Students’ academic reading preferences: An exploratory study

Abstract

The e-book reader revolution is already here. The questions we asked ourselves were: What are the reading preferences of Information Science students at the beginning of the second decade of the 21st century? How do different variables, such as relative advantage, comprehension, and learning strategies affect students’ reading preferences? The research was conducted in Israel during the first semester of the 2015 academic year and encompassed 177 Library and Information Science students in an Information Science Department in Israel. Three questionnaires were used: personal details, relative advantage, and learning strategies, and two further questions that focused on reading habits. The study showed students’ preferences for printed materials. In addition, it emphasizes the importance of two personal variables that may affect students’ will to read electronic materials: relative advantage and comprehension.

Keywords

E-book Innovation Diffusion Theory learning strategies preferences printed reading

Introduction

The e-book reader revolution is now with us. Many are intrigued by the question: is it going to replace the printed book? In 1978, Frederick Lancaster published his book, Toward Paperless Information Systems, in which he described his vision of a paperless scientific system, where researchers will create, submit, store, retrieve, disseminate, discuss, and read scientific literature through their computers. In 1985, he published a follow-up article on the topic, where he states, ‘The replacement of print on paper is not inevitable. That is, society could presumably choose to reject the transition’ (Lancaster, 1985: 555). In many ways, his original vision has been fulfilled. However, even in our current ‘paperless society’, huge amounts of information from computers and the Internet are printed out and ‘consumed’ in print format.

Changes in attitude over time due to technological developments are nicely illustrated by two pieces written by Jakob Nielsen. In the first, from 1998, he explains why electronic books are a bad idea; in the second, from 2009, written after using Kindle and the Kindle app on the iPhone, Nielsen admits that Kindle 2 changed his mind regarding e-books. Thus, it is quite possible that other users experience attitude change over time.

Meanwhile, as a result of the proliferation of online textbooks, newspapers, encyclopedias, and online academic journals, information users are faced with the availability of digital as well as print texts (Cargill, 2011; Hamblen, 2011; Heider et al., 2009). The questions we asked ourselves in the current paper were: What are the academic reading preferences of Information Science students at the beginning of the second decade of the 21st century? and: How do different variables, such as relative advantage, learning strategies, and comprehension, affect students’ academic reading preferences. These students were chosen specifically because of their extensive exposure to information and technology. Although various studies have considered students’ reading habits (Ackerman and Goldsmith, 2011; Ben-Yehudah and Eshet-Alkalai, 2014; Foasberg, 2014; Liu, 2006; Spencer, 2006; Walton, 2014), no one has focused to date on cognitive variables that may explain students’ academic reading preferences. This research may contribute to an understanding of the cognitive variables that influence students’ academic reading preferences, and may lead to further inquiry in this field.

The current study uses the Diffusion of Innovation Theory (Rogers, 1995), as well as a learning strategies perspective (Marton and Säljö, 1976a, 1976b) as theoretical bases from which to predict students’ academic reading preferences. The paper is organized as follows: in the next section we provide a review of some of the relevant literature, then we describe the study design and processes, present the results and discuss them and, lastly, we suggest future directions for research.

Theoretical background

Reading habits

Various studies have reported that there are differences between reading from print and from digital platforms. Evans et al. (2009) suggest that reading from a digital platform is characterized by more time spent on browsing and scanning, keyword spotting, one-time reading, non-linear reading, and reading more selectively, and less time spent on in-depth reading and concentrated reading. Quinn and Stark-Adam (2007) found that digital readers ‘jump’ from place to place in the text as they read, while print readers read the text line-by-line. Further, reading from a digital platform is slower than reading from a print one (Evans et al., 2009; Gould et al., 1987; O’Hara and Sellen, 1997). In addition, readers of digital texts report fatigue and discomfort (Rouet, 2000), and add that the lack of printed text causes a feeling of disorientation (Armitage et al., 2004; Lazar et al., 2003). An evaluation of the shift from print towards digital reading is crucial in higher education as learning from digital texts becomes more common (Cargill, 2011; Heider et al., 2009; Thayer et al., 2011). In the following section, we review some studies comparing print versus online reading, in which the participants were students

