Skip to main content
NCBI home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2023 Mar 25:1–22. Online ahead of print. doi: 10.1007/s11145-023-10420-8

What explains children’s digital word reading performance in L2?

Jana Chi-San Ho 1, Catherine McBride 1,✉, Kelvin Fai Hong Lui 2
PMCID: PMC10039337  PMID: 37359025

Abstract

Word reading fluency is crucial for early L2 development. Moreover, the practice of digital reading has become increasingly common for both children and adults. Therefore, the current study investigated factors that explain digital word reading fluency in English (L2) among Chinese children from Hong Kong. Eighty-six children (age: M = 9.78, SD = 1.42) participated in a digital silent word reading test using a mobile phone, a computer, or a tablet. This is a 10-minute timed test of English word reading. Overall, children’s digital word reading fluency was highly correlated with print word reading fluency, even when measured a year apart. A hierarchical regression model revealed that socio-economic status ( β = .333), grade ( β = .455), and English reading motivation ( β = .375) were positively and uniquely associated with performance in digital reading. These predictors explained 48.6% of the total variance in task performance. Two additional variables, i.e., the type of reading device and extraneous cognitive load, were included as well. Digital word reading fluency was significantly poorer when done using a phone as compared to a computer ( β = -.187). No significant difference was found between reading on a tablet and a computer. Extraneous cognitive load ( β = -.255) negatively and uniquely explained digital word reading fluency as well. Overall, the model explained 58.8% of the total variance. The present study represents the first attempt to highlight a comprehensive set of predictors of digital word reading fluency.

Keywords: Digital reading, English reading, Multiple regression, Digital reading literacy

Due to the rapid advancement of technology, digital technology has been widely applied in various contexts for different purposes. Reading digitally has become part of the 21st century New Literacies highlighted in the learning curriculum as a comprehension skill developed with print reading skills (Hayward, 2019). Digital texts have become a major medium of teaching and learning in most educational institutions because of their advantages over print reading such as portability, affordances, cost and environmental benefits (Sackstein et al., 2015; Wu & Chen, 2018). A survey by Gartner Inc. (2011) showed that people reported spending almost an equal amount of time reading on a screen as reading on paper at that time. The shift to virtual and hybrid learning under COVID-19 has also accelerated the application of digital reading in education. There has been an 80% increase in the usage of digital books in the schools of 71 countries from 2019 to 2020 (OverDrive Education, 2020). Due to the increased prevalence of digital reading in education, it is of primary importance to investigate what factors affect digital reading fluency given how technology can affect the cognitive processes of reading (Wylie et al., 2018).

Researchers (Dillon, 1992; OECD Publishing, 2011) have pointed out that print reading and digital reading may manifest different effects on reading literacy, as the nature of reading has been deeply affected by digital technology. Digital reading changes how texts are produced and displayed, which affects how students read (OECD Publishing, 2011). Most researchers in the past two decades seem to argue that print reading is better than digital reading for fostering reading comprehension because print reading promotes deep reading while digital reading does not and has been identified as a form of surface reading (Carr, 2011; Fitzpatrick, 2013). Yet the affordances that digital technology brings, such as the navigation of texts, still make it an appealing tool for reading. Indeed, in some previous works, digital reading materials were preferred over print reading materials in terms of word recognition and story comprehension (Zipke, 2017).

As the importance of learning English as a second language (ESL) has become increasingly important, students have begun reading English at an early age worldwide. Given the increasing popularity and ubiquity of digital reading in second language learning, it is important to understand what factors affect digital reading fluency in children’s L2 development. This might provide some valuable insights into children’s early literacy development in L2. In this study, we focused on digital reading fluency at the word level as word recognition skills are important for early reading development. Identifying the factors that explain digital word reading fluency can provide insights for incorporating technology for reading and the development of fair digital reading assessments.

Theoretical background

Children’s reading in L2 and the increased popularity of digital assessment

English has become a global language and been widely used in various fields such as education, business, scientific research and diplomacy (Rao, 2019). It has become a compulsory subject in schools in 142 countries (“Countries in”, n.d.). For example, in China, English has become a mandatory subject starting from elementary school as the ability to read English has become a criterion for admissions to many schools and higher education institutes (Qi, 2016). Given the prevalence of English as L2 and its compulsory inclusion in early education, it is useful to understand what factors affect children’s reading fluency in L2.

Meanwhile, there has been a growing trend of reading digitally in educational settings. According to survey data from the 2015 National Assessment of Educational Progress, about 70% of students in 4th-grade classrooms use computers to access reading material in school (Heitin, 2016). Due to the ubiquity of digitalization in learning and reading in our daily lives, many experts have agreed that all students should be learning to read in both a print format and a digital format, even for the very youngest. In recent years, digital technology has been embedded in learning processes, and many international assessments (e.g. PISA, PIRLS) have been digitalized in the past few years (Støle et al., 2018). There are several reasons why digital assessment is becoming increasingly popular. One reason is that digital assessment is becoming less costly than traditional paper-based assessment (Scheuermann & Björnsson, 2009). For example, the scoring can be automatized, which results in a reduction of expenditure on labor. In addition, digital assessment offers plenty of technological advantages that traditional paper-based assessment cannot, such as instant results and accuracy. For example, digital assessment can eliminate the risk of bias from teachers and scoring mistakes due to carelessness. The process of assessment is often faster, and more immediate results can be obtained with digital technology as well. As reading digitally for whatever purposes has become part of our daily lives, it is crucial to understand and assess the new forms of reading literacy that come with digital technology (Coiro, 2009).

Print reading versus digital reading

Print reading is the traditional format of reading where materials are read on paper. Digital reading refers to reading with digital tools on a screen, including tablets, computers, or mobile phones. Digital technology deeply affects the presentation of texts and the flow of reading, and, consequently, affects the nature of reading (OECD Publishing, 2011). Indeed, there are differences in reading in these two modes in relation to reading comprehension, reading speed, and required cognitive resources. Details on some of these differences are discussed in the section below, Evidence of Mode Differences in Reading Performance.

The presentation of texts

Printed reading material and digital reading material each have unique features in how texts are produced, displayed, organized, and connected to other texts. Digital texts differ from printed texts in readability and usability (OECD Publishing, 2011). Although text displays are similar on a superficial level in these two modes (e.g., with margins, paragraphs, and so on), they are vastly different in terms of the physical size of the display area. The size of the display area for printed texts is usually the size of an A4 (8.3 × 11.7 inches) or A5 (5.8 × 8.3 inches) paper whereas the display screens for digital texts can vary considerably, depending on the devices that are used. In general, display screens can vary from a 5-inch mobile phone to a 15-inch computer screen.

The flow of reading

Apart from the differences in the text display area, the navigation of texts is also different in these two reading modes. While printed texts are displayed on static pages, digital texts are displayed in dynamic windows and frames (OECD Publishing, 2011). Printed texts are displayed linearly. It is usually difficult for readers to jump around texts. However, digital texts allow readers to navigate within and across pages of texts via functions like hyperlinks or scroll bars. The nature of digital texts is non-linear. The quick and easy navigation of digital texts has accentuated the interconnections among digital texts. Readers can maneuver around texts while reading digitally, e.g., checking references or finding detailed explanations for given terms.

The affordances of digitalization in reading

Given the ubiquity of digitalization, there has been a debate about the role taken by digital technology in the acquisition of reading as an activity (Fitzpatrick, 2013). Many researchers are interested in the extent to which digitalization has revolutionized the nature of reading and in what aspects digital technology has influenced reading.

