The Investigation of the Structural Relationship among Factors That Affect Digital Reading
Author(s):
Hye-young Park (presenting / submitting) Jong-yun Kim Soo-kyoung Son
Conference:
ECER 2017
Format:
Paper

Session Information

09 SES 02 A, Investigation Factors that Affect ICT-Competencies

Paper Session

Time:
2017-08-22
15:15-16:45
Room:
W5.13
Chair:
Ramona Lorenz

Contribution

ICT have changed the way text is presented and received by readers, which can affect their comprehension of the text and their learning(Coiro, 2011; Leu, Kinzer, Coiro, & Cammack, 2004; Naumann, 2010; Rouet, 2006). A growing body of research on reading has been evolved and accounted for the complex nature of reading. Thus, we understand more diverse and multifaceted aspects of reading than the past (Huey, 1908; Pressley & Afflerbach, 1995; van Dijk & Kintsch, 1983).

Purpose of research

The current study seeks to gain insights into predictors involved in the comprehension process of digital text. PISA conducted a Digital Reading Assessment in 2009 and 2012 to examine students’ digital reading achievement by comparing the changing reading environments of the digital age according to participating countries. Our research analyzed the PISA 2012 DRA results (i.e., score & navigation indices) and questionnaires (i.e. student questionnaire & ICT familiarity). Through this we were able to discover the main variables affecting Korean students’ digital reading and investigate the structural relation between each of the variables.

Research Questions

  1. What factors (e.g., ICT factors, attitude, and navigation indices) affect Korean students’ digital reading assessment scores in PISA 2012 DRA?
  2. What structural relationships were identified among the PISA 2012 DRA factors (e.g., ICT factors, attitude, and navigation indices)?

Literature review

Use of ICT

The accessibility and frequency of ICT within the family affects the student’s digital skill. (Kuhlemeier & Hemker, 2007). Empirical research has revealed that basic computer skills has a strong positive relationship with digital reading and accounts for 38% of the variance in digital reading (i.e. for the German sample in PISA 2009 field trial). This is a significant predictor for digital reading (Goldhammer, Naumann, et al, 2013; Naumann, 2010). The range in accessibility and frequency of utilizing ICT in school greatly differed according to each school (Hohlfeld et al., 2008), and these differences influences the child’s ICT and academic achievement (Gamboa & García-Suaza, 2011).

Navigation

Navigation in digital reading is referred to a reader’s movement through the pages of a hypertext system(Lawless & Schrader, 2008). Navigation reflects how readers access digital text parts and arrange their order to gain information so that readers can create their own text base by their selection and sequencing of pages(cf., Kintsch, 1998). So, effective navigation is assumed to be an important predictor of hypertext comprehension. It has been shown that internet navigation strategy has become a key cognitive factor in determining the success of hypertext and reading in a digital environment. (Lawless & Kulikowich, 1996; Salmerón & García, 2011).

ICT attitude

Some research suggests students’
attitude toward digital devices and digital literacy may be a factor in
predicting digital reading ability. There is a connection between the
motivation or attitude in print-based reading with the amount of reading and
how it affects reading achievement. (Petscher, 2010; Wang & Guthrie, 2004).
This suggests that there is a high correlation between affective and cognitive
achievement. Though there is not much evidence in the context of the digital
environment, we can make positive predictions that similar connections between
the non-cognitive and digital reading achievement do exist (Allen et al.,
2013).

Method

2. Research Method PISA 2012 DRA score was analyzed as a dependent variable and webpage navigation index, questionnaire results (student questionnaire, ICT familiarity) were analyzed as independent variables in the current study. Gender and ESCS were controlled. Table 1. Number of sample and estimated population Type Korea Number of sample Boys 1,251 Girls 1,424 Total 2,675 Number of estimated population Boys 150,375 Girls 171,420 Total 321,795 Table 2. Independent variables *ICT usage at home for school: 7 items with 5-point rating scale(1=Never or hardly ever, 5=Every day) such as Browsing the Internet for schoolwork Using email for communication with other students about schoolwork Using email for communication with teachers and submission of homework or other schoolwork Three measurement variables were used for item parceling *ICT usage at school : 9 items with 5-point rating scale(1=Never or hardly ever, 5=Every day) such as at school, Using email at school, Browsing the Internet for schoolwork *ICT usage for entertainment: 11 items with 5-point rating scale(1=Never or hardly ever, 5=Every day) such as - Playing one-player games - Playing collaborative online games - Using email Four measurement variables were used for item parceling * Attitudes toward the use of ICT for learning:3 items with 4-point rating scale(1=Strongly agree, 4=Strongly disagree) such as The computer is a very useful tool for my schoolwork Doing my homework using a computer makes it more fun The Internet is a great resource for obtaining information I can use for my school work *Webpage navigation: Number of pages visited : student comprehension in DR Number of relevant pages visited : student comprehension in DR Number of visits to relevant pages : student engagement in DR Number of page visits : student engagement in DR Analysis model: Structural Equation Model (SEM) Data analysis procedures Characteristics of PISA 2012 data: Provided plausible value of the 5 sets by replacing matrix sampling design that all students did not take every item and the missing value with regard to the achievement result by area. Therefore, this study analyze the plausible value of the 5 sets using the DATA =IMPUTATION option on Mplus 7.4 Program. Navigation indices are centered. To account for possible effects of test composition and the order of cluster presentation on navigation, the navigation indices are centered on the test forms and countries (OECD, 2011). Student weight variable(w_fstuwt) was used for secured representative of the population(OECD, 2014). Mediation effect such as bootstrapping method was applied (Bollen, Stine, 1990; Mallincrodt et al., 2006).