Noyes and Garland (2005) studied students’ attitudes towards books and found that the survey respondents preferred books, and expected to learn more from books than computer-based materials. In another study, Spencer (2006) surveyed 254 students on their online course-related learning habits. Somewhat contrary to expectations, no differences were found based on the age of the respondents. There was a stronger preference for reading course notes in print than for reading articles. Urgently needed information, like schedule and assignments, was read more often online. Further, Liu (2006) conducted a study of graduate students in 2004 on their preference for print or electronic sources. Among other questions, participants were asked how often they printed electronic sources and how often they read online. Electronic sources were much more frequently used than print sources, but only about one third of the participants reported that they always or frequently read online. On the other hand, around 80% of the respondents printed out electronic sources frequently or always. The results of a survey of 58 students participating in a course at the Open University of Israel showed that the majority of the respondents preferred a combination of a digital and a print textbook (Precel et al., 2009).

Recently, different researchers (Ackerman and Goldsmith, 2011; Foasberg, 2014) found that students prefer to read long academic texts and long-form reading in print. Moreover, some researchers (Ben-Yehudah and Eshet-Alkalai, 2014) have explored the influence of annotation tools (e.g., highlighting, underlining, and note taking) on comprehending print and digital reading texts. Results duplicate previous research reports on the inferiority of digital reading comprehension compared to that of print (Ackerman and Goldsmith, 2011; Ackerman and Lauterman, 2012; Eshet-Alkalai and Geri, 2007, 2010). However, in contrast to this, Eden and Eshet-Alkalai (2014) explored the active-reading abilities of students who were asked to read, edit, recognize errors, and improve the quality of short articles, in print and digital formats. They did not find a significant difference between the performance of participants in the two formats. Digital readers finished their tasks earlier than print readers, and their performance was not lower. The next section presents the Diffusion of Innovation Theory (Rogers, 1995) as a factor that may explain students’ reading preferences.

Diffusion of Innovation Theory

The Diffusion of Innovation Theory (DIT) (Rogers, 1995) has been used to predict an individual’s adoption of new technologies and services. It suggests that innovations are not adopted simultaneously by all people, but rather are the result of personal, social, and technological factors (Leung and Wei, 1999). Rogers (1995) argues that some people are willing to try innovations more than others, and he has classified people into five adopter categories: innovators, early adopters, early majority, late majority, and laggards. In addition, Rogers (2003) suggests that the following attributes help decrease uncertainty about an innovation:

Relative advantage: the extent to which individuals perceive that the innovation is better than the traditional way. The relative advantage can be measured in social prestige, economic terms and satisfaction. The greater the relative advantage, the quicker the rate of the adoption will be.

Compatibility: the degree to which individuals perceive that the innovation is compatible with the issues of existing, traditional values and past experience. The more the idea is compatible with the current norms and values, the more it will be adopted.

Complexity: the extent to which individuals perceive that the innovation is difficult to use or understand. Rogers (1995) proposes that some innovations might be easier to understand and some more difficult.

Trial ability: the extent to which individuals perceive that there are chances they may use the innovation before deciding to adopt it or not. If the individual can try the innovation, it represents less uncertainty when considering adopting the innovation.

Observability: this attitude refers to the extent to which individuals may perceive that the results of the innovation are visible to others. The higher the visibility, the more likely the adoption of the innovation will be.

Rogers’ model that attempts to predict individuals’ adoption of new technologies, has been used in various fields such as marketing (Flight et al., 2011; Tanakinjal et al., 2010), banking (Suki, 2010), engineering management (Tornatzky and Klein, 1982), information technology (Agarwal and Prasad, 1998; Taylor and Todd, 1995), social media (Ma et al., 2014), and higher education and educational environments (Liao and Lu, 2008; Medlin, 2001; Parisot, 1997; Shonfeld and Aharony, 2015). The current study uses the first attribute, relative advantage, and refers to the extent to which individuals perceive that the innovation of reading electronic materials is better than traditional print for reading academic articles in the course of their studies. In other words, we try to investigate how this variable of relative advantage influences students’ uncertainty about adopting reading electronic materials during their process of learning. The next section focuses on learning strategies, a variable that may also affect students’ reading preferences.