The technological affordances of digital technology in reading are abundant. For instance, hyperlinks in digital reading allow readers to navigate their way of encoding information, which traditional print reading cannot. Whether features like this are more beneficial or detrimental to reading performance is still unknown. Nevertheless, many researchers assert that both print reading and digital reading have different pros and cons, and digital reading performance depends on many factors.

To understand the affordances of digital technology in reading, we need to start with understanding the technological frames of digital technology. Just as important as understanding what features are offered by the digital design, technological frames, defined here as how users perceive digital technology, have important implications as well. A technological frame guides how we perceive technology (Taipale, 2014). The affordances of technology are subjective to different individuals and thus lead to different impacts on reading performance (Dourish, 2004; Hutchby, 2013). These unique features determine the possibilities for how an individual reads. For instance, one can write notes when reading on paper, whereas one can navigate across different sections of text with one click on the hyperlinks in digital reading. Undoubtedly, the design of the reading tools contributes to the quality of reading performance. Overwhelmed by the availability of multi-dimensional texts, digital readers are now faced with the challenge of discriminating texts and tend to skim more in digital reading and move across surfaces without deep processing of information. As a result, digital natives “know countless more ‘bits’ of information when compared with previous generations” (Liu, 2012). Based on the analysis of his personal experience of digital reading, Carr (2011) argued that digital reading is more likely to lead to skimming or reading on a surface level, a form of inattention related to internet usage.

Evidence of mode differences in reading performance

Reading comprehension

Since Dillon (1992) pointed out the different impacts on reading between print reading and digital reading, a great deal of research has been conducted to investigate the effects of these two reading modes. Most researchers have advocated for print reading over digital reading because the majority of findings have demonstrated that reading on paper is better than reading on screen for adequate reading comprehension (e.g., Clinton 2019; Delgado et al., 2018; Kong et al., 2018). Only findings from one study by Eno (2011) showed an advantage of digital reading over reading on paper in reading assessments, with a particular sample of high-achieving 10th -grade students. However, these results might not apply to the general population as high-achieving students might have a higher cognitive capacity. Therefore, the cognitive load associated with reading on a computer may have been under the threshold needed by the sample participants to manifest a negative mode effect in digital reading (Eno, 2011).

Reading speed

In addition to reading comprehension, another common indicator of reading literacy is reading speed. In testing this, findings from older research (e.g., Dillon, 1992; Zaphiris & Kurniawan, 2001) indicated that reading on paper is quicker than on digital screens. Zaphiris and Kurniawan (2001) found that print reading speed is 10–30% faster than digital reading speed even when there is no layout difference. On the other hand, findings from recent meta-analyses (e.g., Clinton, 2019; Kong et al., 2018) do not support the advantage of print reading for reading speed; they demonstrate no significant differences between these two reading modes. The advancement of digital technology has been rapid and the number of computers in use worldwide has grown at an exponential rate in the past three decades (OECD Publishing, 2011). It is plausible that the initial disadvantage of digital reading in reading speed was due to the novelty of digital technology for reading as well as the poorer interface and configuration of older digital devices. One study even found that digital reading might accelerate the progress of the reading rate, which might be due to the utilization of e-book features that train the readers to practice their reading speed (Akbar et al., 2015). Nevertheless, it has been argued that a faster reading speed is the result of the trade-off between reading speed and level of comprehension. Longer reading time might indicate increased processing (Rapp & van den Broek, 2005) and increased effort (Chen & Catrambone, 2015) in reading comprehension.

Word reading fluency

Word reading fluency is one of two essential components of reading. Both word reading fluency and language comprehension must be achieved for successful reading comprehension (Gough & Tunmer, 1986; Murray, 2016). Thus, the ability to read words and to understand their meanings within texts are prerequisites to reading comprehension (Davis, 2006). Existing studies have demonstrated the positive effects of digitalization on word reading performance (Arnold & Reed, 2019; Mechling et al., 2008; Zipke, 2017) found that preschoolers’ word recognition skills were significantly higher when reading with a digital storybook than with a comparable print book. Students also reported that they preferred digital reading over print reading material. Another study conducted by Mechling et al. (2008) also revealed an advantage of using a smartboard to promote observation learning in sight word recognition. Students in that study read a greater percentage of observational words when words were presented digitally.

Factors affecting digital reading

As word reading fluency is crucial for early L2 development and research in the area of digital word reading is scarce, we aimed to investigate digital word reading fluency in a comprehensive approach by examining variables related to demographics, print reading and digital reading. Many factors can contribute to individual reading abilities. In the present study, we focused on the key factors that predict reading performance in general and the factors that have been shown to influence digital reading more specifically. Besides demographic variables (i.e., gender and school grade), SES and reading motivation were added to the first block of the hierarchical model in this study as they have been widely investigated in research studies related to reading or L2 reading performance (Fry, 1970; Ling, 2004; Liu et al., 2016; Pirih, 2015). Both SES and reading motivation are moderately linked with reading proficiency in previous studies (Netten et al., 2016; Olshtain et al., 1990). In the second block of the regression model, cognitive resources and the type of reading device were further investigated they were found to be significantly associated with digital reading in many studies (Cheng et al., 2014; Srivastava & Gray, 2012). Different reading devices have distinct screen sizes and text presentation types, which affect the cognitive load for reading. Thus, these two variables were further investigated on top of the aforementioned variables to fully understand what factors influence digital reading fluency.

SES

SES is an economic and sociological combined total measure of a family’s income from work and education level. It is routinely linked with children’s early language development (e.g. Fernald et al., 2013; Pan et al., 2017) and L2 proficiency. In a study conducted on Chinese kindergarteners, participants were asked to read aloud a list of English words. Hierarchical regression analyses revealed that SES explained unique variance in L2 English word reading even after controlling for age, phonological processing, vocabulary, and working memory (Liu et al., 2016). The total effect of SES on word reading in English (L2) in that study was medium, β = 0.36, p < .001. SES also plays an important role in early reading literacy. For example, the SES of families is positively associated with adolescents’ reading proficiency in PISA results (OECD Publishing, 2011), in both print reading and digital reading assessments. Though SES has a strong impact on one’s reading literacy development, its effects have not been further compared in different reading modes. In this study, we investigated the effects of SES in both print reading and digital reading. People from socio-economically advantaged backgrounds might have higher levels of computer and Internet access at home, resulting in higher digital reading proficiency.

Gender

There is some suggestion of a gender gap in print reading literacy in favor of girls (e.g., Marks, 2008). Nevertheless, this pattern has been less pronounced in some recent findings in digital reading literacy. For instance, in the PISA reading assessments (which measure reading comprehension), Wu (2014) found that girls performed better in printed reading assessments but were not significantly better on electronic reading assessments. Researchers have not reached a consensus about whether girls or boys have an advantage in digital reading performance. On the one hand, some researchers (Eno, 2011; Hamhuis et al., 2020; Martin & Binkley, 2009) have argued that gender differences in digital reading performance are similar to those in print reading performance. That is, girls tend to perform somewhat better than boys in digital reading tests. This pattern was found in digital reading on computers (Eno, 2011) and tablets (Hamhuis et al., 2020). On the other hand, other researchers have argued the opposite, claiming that boys perform better in digital reading tests (Jerrim, 2016), and this effect is mediated by the amount of time spent on playing computer games (Rasmusson & Åberg-Bengtsson, 2015). The inconsistency of existing findings has caused an ongoing debate as to whether there is an interactive effect between the type of assessment and gender (OECD Publishing, 2011; Støle et al., 2018).