Expected Outcomes

After controlling gender and ESCS, ICT usage at home for school had a significant positive impact on attitude towards ICT, whereas ICT usage for entertainment had a great negative impact on students. ICT usage at school had a negative impact on navigation. Attitude was not affected at all towards navigation. ICT usage at home for school, attitude, webpage navigation had a notably positive impact on DRA. On the other hand, ICT usage at school and ICT usage for entertainment had a considerable negative impact. Table 3 shows the goodness of fit in the current SEM model. Also, Figure 2 and table 4 describes the analysis results. With regarding to the mediation effect, ICT usage at home for school caused a significant increase in positive attitude towards ICT. Using this as a medium had positive effects on DRA achievement. ICT usage at home for school had positive effects on attitude towards ICT. Not only that, but navigation as a medium had a positive influence on achievement. ICT usage at school had a significant negative influence on navigation and using this as a medium also resulted in negative effects on achievement. In conclusion, we empirically discovered that ICT usage, attitude toward ICT, webpage navigation and digital reading achievement were interconnected to each other. Also, attitude toward ICT mediates between ICT usage and webpage navigation skills. Additionally, webpage navigation skills mediate between a positive attitude toward ICT and digital reading achievement. Based on these empirical evidences, a deliberate and thoughtful educational approach and policy should be made so as to enhance students’ digital reading competencies.

References

Afflerbach, P., Cho, B.-Y., & Kim, J-Y. (2014). Inaccuracy and reading in multiple text and Internet/hypertext environments. In D. Rapp & J. Braasch (Eds.), Processing inaccurate information: Theoretical and applied perspectives from cognitive science and the educational sciences (pp. 403-424). Cambridge, MA: MIT Press. Coiro, J. (2011). Predicting reading comprehension on the internet contributions of offline reading skills, online reading skills, and prior knowledge. Journal of Literacy Research, 43(4), 352-392. Foltz, P. W. (1996). Comprehension, coherence, and strategies in hypertext and linear text. In J. F. Rouet,J. J. Levonen, A. Dillon, & R. J. Spiro (Eds.), Hypertext and cognition (pp. 109–136). Mahwah, NJ:Lawrence Erlbaum. Gamboa, L.; García, A.; (2011). Access to computer and academic achievement. Where is it best: at home or at school?. Serie CEDE de Textos para Discussão No 47 (Discussion Paper No. 47). Universidade federal Fluminence TD047. Kintsch, W. (1998). Comprehension: A paradigm for cognition. NY: Cambridge University Press. Kuhlemeier, H., & Hemker, B. (2007). The impact of computer use at home on students’ Internet skills. Computers & Education, 49(2), 460-480. Leu, D. J., Jr. 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 R. B. Ruddell & N. Unrau (Eds.), Theoretical models and processes of reading (5th., pp. 1570-1613). Newark, DE: International Reading Association. Naumann, J. (2010, May). Predicting comprehension of electronic Reading tasks: the impact of computer skills and Reading literacy. In Paper presented at the annual conference of the American educational research association (AERA) (Denver, CO). OECD (2013), PISA 2012 Assessment and Analytical Framework: Mathematics, Reading, Science, Problem Solving and Financial Literacy. OECD Publishing. Petscher, Y. (2010). A meta-analysis of the relationship between student attitudes towards reading and achievement in reading. Journal of Research in Reading, 33(4), 335-355. Rouet, J. F. (2006). The skills of document use: From text comprehension to Web-based learning. NY: Routledge.Psychology Press. Salmerón, L., & García, V. (2011). Reading skills and children’s navigation strategies in hypertext. Computers in Human Behavior, 27(3), 1143-1151. van Dijk, T. A., & Kintsch, W. (1983). Strategies of discourse comprehension. New York: Academic Press. Wang, J. H. Y., & Guthrie, J. T. (2004). Modeling the effects of intrinsic motivation, extrinsic motivation, amount of reading, and past reading achievement on text comprehension between US and Chinese students. Reading Research Quarterly, 39(2), 162-186

Author Information

Hye-young Park (presenting / submitting)
Korea Institute for Curriculum and Evaluation
Global Education
Seoul
Korea Institute for Curriculum and Evaluation, Korea, Republic of (South Korea)
Korea University

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