Learning strategies

Recently, there has been great interest in exploring individual differences in motives, intentions and process strategies used by students in various learning contexts (Prat-Sala and Redford, 2010). This interest is based on previous work by Marton and Säljö (1976a, 1976b), who used qualitative analysis to examine differences in students’ approaches to written texts. The literature differentiates between a deep and a surface strategy to learning (Biggs, 1987; Golightly and Raath, 2015; Yongjun and Reese, 2014; Vanthournout et al., 2014). A deep learning strategy is characterized by the learner’s search for the meaning of the information, and by intrinsic motivation. Deep learners try to achieve full comprehension of underlying arguments by using different strategies such as elaborating ideas, problem solving, critical thinking, self-management, seeking evidence, and associating new knowledge with old (Biggs, 1987; Entwistle et al. 2003). A deep learning strategy is often followed by success, and high quality learning outcomes (Abdulghani et al., 2014; Ellis et al. 2008a, 2008b, Phan, 2013; Salamonson et al., 2013).

Surface learning strategy is characterized by learners’ extrinsic motivation, accompanied by the will to invest minimal time and efforts in order to achieve minimal requirements, without actually seeking for the meaning of information (Biggs, 1993). Surface learners learn only important and essential facts, applying minimum study efforts (Biggs, 1987). They attempt to avoid failure and use rote strategies such as mere unreflective reading and memorization (Entwistle et al., 2003). Thus, meta-cognitive skills are generally not involved in their learning process (Biggs, 1993). Several studies undertaken in the Library and Information Science (LIS) arena have focused on learning strategies. Aharony (2009), who explored LIS students’ attitudes towards Web 2.0, concluded that students who have deep learning strategies have higher motivation to learn Web 2.0 applications and environments, and make greater use of them than do surface learners. In another study (Aharony, 2014), findings indicated that students who were characterized as deep learners have more positive perceptions of mobile-learning than students who were characterized as surface learners.

Hypotheses

Assuming that relative advantage, learning strategies, and comprehension may predict students’ reading preferences, the underlying assumptions of this study are:

H(1) Students will prefer printed academic materials over electronic ones.

H(2) The higher the advantage of academic e-reading seen by the students, the more they will prefer to use electronic devices.

H(3) The higher students’ comprehension of academic electronic materials is, the greater their preference for using electronic materials.

H(4) Both deep and surface learners will acknowledge the relative advantage of academic electronic materials.

Method

Data collection

The research was conducted in Israel during the first semester of the 2015 academic year and involved LIS students of a Department of Information Science in Israel. The researchers entered six different classes and asked students to complete the questionnaire; 177 responses were received.

Data analysis

Of the respondents 52 (29.37%) were male and 125 (70.62%) were female. Their average age was 31.41 years old. As for their enrollment by educational level, 85 (48%) were undergraduates, and 92 (52%) were MA students, and PhD students.

Measures

Three questionnaires were used: personal details, relative advantage, learning strategies, and two further questions that focused on reading preferences (see Appendix 1). The relative advantage questionnaire was based on Rogers’ theory, and modified for this study. It was previously used by Tšoenyo and Wole (2012). The questionnaire consisted of three statements rated on a five-point Likert scale (1 = strongest disagreement; 5 = strongest agreement), focusing on relative advantage of electronic materials. Cronbach’s Alpha coefficient was .87. The learning strategies questionnaire consisted of 14 statements rated on a 5-point Likert scale (1 = strongest disagreement; 5 = strongest agreement). This questionnaire, which was previously validated (Aharony, 2014), consists of two factors, deep and surface learning strategies, with seven items for deep learning and seven for surface learning. Cronbach’s Alpha coefficients were .84 and .77, respectively. The reading preferences questions were taken from Bar-Ilan and Shalom’s questionnaire (2010). Each question was rated on a seven-point Likert scale (1 = strongest disagreement; 7 = strongest agreement) and related to overall preferences, perceived comprehension, reading mode and processing. Here we report only on preferences and comprehension.

Results

The relative advantage of reading from electronic devices was measured on a scale of 1 to 5. Its mean was M = 3.16 (SD = 1.15). In order to examine whether there are differences between students’ reading habits, a MANOVA was performed. Means, standard deviations, and the MANOVA analysis are presented in Table 1. Comprehension and preference were on a scale of 1 to 7.

Table1.

Means, standard deviations and MANOVA analysis on each measure separately of students’ reading preferences.