Reading motivation

Reading motivation is a consistently significant predictor of student reading performance. A review conducted by Morgan and Fuchs (2007) demonstrated a moderate correlation between children’s reading motivation and reading skills. Evidence shows that reading motivation has an impact on reading fluency among elementary students as students with higher motivation tend to spend more time on reading which is closely linked with reading achievement (Quirk, 2005). Reading motivation is a key factor in developing reading skills. Data from the Progress in International Reading Literacy Study (PIRLS) in 2006, which is one of the most important large-scale studies of reading literacy among elementary students around the world, revealed that reading motivation was a strong predictor of reading performance in elementary students even after other literacy-related variables were statistically controlled (Mullis et al., 2007). Reading motivation in that study explained 17% of the variance in reading performance. Both intrinsic (Kavanagh, 2019) and extrinsic reading motivation (Yang et al., 2018) have been found to be positively associated with reading achievement of elementary students. Reading motivation also plays an important role in reading achievement across grade levels and cultures (Barber & Klauda, 2020) and promotes reading engagement, which leads to reading achievement (Guthrie & Klauda, 2015). Similarly, in the context of L2 reading, many studies have demonstrated that reading motivation was positively associated with reading comprehension in L2 (e.g., Dhanapala & Hirakawa, 2016). For example, Lin et al. (2012) found that reading motivation was positively associated with elementary students’ reading comprehension in both L1 and L2 and explained 16% variance in L1 Chinese and 12% in L2 English comprehension respectively. In terms of digital reading in L2, differences in reading motivation were found between digital reading and print reading. Pan and Huang (2009) found that both reading comprehension and motivation of the EFL (English as a Foreign Language) freshmen were better when students read digitally than on printed materials. Given that motivation is a key predictor of reading achievement in the contexts of L2 and EFL, more research examining its effect on digital reading fluency is called for.

Cognitive resources

Every individual has a limited amount of cognitive resources in mental processing with which to construct schemas and deal with information. ‘Cognitive load’ refers to the total amount of mental effort that is used in one’s working memory during information processing. According to Sweller’s cognitive load theory (1994), cognitive load can be further differentiated into three types, namely, intrinsic, extraneous, and germane. Intrinsic load is caused by the individual’s perception of the internal complexity of the learning materials; this depends on the nature of the tasks and is often difficult for instructors to manipulate. It is determined by one’s perceived difficulty in the learning task. Extraneous load refers to the processes that are not necessary but are required for the learning processes and is related to the design of the tasks. It is mainly determined by the instructional design. Poor designs, for instance, disorganized material that contains irrelevant information, can increase extraneous cognitive load for students as they learn the materials. Germane load is associated with effortful cognitive activities such as schemas that lead to the construction of knowledge. It can be influenced by the learners’ information processes as well as the instructional design. This theory of cognitive load is used in instructional design to manage the cognitive load in order to maximize the learning outcomes. While instructional design cannot affect intrinsic load as it is determined by the inherent difficulty of the material itself, which is heavily influenced by an individual’s prior knowledge of the topic, extraneous cognitive load results from the design of learning materials. The goal of instructional design should be to decrease extraneous load while increasing germane load.

In the context of reading, drawing from cognitive load theory, a reader’s extraneous load is related to how texts are presented. Existing research has not explicitly examined the relationship between digital reading and the reader’s extraneous load. To what extent extraneous cognitive load affects reading performance and what types of digital designs are more favorable in digital reading remains unclear. Most researchers agree that digital reading requires more cognitive resources than print reading as digital readers need extra mental effort for proper navigation of the non-linear structured text without getting lost (Coiro, 2011; Gyselinck et al., 2008; Srivastava & Gray, 2012). Other factors that might contribute to the extra burdens on cognitive resources are the need to scroll and the inability to annotate (Paek, 2005). For this reason, we tested extraneous load only in the present study.

The type of reading devices

As mentioned earlier, different digital technology offers different affordances in reading. Thus, to fully understand the different effects brought by reading on paper versus reading on a screen, researchers should also investigate specific types of devices for digital reading and their effects on digital reading. In different contexts, this issue is crucial. For example, in some rural areas in the world, most adults have access to a mobile phone rather than a tablet or a computer. A large-scale survey across India in 2019 demonstrated an overwhelming majority of rural Indians used mobile phones to access the Internet while only 2% used a computer or a tablet to go online (Basuroy, 2019). Due to the advancement of mobile technology, reading on mobile devices such as tablets and smartphones has become increasingly common.

In a study of college students, Chen et al. (2014) investigated the effects of reading comprehension across paper, computers, and tablets in college students and reported that the scores of the tablet group were higher than those of both the computer and paper groups, implying that the different digital devices have different effects on reading comprehension. Participants in the paper-and-pencil condition scored significantly higher on the shallow level reading comprehension than those in the computer condition as the navigation of digital reading is more difficult than print reading. It was believed that scrolling on a digital screen led to spatial instability and consequently hampered the reader’s reading comprehension (Mangen et al., 2013). No significant differences among conditions were found in deep-level reading comprehension as the task was too difficult for participants. Although this study targeted college students, the effect size of the differences among different reading devices may vary across different age groups due to the familiarity with information communication technology. The discrepancy in digital reading and print reading likely diminishes in younger generations when they grow up with more advanced technology.

Research gap and objectives

While the majority of current research studies have identified print reading as preferable to digital reading for reading comprehension, many researchers also agree that they both have their own merits and can be equally effective when used properly or used in certain situations. As learning with digital texts and digital assessments is becoming increasingly popular, the present study aimed to investigate a comprehensive set of predictors and to what extent they contribute to digital word reading fluency, with a focus on the effects of extraneous cognitive load and the type of reading devices as these two variables are associated with digital reading (Chen et al., 2014; Paek, 2005).

As pointed out by Chen et al. (2014), advanced reading tasks may not be effective in investigating the effects of digital reading when they are too difficult for the participants. Differences of the effects induced by different reading devices may not be detected when the reading task is too advanced. Thus, the present study adopted a word reading task, which is a lower-level form of processing in reading and was, therefore, deemed more appropriate for children as participants.

To avoid practice and retest effects, given that our print and digital reading assessments were identical in word content, the digital reading assessment was performed one year after the print reading assessment. As a consequence, the results of the two tests cannot be directly compared due to the one-year gap. To investigate the effects of the aforementioned predictors on children’s word reading performance in L2, two multiple regression models were performed for digital and print reading, respectively. First, the effects of individual differences in grade, gender, SES and English reading motivation were investigated in print and digital word reading fluency. Additionally, the effects of extraneous cognitive load and the type of reading devices were further investigated in digital reading. Our primary research questions were as follows:

  1. How do children’s grade, gender, SES and English reading motivation explain individual differences in print reading and digital word reading fluency, respectively?

  2. How do extraneous cognitive load and the type of reading devices explain children’s digital word reading fluency after controlling for grade, gender, SES and English reading motivation?

Methods

Participants

This research was a part of a larger project that aims to investigate children’s literacy development. Eighty-six Chinese participants (46.5% female; age: M = 9.78, SD = 1.42) were recruited from various elementary schools in Hong Kong SAR. Around 90.7% of the participants were from Hong Kong SAR and the rest were from Mainland China, Taiwan, China and Honduras. All spoke Chinese as a first language. Participants were administered the print reading test in 2019 and the digital reading test in late 2020. The majority of the participants used a tablet to finish the digital reading test (48.8%), followed by using a computer (43.0%) and a smartphone (8.1%).

