 16 % der Varianz. Der Zusammenhang der Performanz beim Lesen digitaler Texte mit Zugang zu ICT und Nutzung von ICT war dabei umgekehrt U‑förmig.

Schlüsselwörter

Lesekompetenz Hypertext Lesen Digitaler Texte PISA 2012 Nutzung von Informations- und Kommunikationstechnologie Computerbezogene Einstellungen 

References

  1. Afflerbach, P., & Cho, B.-Y. (2008). Identifying and describing constructively responsive comprehension strategies in new and traditional forms of reading. In S. E. Israel & G. G. Duffy (Eds.), Handbook of research on reading comprehension (pp. 69–90). New York: Routledge.Google Scholar
  2. Alloway, T. P., & Alloway, R. G. (2012). The impact of engagement with social networking sites (SNSs) on cognitive skills. Computers in Human Behavior, 28, 1748–1754.CrossRefGoogle Scholar
  3. Artelt, C., Naumann, J., & Schneider, W. (2010). Lesemotivation und Lernstrategien. In E. Klieme, C. Artelt, J. Hartig, N. Jude, O. Köller, M. Prenzel, W. Schneider, & P. Stanat (Eds.), PISA 2009. Bilanz nach einem Jahrzehnt (pp. 73–112). Münster: Waxmann.Google Scholar
  4. Brand-Gruwel, S., Wopereis, I., & Walraven, A. (2009). A descriptive model of information problem solving while using Internet. Computers & Education, 53, 1207–1217.CrossRefGoogle Scholar
  5. Brock, D. B., & Sulsky, L. M. (1994). Attitudes toward computers: construct validation and relations to computer use. Journal of Organizational Behavior, 15, 17–35.CrossRefGoogle Scholar
  6. Cacioppo, J. T., & Berntson, G. C. (1994). Relatshionship between attitudes and evaluative space: a critical review, with emphasis on the seperability of positive and negative substrates. Psychological Bulletin, 115, 401–423.CrossRefGoogle Scholar
  7. Eickelmann, B., Schaumburg, H., Drossel, K., & Lorenz, R. (2014). Schulische Nutzung von neuen Technologien in Deutschland im internationalen Vergleich. In W. Bos, B. Eickelmann, J. Gerick, F. Goldhammer, H. Schaumburg, K. Schwippert, M. Senkbeil, R. Schulz-Zander, & H. Wendt (Eds.), ICILS 2013. Computer- und informationsbezogene Kompetenzen von Schülerinnen und Schülern der 8. Jahrgangsstufe im internationalen Vergleich (pp. 197–230). Münster: Waxmann.Google Scholar
  8. 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. Cham: Springer.CrossRefGoogle Scholar
  9. Goldhammer, F., Naumann, J., & Keßel, Y. (2013). Assessing individual differences in basic computer skills: psychometric characteristics of an interactive performance measure. European Journal of Psychological Assessment, 29, 263–275.CrossRefGoogle Scholar
  10. Guo, Y., Sun, S., Breit-Smith, A., Morrison, F. J., & Connor, C. M. (2015). Behavioral engagement and reading achievement in elementary-school-age children: a longitudinal cross-lagged analysis. Journal of Educational Psychology, 107, 332–347.CrossRefGoogle Scholar
  11. Hahnel, C., Goldhammer, F., Naumann, J., & Kröhne, U. (2016). Effects of linear reading, basic computer skills, evaluating online information, and navigation on reading digital text. Computers in Human Behavior, 55, 486–500.CrossRefGoogle Scholar
  12. Junco, R. (2012a). Too much face and not enough books: the relationship between multiple indices of Facebook use and academic performance. Computers in Human Behavior, 28, 187–198.CrossRefGoogle Scholar
  13. Junco, R. (2012b). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers & Education, 58, 162–171.CrossRefGoogle Scholar