Measures	Printed materials		Electronic materials		F(1,168)	Eta²
Measures	M	SD	M	SD	F(1,168)	Eta²
Comprehension	5.15	1.91	3.64	1.77	52.96***	.24
Preference	5.18	1.89	4.19	1.95	13.87***	.08

***

p < .001.

In order to examine H(1) and to see whether there are differences between students’ reading preferences a MANOVA with repeated measurements was performed. The MANOVA revealed a significant difference between students’ reading preferences (F (2,174) =30.58, p < .001, η² = .26). Means, standard deviations, and the MANOVA analysis are presented in Table 1.

Table 1 shows significant differences relating to students’ academic reading preferences. It seems that students prefer printed materials to electronic ones, and that their level of comprehension is higher with printed materials. Pearson correlations were performed to examine the relationship between electronic relative advantage (relative advantage of reading academic articles on a desktop computer, laptop or a mobile device), electronic comprehension, printed comprehension, and printed and electronic preferences and the results are presented in Table 2.

Table 2.

Pearson correlations between electronic relative advantage, electronic comprehension, printed comprehension, and printed and electronic preferences measures (n = 177).

Measures	Electronic relative advantage	Printed compr.	Electronic compr.	Printed pref.
Electronic relative advantage
Printed comprehension	−.24***
Electronic comprehension	.41 ***	−.00
Printed preference	−.23**	.62 ***	−.25***
Electronic preference	.47 ***	−.42***	.50 ***	−.62***

p < .01, ***p < .001.

Table 2 shows that significant positive correlations were found between the reported advantage of e-reading/comprehension when e-reading and preferences for e-reading. The higher the level of advantage students perceive in reading electronic materials, the higher the levels of electronic comprehension and preference for electronic materials they report. Significant negative correlations were found between the reported advantage of e-reading and comprehension of and preference for printed materials. Thus, the higher the students’ levels of the reported relative advantage of e-reading, the lower their comprehension when reading from print and the lower their preference for printed materials. Further, a significant positive correlation was found between comprehension of printed materials and preference for reading from print. Therefore, we may conclude that the higher the level of comprehension of print that students report, the more they prefer printed materials. Moreover, a significant negative correlation was found between comprehension of printed materials and preference for e-reading. In other words, the higher the level of comprehension of printed materials the students report, the less they prefer electronic materials. Similarly, a significant negative correlation was found between comprehension of electronic materials and preference for print. In contrast, a significant positive correlation was found between comprehension of electronic material and electronic preference. We may understand that the more students comprehend academic electronic materials, the greater their preference for reading from electronic devices. Another negative significant correlation was found between printed and electronic preferences. Thus, the higher the students’ levels of preference for print, the lower their electronic preference. Pearson correlations were also performed to examine the relationship between learning strategies and printed and electronic preferences. These are presented in Table 3.

Table 3.

Pearson correlations between learning strategies, and printed and electronic preferences (n = 177).

Strategy	Electronic relative advantage	Printed compr.	Electronic compr.	Printed pref.	Electronic pref.
Deep learning	.55***	.11	.14	.10	.07
Surface learning	.43***	.12	.12	.06	.05

***

p < .001.

Table 3 confirms that significant positive correlations were found between electronic relative advantage and deep and surface learning. Thus, the higher students’ level of relative advantage, the higher their deep and surface learning strategies are.

In order to examine the relationship between demographic characteristics (gender and education) and research variables, a one-way MANOVA was performed. The MANOVA revealed a significant difference between males and females regarding electronic academic reading preference (F (1,148) = 4.75, p < .05, η² = .03). It seems that males’ electronic academic reading preference is higher (M = 4.71, SD = 1.88) than that of females (M = 3.98, SD = 1.96). The MANOVA also revealed a significant difference concerning electronic relative advantage of graduate and undergraduate students (F (1,167) = 8.86, p < .01, η² = .05). It seems that graduate students have a higher level of electronic relative advantage (M =3.42, SD = 1.08) than undergrads (M =2.90, SD = 1.18). Pearson correlations were performed in order to examine the relationship between age and research variables. A significant positive correlation was found between age and electronic relative advantage (r = .24, p < .01). In other words, older students have a higher level of electronic relative advantage.