Measures

Outcomes variables

Print word reading fluency and digital word reading fluency

Both print and digital word reading fluency scores were quantified as the numbers of words that were identified by participants in a 10-minute silent word reading test. The test came from Kalindi et al. (2015). It is a silent word reading test. This test was subsequently digitized to form the WordSword Test (link: https://wordsword.psy.cuhk.edu.hk). Thus, the content in the two tests was identical and the only difference was how the test was delivered – one through a paper-and-pencil test and the other through a web browser (see Fig. 1). The test consists of two halves – individual word reading and word reading in contextualized sentences. The total score of the test indicates reading performance.

Fig. 1.

Fig. 1

Open in a new tab

The silent word reading test

In the digital test, participants were asked to “slice” through letters to make words either via touching on a touchscreen (on a mobile phone and a tablet) or clicking with a mouse (on a computer screen) to indicate the separation of words. Similar to turning pages in print reading, on this website, a “next” button was designed for participants to proceed to the next page when finished reading the current page. Scrolling while reading was necessary and the amount of scrolling depended on the size of the display screen. More scrolling was needed for navigating the reading process on a smaller screen. Participants did not need to navigate multiple windows as all reading materials were presented on the same website. The goal of this test is to measure a child’s English reading ability using the sum of the number of correctly identified words for each individual. The first half of the test includes 25 strings of random English words with progressive difficulty. Each string contains 10–20 printed words without spaces between them. The second half of the test consists of random words in strings of letters contextualized in sentences. Twenty sentences, without space between words, were presented to the participants in this task (e.g., heisnothappy). Participants were asked to split the letters into words that they could identify from the word strings. One point was given for each identified word.

Predictors

The following variables were tested in the study via questionnaire: SES, gender, school grade, reading motivation, extraneous cognitive load and the type of reading device used. As participants and parents were Chinese, the questionnaire had been translated into Chinese before data collection.

SES

SES was indicated by parental educational levels and the family’s average monthly income. Parents filled out a parent questionnaire on demographics. Mother’s and father’s educational levels were measured on an 8-point scale from (1) year 9 or below; (2) year 9–11; (3) year 11–13; (4) post high school diploma (associate degree/higher diploma); (5) bachelor’s degree; (6) master’s degree; (7) doctoral degree and (8) others. Family’s monthly income was measured on a 6-point scale, with 1 = 10,000 HK$ or less; 2 = HK$ 10,001–20,000; 3 = HK$ 20,001–30,000; 4 = HK$ 30,001–40,000; 5 = HK$ 40,001–50,000; 6 = HK$ 50,001 or above. The SES composite score, a sum of the standardized score of income and the standardized averaged value of the father’s and mother’s education level, was used for data analyses.

Gender and school grade level

The demographics of the participant’s gender and school grade level were reported by the parents in a questionnaire.

English reading motivation

English reading motivation was reported by participants. It was a 17-item scale with each item presented on a 5-point Likert scale. Participants rated the items from not at all like me (1) to exactly like me (5). The three subscales in this measure were adapted from the reading motivation measures from the previous studies (i.e., Becker et al., 2010; Lin et al., 2012). Questions were specifically adapted to the context of digital reading instead of print reading. A pilot test was carried out before formal testing to ensure that all items included on this questionnaire made sense to all participants. We attempted to measure three aspects of motivation, namely, intrinsic motivation, extrinsic motivation and self-efficacy. A sample item for intrinsic motivation is: “I like reading,”. A sample item for extrinsic motivation is: “I read because my parents find it important that I read a lot.” A sample item for self-efficacy is “I think I am good at English reading.” The Cronbach’s α is 0.850, and we examined motivation as a total scale only in the present study.

Extraneous cognitive load

The extraneous cognitive load was reported by participants. It measured participants’ perception of the mental resources required for navigating digital reading, which was associated with the clarity of instructions and the difficulty of navigation. This 6-item 9-point bipolar scale was adapted from the extraneous cognitive load subscale in the Cognitive Load Index (CLI, Leppink et al., 2013). Participants answered to what extent they agreed with the items on the scale (from not at all to completely) immediately after the digital reading test was finished. The Cronbach’s α is 0.743.

Below are the items:

  1. The instructions during the silent word-reading test were very unclear.

  2. The navigation during the silent word-reading test was very unclear.

  3. The instructions were, in terms of silent word-reading, very effective.

  4. The navigation was, in terms of silent-word reading, very effective.

  5. The instructions were full of unclear language.

  6. The navigation was full of unclear language.

The type of reading devices

The type of reading device used to test word reading in the child (i.e., a computer with a mouse, a tablet with a touch display screen or a mobile phone) was reported by the parents.

Procedure

The parental written consent and participants’ oral assent were obtained before the testing began. Research assistants had been trained to administer the reading tasks and questionnaire data collection. As print reading testing was carried out in 2019 before the pandemic COVID-19 took place, testing and questionnaires were conducted in face-to-face settings. Digital reading testing was conducted in late 2020 during the pandemic; thus, procedures for that testing were explained and implemented via Zoom.

For print reading testing, all participants were invited to complete the study in a quiet area, either a laboratory or at their homes. A research assistant gave instructions to the participants on the silent word reading test. Parents were not allowed to interfere during the reading test. After the participant finished the reading test, a parent filled out the questionnaire containing questions about demographics and other variables.

For digital reading testing, research assistants created an online account for each participant for the digital reading assessment before the testing. On the testing day, participants were informed about the log-in information for the digital reading test. They took the online testing using their device but at the same time shared their screen with the research assistant via Zoom. The research assistant went through the instructions, the testing session, and the questionnaire together with the participants. Questions were asked orally and vocal responses were recorded by the research assistant. After that, the research assistant asked the parent the questions for the parent questionnaire and recorded their responses.

The duration of the reading testing and questionnaire was about 15–20 min. A debriefing was given to parents and participants after the session.

Planned statistical analysis

To test the effects of the predictors on the outcome variables: print reading performance and digital reading performance, multiple linear regression models were run on SPSS 24 (SPSS; IBM Corp, NY). The enter method was used for the multiple regression analyses. Nominal variables, such as gender and the type of reading devices, were dummy coded before running the analyses.

Results

All variables were transformed into Z-scores for data analyses (except for the type of devices, which was dummy coded). Normal Probability Plots of standardized residuals against standardized predictors values, collinearity diagnostics, scatterplots of residuals, Cook’s Distance, and histograms with a superimposed normal curve were checked before data analyses and interpretations. The assumptions of an absence of multicollinearity and outliers, linearity, homoscedasticity and multivariate normality were all met.

The descriptive statistics of the data are shown in Table 1 and the correlations among variables are shown in Table 2. Given the one-year gap between the two assessments, it is invalid to make a direct comparison across the means of reading performance. Rather, a correlation analysis was performed on the test results of the digital assessment and the print assessment to demonstrate the strength of the association between the two test versions. The correlation between the two versions of the test was high (r = .835, p < .001).

Table 1.

Descriptive statistics

N Minimum Maximum Mean Std. Deviation
grade 86 1 5 3.53 1.20
SES 74 -3.54 2.85 − 0.055 1.75
device (phone)a 86 0.00 1.00 0.081 0.275
device (tablet)a 86 0.00 1.00 0.488 0.503
extraneous cognitive load 86 6 42 17.97 8.79
English reading motivation 85 19.00 75.00 45.18 12.24
SWRb (digital) 84 34 284 160.89 60.14
SWRAc (digital) 84 9 141 77.02 32.89
SWRBd (digital) 84 25 148 83.87 28.67
SWRb_(print) 81 11 271 125.59 61.82
SWRAc_(print) 81 4 138 56.37 32.64
SWRBd_(print) 84 6 155 72.07 33.57
Open in a new tab

Note.a Dummy-coded variable: the reference group was computer (computer = 0).

b SWR = silent word reading (total).

c SWRA = silent word reading (uncontextualized).

d SWRB = silent word reading (contextualized).