  14. Keith, N., Richter, T., & Naumann, J. (2010). Active/exploratory training promotes transfer even in learners with low motivation and cognitive ability. Applied Psychology: An International Review, 59, 97–123.CrossRefGoogle Scholar
  15. Kintsch, W. (1998). Comprehension: a paradigm for cognition. Cambridge: Cambridge University Press.Google Scholar
  16. Lawless, K. A., & Schrader, P. G. (2008). Where do we go now? Understanding research on navigation in complex digital environments. In D. J. Leu & J. Coiro (Eds.), Handbook of new literacies (pp. 267–296). Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  17. Lee, Y.-H., & Wu, J.-Y. (2013). The indirect effects of online social entertainment and information seeking activities on reading literacy. Computers & Education, 67, 168–177.Google Scholar
  18. Leu, D. J., Kinzer, C. K., Coiro, J. L., & Cammack, D. W. (2004). Toward a theory of new literacies emerging from the Internet and other information and communication technologies. In N. J. Unrau & R. B. Ruddell (Eds.), Theoretical models and processes of reading (5th edition, pp. 1570–1613). Newark: International Reading Association.Google Scholar
  19. Mullis, I. V. S., Martin, M. O., Foy, P., & Drucker, K. T. (2012). PIRLS 2011 International results in reading. Chestnut: Boston College.Google Scholar
  20. Naumann, J. (2015). A model of online reading engagement: linking engagement, navigation, and performance in digital reading. Computers in Human Behavior, 53, 263–277.CrossRefGoogle Scholar
  21. Naumann, J., & Salmerón, L. (2016). Does navigation always predict performance? Effects of navigation on digital reading are moderated by comprehension skills. The International Review of Research in Open and Distributed Learning, 17(1), 42–59.CrossRefGoogle Scholar
  22. Naumann, J., Richter, T., & Groeben, N. (2001). Validierung des Inventars zur Computerbildung (INCOBI) anhand eines Vergleichs von Anwendungsexperten und Anwendungsnovizen (Validation of the Computer Literacy Inventory through a comparison between expert and novice computer users). Zeitschrift für Pädagogische Psychologie, 15, 219–232.CrossRefGoogle Scholar
  23. Naumann, J., Elson, M., & Rauch, D. P. (2016). Explaining performance gaps between native and immigrant students through group-specific navigation behavior. Paper presented at the AERA Annual Meeting, Washington, DC.Google Scholar
  24. OECD. (2002). Education at a glance: glossary. Paris: OECD.Google Scholar
  25. OECD. (2010). PISA 2009 assessment framwork: Key competencies in reading, mathematics and science. Paris: OECD.Google Scholar
  26. OECD. (2011a). PISA 2009 results: Students on line. Digital technologies and performance (Volume VI). Paris: OECD.Google Scholar
  27. OECD. (2011b). Information and communication technology familiarity questionnaire for PISA 2012. Paris: OECD. Retrieved from http://www.oecd.org/pisa/pisaproducts/PISA12_ICT_ENG.pdf. Accessed: 06. June 2016.Google Scholar
  28. OECD. (2013). PISA 2012 assessment and analytical framework: mathematics, reading, science, problem solving and financial literacy. Paris: OECD.CrossRefGoogle Scholar
  29. OECD. (2014a). PISA 2012 results: what students know and can do – student performance in mathematics, reading and science (volume I, revised edition). Paris: OECD.Google Scholar
  30. OECD. (2014b). PISA 2012 technical report. Paris: OECD.Google Scholar
  31. OECD. (2014c). PISA 2012 results: creative problem solving. Paris: OECD.CrossRefGoogle Scholar
  32. OECD. (2015). Students, computers and learning. Making the connection. Paris: OECD.CrossRefGoogle Scholar