We also conducted a hierarchical regression using electronic preference as a dependent variable. The predictors were entered as five steps: (1) personal variables (age, gender, education); (2) learning strategies (deep and surface); (3) relative advantage; (4) comprehension; (5) interaction between electronic comprehension X age. The entrance of the first four steps was forced, while the interaction was done according to its contribution to the explained variance of electronic preference. The regression explained 47% of electronic preference. Table 4 presents the standardized and unstandardized coefficients of the hierarchical regression of electronic preferences.

Table 4.

Hierarchical regression coefficients of electronic preference (n =177).

Predictors	B	β	R²	ΔR²
1. Age	−.02	−.12	.05	.05
Gender	−.67	−.16
Education	.71	.18
2. Deep learning	.03	.01	.06	.01
Surface learning	.47	.09
3. Relative advantage	1.4	.61***	.40***	.34***
4. Comprehension	.27	.24***	.44***	.04**
5. Age X compr.	−.39	−.19**	.47***	.03**

p < .01, ***p < .001.

Table 4 shows that neither personal variables nor learning strategies variables contributed to the explained variance of electronic preference.

The third step introduced the relative advantage variable, and contributed significantly by adding 34% to the explained variance of electronic preference. The beta coefficient was positive: respondents who reported higher levels of relative advantage appear to have higher preferences for electronic materials.

The fourth step that introduced the comprehension variable contributed significantly by adding 4% to the explained variance of electronic preference. The beta coefficient was positive: respondents whose level of comprehension is higher appear to have higher preference for electronic materials.

In the fifth step, the interaction between age X comprehension was entered and added 3% to the explained variance of electronic preference. The interaction age X comprehension is presented in Figure 1.

Figure 1.

Interaction of age X comprehension.

Figure 1 shows that no correlation was found among older students between comprehension and electronic preference (β =.03, p >.05). However, significant correlation was found among younger students between comprehension and electronic preference (β =.45, p <.001). Thus, especially among young students, the higher their level of electronic comprehension, the higher their electronic preference.

Discussion

Based on the premises of the Diffusion of Innovation Theory (Rogers, 1995), and on a learning strategies perspective (Marton and Säljö, 1976a, 1976b), the present study explored the extent to which relative advantage, deep and surface learning strategies, and comprehension variables, explain students’ academic reading preferences. By addressing these questions, this article makes a number of theoretical contributions:

Findings echo previous studies revealing students’ academic preferences for printed materials.

Findings expand the current reading preferences literature by focusing on personal variables that may affect students’ academic electronic reading preferences.

Findings confirm that the relative advantage variable, as well as the comprehension variable, affects students’ academic electronic reading preferences.

Addressing the research hypotheses, H(1) was accepted and indicates that students prefer academic printed materials over electronic ones. This finding duplicates previous studies that presented this preference (Ackerman and Goldsmith, 2011; Allen, 2008; Ben-Yehudah and Eshet-Alkalai, 2014; Foasberg, 2014; Noyes and Garland, 2005; Pinto, 2014; Redden, 2009; Spencer, 2006). In addition, it was also found that students’ level of comprehension is higher with printed materials. We may assume that, perhaps, students are more used to the traditional learning materials. Thus they feel more comfortable while reading printed academic learning materials. Another reason for this finding may be their feeling that it is easier for them to concentrate when using printed learning materials, as there are fewer distractions and they may feel less disoriented. In addition, perhaps they confront difficulties such as fatigue, discomfort from eye strain, or lack the simple abilities to annotate, highlight, underline, and take notes while dealing with learning materials in electronic format.

The current result can also be associated with other studies that explored researchers’ reading preferences (Halevi et al., 2015; Tenopir, 2003; Tenopir and King, 2002; Tenopir et al., 2009, 2012). These studies indicated a preference for printed materials. We may conclude that, though the beginning of the 21st century offers many new technological innovations, both students and academics still generally prefer reading academic scholarly articles in print.