Table 2.

Correlations

1 2 3 4 5 6 7 8 9
1. Gendera 1
2. Grade 0.145 1
3. SES − 0.137 0.081 1
4. English reading motivation 0.012 − 0.155

0.243

*

 1
5. Extraneous cognitive load − 0.044

− 0.465

***

− 0.299

*

 0.008 1
6. Device (phone)b 0.192

0.260

*

 − 0.057 − 0.180 0.079 1
7. Device (tablet)b 0.072 0.030 0.034 − 0.039 − 0.087

− 0.291

**

 1
8. SWRc (print) 0.096

0.513

***

0.468

***

 0.180

− 0.512

***

 − 0.022 0.024 1
9. SWRc (digital) 0.119

0.378

***

0.446

***

0.336

**

− 0.563

***

 − 0.182 0.123

0.835

***

 1
Open in a new tab

Note. *p < .05; **p < .01; ***p < .001.

a Dummy-coded variable (female = 0).

b Dummy-coded variable (computer = 0).

c Silent word reading (total).

Print reading performance

To test the effects of the individual differences on print reading performance, a multiple linear regression model (N = 70) was performed. Multicollinearity diagnostics revealed only weak signs of multicollinearity (variance inflation factor was lower than 4 in all cases). Gender was not found to be a significant predictor (β = − 0.014, t = − 0.161, p = .872) and was not correlated to any variables investigated in this study. Therefore, it was removed from the model. Print reading performance was significantly and positively explained by grade (β = 0.568, t = 6.96, p < .001), SES (β = 0.382, t = 4.60, p < .001) and English reading motivation (β = 0.278, t = 3.33, p < .01). The overall model accounted for 55.0% of variance in print reading performance (adjusted R2 = 0.550, F(3,66) = 29.16, p < .001).

Digital reading performance

A hierarchical regression model (see Table 3) was performed (N = 72) to test if the extraneous cognitive load and the type of devices could explain digital reading performance after controlling for the effects of individual differences (i.e., gender, SES, grade and English reading motivation). Since gender was found to be not significant (β = 0.001, t = 0.012, p = .991) and was not correlated to any variables investigated in this study, it was removed from the model. Thus, SES, grade and English reading motivation were entered into the first block of the model while extraneous cognitive load and the type of devices were entered into the second block of the model. The variable labeled as a type of devices was dummy-coded into two predictor variables (i.e., reading on a tablet and reading on a phone) in this model, with reading on a computer being the reference group.

Table 3.

The predictors of digital reading performance

Step and predictor Standardized β
1 (Constant)
grade 0.455*** 0.378***
SES 0.333*** 0.268**
English reading motivation 0.375*** 0.326***
2 extraneous cognitive load / − 0.255**
device (phone) / − 0.187*
device (tablet) / 0.076
Open in a new tab

Note. *p < .05; **p < .01; ***p < .001.

Multicollinearity diagnostics revealed only weak signs of multicollinearity (variance inflation factor was lower than 4 in all cases). Similar to print reading performance, grade remained the strongest predictor of digital, reading performance. Digital reading performance was positively and significantly predicted by grade (β = 0.378, t = 3.98, p < .001), SES (β = 0.268, t = 3.30, p < .01) and English reading motivation (β = 0.326, t = 4.04, p < .001) and negatively and significantly predicted by extraneous cognitive load (β = .- 255, t = -2.68, p < .01) and reading on a phone (β = − 0.187, t = -2.14, p < .05). Nevertheless, reading on a tablet was not a significant predictor of digital reading performance (β = 0.076, t = 0.935, p = .353). The overall model accounted for 58.8% of variance in digital reading performance (adjusted R2 = 0.588, F(6,65) = 17.92, p < .001), with an 11.5% increase in the accounted variance after including the variables in the second block (ΔR2 = 0.115).

Discussion

Many assessments have been digitalized in the present era due to the advancement of technology and the pandemic COVID-19. Therefore, it is important to understand how reading digitally is associated with a variety of factors. This study examined the effects of a comprehensive set of predictors on children’s digital word reading fluency in L2, with a focus on the effects of extraneous cognitive load and the type of reading device. In this study, gender was not found to significantly predict print word reading fluency or digital word reading fluency when other predictors (e.g., SES, grade and English motivation) were included in our model. Both print reading and digital reading performance were significantly and positively predicted by grade, SES and English reading motivation. Our findings demonstrated the prominent role of SES and grade in word reading performance in early L2 development. Although the print reading assessment was measured one year prior to the digital reading assessment and other variables, the correlation between the two tests was high. Moreover, some research findings have demonstrated that reading motivation overall tends to remain stable over a school year (e.g., Lau 2016; Vaknin-Nusbaum et al., 2018). Given the high association between the two tests and the stability of the predictors (i.e., SES, grade, and English reading motivation) over time, it appears that SES, grade and English reading motivation tend to explain print reading and digital reading performance similarly.

In addition, extraneous cognitive load and the type of reading device used were significant correlates of digital reading performance after controlling for individual differences in grade, SES, and English reading motivation. Extraneous cognitive load negatively predicted digital reading performance. Digital reading performance was affected by the type of device. More specifically, no significant difference was found between reading on a tablet and a computer. However, reading performance was worse when the test was performed using a phone than performed using a computer.

Advanced technology affords special features for digital reading, which shapes readers’ reading behaviors. For instance, compared to printed reading materials, which are displayed in a linear fashion and can be easily flipped through, digital reading materials do not support information location in a document and can even be structured and displayed in a non-linear fashion, e.g., the use of hyperlinks. Readers may also need to constantly scroll on a screen to reveal another portion of the texts. Such technological features make it difficult to establish a visual-spatial memory of the whole reading document (Manguel, 1996). This may require additional cognitive resources for the navigation of digital reading that print reading does not.

The current study focused on investigating how extraneous cognitive load and the type of device explained digital word reading performance. The type of reading device is crucial in promoting digital reading performance. Existing findings have revealed a negative correlation between the size of the display screen and cognitive resources and suggested that bigger screens have an advantage as there will be fewer scrolls and therefore less cognitive disruption (Sanchez & Wiley, 2009). Since most mobile phones have a smaller screen than a computer, it is possible that the negative effects of reading on a phone partially stemmed from a smaller device screen. For example, Gostner et al. (2008) found that participants’ (age: M = 29.9, SD = 4.8) reading performance was significantly better when reading on a phone which was connected to an external larger screen than reading on a phone alone.

Generally speaking, reading on a larger screen is always likely to be easier than reading on a small one (Öquist and Lundin, 2007). Nevertheless, researchers should not overlook other technological affordances with different devices. These differences might be responsible for the burden on readers’ extraneous cognitive load, which negatively affects digital reading performance. Extraneous cognitive load refers to the cognitive resources that are not necessary for learning processes but are required for processing information. It is determined by the design of the reading tasks. Future research should investigate the effects of other features of digital devices. For example, a study comparing reading on a phone with or without a touch screen (Gostner et al., 2008) demonstrated a significant difference in reading performance. Participants read faster and completed the task significantly earlier when reading on a phone with a touch screen. Understanding the underlying reasons for the different effects of different reading devices can allow readers to maximize their digital reading performance by utilizing the most suitable reading device.