  33. Perfetti, C. A. (1994). Psycholinguistics and reading ability. In M. A. Gernsbacher (Ed.), Handbook of psycholinguistics (pp. 849–894). San Diego: Academic Press.Google Scholar
  34. Pfost, M., Dörfler, T., & Artelt, C. (2013). Students’ extracurricular reading behavior and the development of vocabulary and reading comprehension. Learning and Individual Differences, 26, 89–102.CrossRefGoogle Scholar
  35. R Development Core Team (2015). R: A language and environment for statistical computing (Computer program). Vienna: R Foundation for Statistical Computing. https://www.R-project.org/. Accessed: 08. September 2015.Google Scholar
  36. Richter, T., Naumann, J., & Groeben, N. (2000). Attitudes toward the computer: construct validation of an instrument with scales differentiated by content. Computers in Human Behavior, 16, 473–491.CrossRefGoogle Scholar
  37. Richter, T., Naumann, J., & Groeben, N. (2001). Das Inventar zur Computerbildung (INCOBI): Ein Instrument zur Erfassung von Computer Literacy und computerbezogenen Einstellungen bei Studierenden der Geistes- und Sozialwissenschaften. Psychologie in Erziehung und Unterricht, 48, 1–13.Google Scholar
  38. Richter, T., Naumann, J., & Horz, H. (2010). Eine revidierte Fassung des Inventars zur Computerbildung (INCOBI-R) (A revised version of the Computer Literacy Inventory). Zeitschrift für Pädagogische Psychologie, 24, 23–37.CrossRefGoogle Scholar
  39. Robinson, J. P., & Lubienski, S. T. (2011). The development of gender achievement gaps in mathematics and reading during elementary and middle school examining direct cognitive assessments and teacher ratings. American Educational Research Journal, 48, 268–302.CrossRefGoogle Scholar
  40. Robitzsch, A. (2015). BIFIE survey (R package, version 1.7).  http://CRAN.R-project.org/package=BIFIEsurvy. Accessed: 10th September 2015.Google Scholar
  41. Salmerón, L., Cañas, J. J., Kintsch, W. J., & Fajardo, I. (2005). Reading strategies and hypertext comprehension. Discourse Processes, 40, 171–191.CrossRefGoogle Scholar
  42. Schulz-Zander, R., Eickelmann, B., & Goy, M. (2010). Mediennutzung, Medieneinsatz und Lesekompetenz. In W. Bos, S. Hornberg, K.-H. Arnold, G. Faust, L. Fried, E.-M. Lankes, K. Schwippert, I. Tarelli & R. Valtin (Eds.), IGLU 2006 – Die Grundschule auf dem Prüfstand. Vertiefende Analysen zu Rahmenbedingungen des schulischen Lernens (pp. 91–120). Münster: Waxmann.Google Scholar
  43. Stanat, P., & Christensen, G. (2006). Where immigrant students succeed. A comparative review of performance and engagement in PISA 2003. Paris: OECD.Google Scholar
  44. Stanat, P., Rauch, D., & Segeritz, M. (2010). Schülerinnen und Schüler mit Migrationshintergrund. In E. Klieme, C. Artelt, J. Hartig, N. Jude, O. Köller, M. Prenzel, W. Schneider & P. Stanat (Eds.), PISA 2009. Bilanz nach einem Jahrzehnt (pp. 200–230). Münster: Waxmann.Google Scholar
  45. Stanovich, K. E. (1986). Matthew effects in reading: some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, 360–407.CrossRefGoogle Scholar
  46. Voss, A. (2006). Print- und Hypertext-Lesekompetenz im Vergleich. Eine Untersuchung von Leistungsdaten aus der Internationalen Grundschul-Leseuntersuchung (IGLU) und der Ergänzungsstudie Lesen am Computer (LaC). Münster: Waxmann.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden 2017

Authors and Affiliations

  1. 1.Fachbereich ErziehungswissenschaftenGoethe University FrankfurtFrankfurt am MainGermany
  2. 2.Technical University MunichMunichGermany

Personalised recommendations