Results pertaining to H(2) show that the hypothesis was accepted as well. The more students perceive the relative advantages of academic electronic materials, the more they use them. The regression analysis shows that the relative advantage was the variable that contributed more than other variables to the explanation of the dependent variable (electronic preference). H(2) is based on Rogers’ model that attempts to predict individuals’ adoption of new technologies. Findings reveal that when students perceive the benefits of learning from academic electronic materials, they will prefer to use them. Therefore, if instructors would like to integrate more electronic materials within their teaching environment, they should expose their students to the advantages and benefits this environment offers. Another interesting result is that the higher the students’ level of electronic relative advantage, the lower their preference for print. This finding strengthens the previous one about students’ preferences. In addition, findings can be linked to a previous study (Shonfeld and Aharony, 2015) that suggested that the higher relative advantage students perceive from ICT use, the more they use it.

H(3) was accepted, revealing that the higher students’ electronic comprehension is, the greater their electronic preference. This finding presents a new variable: comprehension that contributes to the understanding of students’ reading preferences. In other words, when students understand the articles assigned to them in an electronic format, they prefer this format. Furthermore, the interaction between age X comprehension showed, that especially among young students, a higher level of electronic comprehension is associated with a higher preference for material in electronic format. These findings are important for instructors who would like to use electronic materials in their classes. They should be aware that the materials should be understandable and not too complicated. By presenting simpler materials, they may enable more students to read from the electronic source, and thereby reduce their level of uneasiness or fear of using new technological platforms. In addition, the finding that this preference takes place especially among young students should cause instructors to try to convince older students that electronic materials are not so complicated and can be understandable and used for learning.

As a whole, we should remember that the study findings demonstrate that students prefer academic printed materials over electronic ones. However, we see that H(4) was confirmed, revealing that both deep and surface learners admit to the relative advantage of academic electronic materials. This finding echoes Biggs’s (1993) approach and acknowledges that deep learners recognize the advantages of electronic materials as part of their search for a fuller understanding of the learning materials. Surface learners understand that electronic materials may facilitate their process of learning, thus supporting their intention to invest minimal time and effort in order to achieve minimal requirements.

Another intriguing finding shows that males’ academic electronic preference is higher than that of females’. This finding is similar to a previous one that showed that males prefer news sites to print newspapers (Cherian and Jacob, 2013), and to other studies that revealed that males are more technology oriented than females (Elliott and Hall, 2005; Todman, 2000; Venkatesh and Morris, 2000). A final appealing finding shows that the older students are, the higher their level of electronic relative advantage. This finding addresses the fact that older students are not ‘digital natives’ and yet still consider the advantages of learning from electronic materials. Perhaps younger students accept this electronic environment as a natural one and, as a result, they do not take into account its relative advantage.

In conclusion, the current study found that students prefer printed academic learning materials over electronic ones and highlights the importance of relative advantage and comprehension as variables that may affect students’ electronic reading preferences.

This study has some limitations. The first is that in order to gain a broader perspective, a future study should include a larger number of students from different disciplines. In addition, in a follow up study, we would also like to include faculty and see whether there is a difference between these two kinds of populations. As the current study focused only on Israeli students, we suggest that in order to have an international perspective of students’ reading preferences, the study should be conducted in other countries as well. Further, we assume that it will be a good idea to continue exploring the interesting result that indicated that older students have a higher level of electronic relative advantage.

Footnotes

Appendix: Academic reading preferences questionnaire

Enclosed is a questionnaire that focuses on your academic reading preferences.

The questionnaire is anonymous.

We appreciate your participation.

Sincerely,

Prof. Bar-Ilan and Dr Aharony

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author biographies

Noa Aharony is professor and head of the Department of Information Science at Bar-Ilan University in Israel. She received her PhD in 2003 from the School of Education at Bar-Ilan University. Her research interests are in education for Library and Information Science, information literacy, technological innovations and the LIS community, and Web 2.0. Professor Aharony is a member of the editorial boards of the Journal of Librarianship and Information Science, Online Information Review, and IJELLO.

Judit Bar-Ilan is professor at the Department of Information Science at Bar-Ilan University in Israel. She received her PhD in Computer Science from the Hebrew University of Jerusalem and started her research in Information Science in the mid-1990s at the School of Library, Archive and Information Studies of the Hebrew University of Jerusalem. She moved to the Department of Information Science at Bar-Ilan University in 2002. She is a member of the editorial boards of JASIST, Scientometrics, Journal of Informetrics, PLoS ONE, Cybermetrics, and Online Information Review. Her areas of interest include: informetrics, information retrieval, Internet research, information behavior and usability.

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