While it is important to examine the features of different reading devices and how they enable the navigation of digital texts, the presentation of digital texts is also associated with extraneous cognitive load. Instructional designers or teachers should aim at lowering extraneous cognitive load when designing digital reading tasks and assessments. While enlarging the screen of reading devices may appear to be an obvious solution, with the rapid advancement of mobile technology, reading on a mobile phone has become increasingly ubiquitous. Design guidelines for the improvement of readability, especially the readability of smaller screens, are crucial for improving digital reading performance. For instance, Öquist and Lundin (2007) discovered that different ways of text presentations on small screens affected reading speed and task load differently. They examined four types of text presentation: scrolling, paging, leading and rapid serial visual presentation. Overall, paging (i.e., texts are divided into pages that fit the screen area) offered the best readability on the mobile phone as it was most efficient in terms of reading speed and the least demanding to use. Eye movement analyses demonstrated that scrolling increased the number of regressions when compared with paging.

This study focused on investigating the impact of digitalization on word reading fluency. In past research on reading, conclusions from L1 reading were systematized by many researchers to improve L2 reading instruction (e.g., Bernhardt 2003; Grabe & Stoller, 2013; Koda 2005). Since no existing evidence has suggested that the impacts of digitalization on L1 reading and L2 reading are different, we had not expected any significant differences in the effects of digitalization on word reading between L1 and L2. Therefore, instead of differentiating the effects on L1 and L2, our interpretation of the findings focused more on the implications for digital assessments in L2 as participants in this study were drawn from L2 readers. As learning ESL has become increasingly important worldwide, the development of effective and efficient reading assessments of L2 English is beneficial to educators. Our findings have shown that reading performance in the printed and digital formats were highly correlated, demonstrating high reliability of print and digital assessments in L2 English assessments. Given the wide array of the benefits of digital assessments such as the automation in scoring, the convenience in test distribution through self-administration, and testing in groups, digital assessments like the WordSword Test in this study could be a useful and cheap method of assessment for L2 English worldwide.

To conclude, our findings demonstrated similar correlates of reading performance in printed and digital formats. Grade level, SES, and English reading motivation are independently important for reading skills. In addition, our findings have highlighted the importance of external factors such as instructional design and technological design in improving digital reading performance. More research should be conducted to investigate how to maximize digital reading performance by increasing the readability of reading devices and reading tasks. Implications for L2 English assessments were discussed and findings demonstrated the possibility of using digital assessments for measuring L2 English reading performance.

Authors’ contributions

Jana Chi-San Ho contributed to the conceptual ideas and data analyses in this study. Catherine McBride and Kelvin Fai Hong Lui contributed to the revisions. The first draft of the manuscript was written by Jana Chi-San Ho and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

No funding was received to assist with the preparation of this manuscript.

Data Availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval

Data collection of this study has been approved by the ethics board at the Chinese University of Hong Kong Survey and Behavioural Research Ethics Committee. References numbers are C4054-17WF and SBRE-19-540.

Consent to participate

In the norming phase, invitation letters were first sent to schools and informed consent was again obtained from parents of all participating children.

Consent for publication

The authors have given consent to publication of this manuscript.

Conflicts of Interest

The authors have declared that they have no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Jana Chi-San Ho, Email: ho253@purdue.edu.

Catherine McBride, Email: mcbridca@purdue.edu.

Kelvin Fai Hong Lui, Email: kelvinlui@ln.edu.hk.

