A model of online reading engagement: Linking engagement, navigation, and performance in digital reading

Abstract

a b s t r a c tA model of online reading engagement is outlined. This model proposes that online reading engagement predicts dedication in digital reading. Dedication in digital reading according to the model is reflected in task-adaptive navigation, and task-adaptive navigation predicts digital reading performance over and above print reading skill. Information engagement is assumed to positively predict task-adaptive navigation, while social engagement is assumed to negatively predict task-adaptive navigation. These hypotheses were tested using OECD PISA 2009 Digital Reading Assessment data from 17 countries and economies (N = 29,395). Individual task responses served as the primary unit of analysis. Linear mixed models were used to predict navigation behavior from the interaction of information and social online reading engagement with navigation demands. High information engagement was associated with more task-adaptive navigation behavior, as shown by significant positive interactions between information engagement and tasks' navigation demands. In contrast, high social engagement was associated with less adaptive navigation behavior, as shown by negative interactions between social engagement and navigation demands. Generalized linear mixed models were used to predict task performance by the interaction of navigation demands and navigation behavior. Adaptive navigation behavior predicted digital reading task performance, as shown by significant interactions between navigation behavior and navigation demands. These results are in support of the proposed model of online reading engagement.

Leisure Reading (But Not Any Kind) and Reading Comprehension Support Each Other—A Longitudinal Study Across Grades 1 and 9

“…Research on digital reading is constantly developing and future studies may be able to examine in more detail the associations between leisure reading genres and both digital and print reading skills. For example, a focus on information content as opposed to social participation likely results in different reading strategies (Naumann, ). In sum, our results suggest that for comprehension of continuous printed text, book reading was a superior predictor, whereas the early onset of digital text consumption (possibly at the expense of print reading) had a negative association with print reading skills.…”

“…It seems justifiable to conclude that time spent on digital reading can be a mixed blessing. Skilled navigation and source evaluation necessarily require good comprehension skills (e.g., Naumann, ) but the evidence suggests an asymmetry in that competent reading of linear printed text is a strong facilitator of competent digital reading but not vice versa (Hahnel, Goldhammer, Kröhne, & Naumann, ; Hahnel, Goldhammer, Naumann, & Kröhne, ; Naumann & Salmerón, ). It is plausible that intensive reading of superficial digital material instead of print reading is likely associated with comprehension problems and may even augment them.…”

The Relation Between ICT and Science in PISA 2015 for Bulgarian and Finnish Students

“…An explanation for the association between ICT use and availability with lower science scores is that perhaps students are being distracted by the ICT in terms of the types of activities performed or the time taken to engage in extracurricular activities at the expense of academic learning. It could also be that they may be using technology for its own sake, rather than to support and uplift their learning (Martin-Perpiñá, Viñas i Poch, & Malo Cerrato, 2019;Naumann, 2015). Teaching for innovation is an intentional process and teacher preparation cannot be compensated by a technologically-rich learning environment.…”

Reading digital text involves working memory updating based on task characteristics and reader behavior

“…In general, readers are sensitive to demands of reading tasks that influence the way of their cognitive information processing (cf. Kendeou, van den Broek, Helder & Karlsson, 2014;Naumann, 2015;McCrudden & Schraw, 2007;Rouet, 2006). Such demands are often described as sources of cognitive load in WM (DeStefano & LeFevre, 2007;Rouet, 2009;Scheiter et al, 2009).…”