References

  1. Akbar RS, Taqi HA, Dashti AA, Sadeq TM. Does e-reading enhance reading fluency? English Language Teaching. 2015;8(5):195–207. doi: 10.5539/elt.v8n5p195. [DOI] [Google Scholar]
  2. Arnold S, Reed P. Measuring the word recognition abilities of children who are both verbal and nonverbal with ASD using a traditional paper-based and a novel digital test format. British Journal of Special Education. 2019;46(3):340–360. doi: 10.1111/1467-8578.12279. [DOI] [Google Scholar]
  3. Barber AT, Klauda SL. How reading motivation and engagement enable reading achievement: policy implications. Policy Insights from the Behavioral and Brain Sciences. 2020;7(1):27–34. doi: 10.1177/2372732219893385. [DOI] [Google Scholar]
  4. Basuroy, T. (2019, Sept). India - devices of internet access in rural area 2019. Statista. https://www.statista.com/statistics/751322/devices-of-internet-access-in-rural-area-india/
  5. Becker M, McElvany N, Kortenbruck M. Intrinsic and extrinsic reading motivation as predictors of reading literacy: a longitudinal study. Journal of Educational Psychology. 2010;102(4):773–785. doi: 10.1037/a0020084. [DOI] [Google Scholar]
  6. Bernhardt E. Challenges to reading research from a multilingual world. Reading Research Quarterly. 2003;38(1):112–117. [Google Scholar]
  7. Carr N. The Shallows: what the internet is doing to our brains. Contemporary Sociology. 2011;40(6):772–774. doi: 10.1177/0094306111425021d. [DOI] [Google Scholar]
  8. Chen, D. W., & Catrambone, R. (2015). Paper vs. Screen: Effects on Reading Comprehension, Metacognition, and Reader Behavior. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 332–336. 10.1177/1541931215591069
  9. Chen G, Cheng W, Chang TW, Zheng X, Huang R. A comparison of reading comprehension across paper, computer screens, and tablets: does tablet familiarity matter? Journal of Computers in Education. 2014;1(2–3):213–225. doi: 10.1007/s40692-014-0012-z. [DOI] [Google Scholar]
  10. Cheng, W., Zheng, X., Li, M., & Chen, G. (2014). Which is the best for reading: Paper, computer, or tablet computer?. 2014 IEEE 14th International Conference on Advanced Learning Technologies, 655–656. 10.1109/ICALT.2014.192
  11. Clinton V. Reading from paper compared to screens: a systematic review and meta-analysis. Journal of Research in Reading. 2019;42(2):288–325. doi: 10.1111/1467-9817.12269. [DOI] [Google Scholar]
  12. Coiro J. Rethinking online reading assessment. Educational Leadership. 2009;66(6):59–63. [Google Scholar]
  13. Coiro J. Predicting reading comprehension on the internet: contributions of offline reading skills, online reading skills, and prior knowledge. Journal of Literacy Research. 2011;43(4):352–392. doi: 10.1177/1086296X11421979. [DOI] [Google Scholar]
  14. Countries in which English language is a mandatory or an optional subject (interactive) (n.d.). The University of Winnipeg. Retrieved Nov 01 (2022)., from https://www.uwinnipeg.ca/global-english-education/countries-in-which-english-is-mandatory-or-optional-subject.html
  15. Davis, M. (2006). Reading instruction: the two keys. Core Knowledge Foundation.
  16. Delgado P, Vargas C, Ackerman R, Salmerón L. Don’t throw away your printed books: a meta-analysis on the effects of reading media on reading comprehension. Educational Research Review. 2018;25:23–38. doi: 10.1016/j.edurev.2018.09.003. [DOI] [Google Scholar]
  17. Dhanapala, K. V., & Hirakawa, Y. (2016). L2 reading motivation among Sri Lankan university students. Reading Psychology, 37(2), 202–229. 10.1080/02702711.2015.1025163
  18. Dillon A. Reading from paper versus screens: a critical review of the empirical literature. Ergonomics. 1992;35(10):1297–1326. doi: 10.1080/00140139208967394. [DOI] [Google Scholar]
  19. Dourish, P. (2004). Where the action is: the foundations of embodied interaction. MIT Press. 10.7551/mitpress/7221.001.0001.
  20. Eno, L. P. (2011). Comparing the reading performance of high-achieving adolescents: Computer-based testing versus paper/pencil (dissertation). Seton Hall University, South Orange, NJ.
  21. Fernald A, Marchman VA, Weisleder A. SES differences in language processing skill and vocabulary are evident at 18 months. Developmental Science. 2013;16(2):234–248. doi: 10.1111/desc.12019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Fitzpatrick N. Digital reading: a question of Prelectio? In: Fowley C, English C, Thouësny S, editors. Internet Research, Theory, and practice: perspectives from Ireland. Dublin: Research-publishing.net; 2013. pp. 95–110. [Google Scholar]
  23. Fry, E. (1970, Dec 5). A Classification of Factors Affecting Reading Performance [Conference presentation]. Twentieth Yearbook of the National Reading Conference, Milwaukee, WI, United States.
  24. Gartner Inc (2011, May 10). Gartner survey on consumer digital reading preferences reveal the exaggerated death of paper. Gartner Newsroom. https://prwire.com.au/pr/22972/gartner-survey-on-consumer-digital-reading-preferences-reveal-the-exaggerated-death-of-paper
  25. Gostner, R., Gellersen, H., Kray, C., & Sas, C. (2008). Reading with Mobile Phone & Large Display CHI08 Workshop: Designing and Evaluating Mobile Phone-Based Interaction with Public Displays. https://eprints.lancs.ac.uk/id/eprint/41513/1/Gostner-et-al_CHI08.pdf
  26. Gough PB, Tunmer WE. Decoding, reading, and reading disability. Remedial and Special Education. 1986;7(1):6–10. doi: 10.1177/074193258600700104. [DOI] [Google Scholar]
  27. Grabe, W. P., & Stoller, F. L. (2013). Teaching and researching: Reading. Routledge. 10.4324/9781315833743
  28. Guthrie, J. T., & Klauda, S. L. (2015). Engagement and motivational processes in reading. In P. Afflerbach (Ed), Handbook of individual differences in reading (pp. 59–71). Routledge. 10.4324/9780203075562
  29. Gyselinck V, Jamet E, Dubois V. The role of working memory components in multimedia comprehension. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition. 2008;22(3):353–374. doi: 10.1002/acp.1411. [DOI] [Google Scholar]
  30. Hamhuis E, Glas C, Meelissen M. Tablet assessment in primary education: are there performance differences between TIMSS’ paper-and-pencil test and tablet test among dutch grade-four students? British Journal of Educational Technology. 2020;51(6):2340–2358. doi: 10.1111/bjet.12914. [DOI] [Google Scholar]
  31. Hayward, L. A. (2019). The Digital Reading Experiences of Middle School Readers: A Phenomenological Study [Doctoral dissertation, The University of Texas Rio Grande Valley]. ProQuest Dissertations Publishing.
  32. Heitin, L. (2016, Nov 9). How should reading be taught in a digital era? EducationWeek. https://www.edweek.org/ew/articles/2016/11/09/how-should-reading-be-taught-in-digital-era.html
  33. Hutchby, I. (2013). Conversation and technology: from the telephone to the internet. John Wiley & Sons.
  34. IBM Corp. Released 2016. IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY:IBM Corp.
  35. Jerrim J. PISA 2012: how do results for the paper and computer tests compare? Assessment in Education: Principles Policy & Practice. 2016;23(4):495–518. doi: 10.1080/0969594X.2016.1147420. [DOI] [Google Scholar]
  36. Kalindi SC, McBride C, Chan S, Chung KHK, Lee CY, Maurer U, Tong X. A short test of English silent word reading for English language learners. Child Studies in Asia-Pacific Contexts. 2015;5(2):95–105. doi: 10.5723/csac.2015.5.2.095. [DOI] [Google Scholar]
  37. Kavanagh L. Relations between children’s reading motivation, activity and performance at the end of primary school. Journal of Research in Reading. 2019;42(3–4):562–582. doi: 10.1111/1467-9817.12284. [DOI] [Google Scholar]
  38. Koda, K. (2005). Insights into second language reading: a cross-linguistic approach. Cambridge University Press.
  39. Kong Y, Seo YS, Zhai L. Comparison of reading performance on screen and on paper: a meta-analysis. Computers & Education. 2018;123:138–149. doi: 10.1016/j.compedu.2018.05.005. [DOI] [Google Scholar]
  40. Kurniawan, S. H., & Zaphiris, P. (2001). Reading Online or on Paper: Which is Faster? Abridged Proceedings of the 9th International Conference on Human Computer Interaction, 220–222. Semantic Scholar. https://ktisis.cut.ac.cy/bitstream/10488/5247/5/Kurniawan2001-Reading_Online_or_on_Paper_Which_is_Faster.pdf
  41. Lau KL. Within-year changes in chinese secondary school students’ perceived reading instruction and intrinsic reading motivation. Journal of Research in Reading. 2016;39(2):153–170. doi: 10.1111/1467-9817.12035. [DOI] [Google Scholar]
  42. Leppink J, Paas F, Van der Vleuten CP, Van Gog T, Van Merriënboer JJ. Development of an instrument for measuring different types of cognitive load. Behavior Research Methods. 2013;45(4):1058–1072. doi: 10.3758/s13428-013-0334-1. [DOI] [PubMed] [Google Scholar]
  43. Lin D, Wong KK, McBride-Chang C. Reading motivation and reading comprehension in chinese and english among bilingual students. Reading and Writing. 2012;25(3):717–737. doi: 10.1007/s11145-011-9297-8. [DOI] [Google Scholar]
  44. Ling, L. (2004). Improving student reading motivation through web-based technologies [Unpublished doctoral dissertation]. The University of Hong Kong.
  45. Liu D, Chung KK, McBride C. The role of SES in Chinese (L1) and English (L2) word reading in chinese-speaking kindergarteners. Journal of Research in Reading. 2016;39(3):268–291. doi: 10.1111/1467-9817.12046. [DOI] [Google Scholar]
  46. Liu, Z. (2012). Digital reading.Chinese Journal of Library and Information Science (English edition),85–94.
  47. Mangen A, Walgermo BR, Brønnick K. Reading linear texts on paper versus computer screen: Effects on reading comprehension. International Journal of Educational Research. 2013;58:61–68. doi: 10.1016/j.ijer.2012.12.002. [DOI] [Google Scholar]
  48. Manguel A. A history of Reading. New York: Viking Penguin; 1996. [Google Scholar]
  49. Marks GN. Accounting for the gender gaps in student performance in reading and mathematics: evidence from 31 countries. Oxford Review of Education. 2008;34(1):89–109. doi: 10.1080/03054980701565279. [DOI] [Google Scholar]
  50. Martin R, Binkley M. Gender differences in cognitive tests: a consequence of gender-dependent preferences for specific information presentation formats? In: Friedrich S, Julius B, editors. The transition to computer-based assessment: new approaches to skills assessment and implications for large-scale testing. Luxembourg: European Communities; 2009. pp. 75–82. [Google Scholar]
  51. Mechling LC, Gast DL, Thompson KL. Comparison of the effects of smart board technology and flash card instruction on sight word recognition and observational learning. Journal of Special Education Technology. 2008;23(1):34–46. doi: 10.1177/016264340802300103. [DOI] [Google Scholar]
  52. Morgan PL, Fuchs D. Is there a bidirectional relationship between children’s reading skills and reading motivation? Exceptional Children. 2007;73(2):165–183. doi: 10.1177/001440290707300203. [DOI] [Google Scholar]
  53. Mullis, I. V. S., Martin, M. O., Kennedy, A. M., & Foy, P. (2007). PIRLS 2006 international report International Association for the Evaluation of Educational Achievement. https://timssandpirls.bc.edu/PDF/PIRLS2006_international_report.pdf
  54. Murray, M. S. (2016). 3. Word Recognition Skills: One of Two Essential Components of Reading Comprehension. In K. A. Munger (Ed.), Steps to success: Crossing the bridge between Literacy Research and Practice (pp. 27–40). Open SUNY Textbooks.
  55. Netten A, Luyten H, Droop M, Verhoeven L. Role of linguistic and sociocultural diversity in reading literacy achievement: a multilevel approach. Journal of Research in Reading. 2016;39(2):189–208. doi: 10.1111/1467-9817.12032. [DOI] [Google Scholar]
  56. OECD Publishing (2011). PISA 2009 Results: Students On Line: Digital Technologies and Performance (Volume VI). https://www.oecd.org/pisa/pisaproducts/48270093.pdfhttp10.1787/9789264112995-en
  57. Olshtain E, Shohamy E, Kemp J, Chatow R. Factors predicting success in EFL among culturally different learners. Language Learning. 1990;40:23–44. doi: 10.1111/j.1467-1770.1990.tb00953.x. [DOI] [Google Scholar]
  58. Öquist, G., & Lundin, K. (2007). Eye movement study of reading text on a mobile phone using paging, scrolling, leading, and RSVP. Proceedings of the 6th International Conference on Mobile and Ubiquitous Multimedia, 176–183. 10.1145/1329469.1329493
  59. OverDrive Education (2020, December 1). Schools’ Usage of Ebooks and Audiobooks Surges in 2020 [Press release]. https://company.overdrive.com/2020/12/01/schools-usage-of-ebooks-and-audiobooks-surges-in-2020/
  60. Paek, P. (2005, Aug). Recent trends in comparability studies.Pearson Educational Measurement. http://images.pearsonassessments.com/images/tmrs/tmrs_rg/TrendsCompStudies.pdf
  61. Pan CY, Huang PC. The development and evaluation of a web-based college English outside reading system for EFL freshmen. JALT CALL Journal. 2009;5(3):3–22. doi: 10.29140/jaltcall.v5n3.83. [DOI] [Google Scholar]
  62. Pan J, Kong Y, Song S, McBride C, Liu H, Shu H. Socioeconomic status, parent report of children’s early language skills, and late literacy skills: a long term follow-up study among chinese children. Reading and Writing. 2017;30(2):401–416. doi: 10.1007/s11145-016-9682-4. [DOI] [Google Scholar]
  63. Pirih A. Who says they don’t read? Slovene elementary school students’ reading motivation in EFL. Journal of Elementary Education. 2015;8(1/2):113–132. [Google Scholar]
  64. Qi GY. The importance of English in primary school education in China: perceptions of students. Multilingual Education. 2016;6(1):1–18. doi: 10.1186/s13616-016-0026-0. [DOI] [Google Scholar]
  65. Quirk, M. P. (2005). The development of reading fluency and motivation to read: an examination of the causal relationship [Doctoral dissertation]. The University of Georgia.
  66. Rao PS. The role of English as a global language. Research Journal of English. 2019;4(1):65–79. [Google Scholar]
  67. Rapp DN, Van Den Broek P. Dynamic text comprehension: an integrative view of reading. Current Directions in Psychological Science. 2005;14(5):276–279. doi: 10.1111/j.0963-7214.2005.00380.x. [DOI] [Google Scholar]
  68. Rasmusson M, Åberg-Bengtsson L. Does performance in digital reading relate to computer game playing? A study of factor structure and gender patterns in 15-year-olds’ reading literacy performance. Scandinavian Journal of Educational Research. 2015;59(6):691–709. doi: 10.1080/00313831.2014.965795. [DOI] [Google Scholar]
  69. Sackstein, S., Spark, L., & Jenkins, A. (2015). Are e-books effective tools for learning? Reading speed and comprehension: iPad® i vs. paper. South African Journal of Education, 35(4), 10.15700/saje.v35n4a1202.
  70. Sanchez, C. A., & Wiley, J. (2009). To scroll or not to scroll: scrolling, working memory capacity, and comprehending complex texts. Human Factors, 51(5), https://doi.org/730-738. [DOI] [PubMed]
  71. Scheuerman F, Bjornsson J. The transition to computer-based assessment: new approaches to skills assessment and implications for large-scale testing. Luxembourg: European Communities; 2009. [Google Scholar]
  72. Støle, H., Mangen, A., Frønes, T. S., & Thomson, J. (2018). Digitisation of reading assessment. In M. Barzillai, J. Thomson, S. Schroeder, & P. van den Broek (Eds.), Learning to read in a Digital World (pp. 205–224). John Benjamins Publishing Company. 10.1075/swll.17.09sto
  73. Sweller J. Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction. 1994;4(4):295–312. doi: 10.1016/0959-4752(94)90003-5. [DOI] [Google Scholar]
  74. Srivastava P, Gray S. Computer-based and paper-based reading comprehension in adolescents with typical language development and language-learning disabilities. Language Speech and Hearing Services in Schools. 2012;43(4):424–437. doi: 10.1044/0161-1461(2012/10-0108). [DOI] [PubMed] [Google Scholar]
  75. Taipale S. The affordances of reading/writing on paper and digitally in Finland. Telematics and Informatics. 2014;31(4):532–542. doi: 10.1016/j.tele.2013.11.003. [DOI] [Google Scholar]
  76. Vaknin-Nusbaum V, Nevo E, Brande S, Gambrell L. Developmental aspects of reading motivation and reading achievement among second grade low achievers and typical readers. Journal of Research in Reading. 2018;41(3):438–454. doi: 10.1111/1467-9817.12117. [DOI] [Google Scholar]
  77. Wu JY. Gender differences in online reading engagement, metacognitive strategies, navigation skills and reading literacy. Journal of Computer Assisted Learning. 2014;30(3):252–271. doi: 10.1111/jcal.12054. [DOI] [Google Scholar]
  78. Wu TT, Chen AC. Combining e-books with mind mapping in a reciprocal teaching strategy for a classical chinese course. Computers & Education. 2018;116:64–80. doi: 10.1016/j.compedu.2017.08.012. [DOI] [Google Scholar]
  79. Wylie, J., Thomson, J., Leppänen, P. H., Ackerman, R., Kanniainen, L., & Prieler, T. (2018). Cognitive processes and digital reading. Learning to Read in a Digital World, 17, 57–90. 10.1075/swll.17.03wyl
  80. Yang G, Badri M, Rashedi A, Almazroui K. The role of reading motivation, self-efficacy, and home influence in students’ literacy achievement: a preliminary examination of fourth graders in Abu Dhabi. Large-Scale Assessments in Education. 2018;6(1):1–19. doi: 10.1186/s40536-018-0063-0. [DOI] [Google Scholar]
  81. Zipke M. Preschoolers explore interactive storybook apps: the effect on word recognition and story comprehension. Education and Information Technologies. 2017;22(4):1695–1712. doi: 10.1007/s10639-016-9513-x. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Articles from Reading and Writing are provided here courtesy of Nature Publishing Group

RESOURCES