The digital divide: conveying subtlety in online communication


In this study, we tested the idea that people born after online technology became a part of daily life (“digital natives”) interpret online communication differently when compared with those born before the Internet age (“digital immigrants”). Specifically, across two experiments, 213 participants recruited from a crowdsourcing site were presented with 16 text messages that either included or did not include a line break or a period, in a fully crossed 2 × 2 design. Both immigrants and natives rated the messages on an affect scale and indicated their confidence in their rating. In a third experiment, 72 participants produced responses to 16 text message prompts each, and these responses were coded for line breaks and periods to test whether production of these cues varies between natives and immigrants. The results suggest that immigrants and natives are alike in how they interpret messages, but that natives are more sensitive to minor linguistic cues, especially the use or nonuse of a period in a text message, considering this cue to carry more negative affect than immigrants do. This suggests that, even in cases in which immigrants make use of the same communication technology to the same extent as natives, they still have a digital “accent,” and fail to make subtle distinctions that are meaningful to natives. We further discuss how such subtle differences could impact online classroom communication, particularly between students of different generations and between the students and the teacher. As texting becomes increasingly used as a classroom management or communication tool, older students and faculty must be sensitive to the fact that younger students may consider the use of periods to signal negative affect and may respond differently to such messages than intended by the writer. We issue a call for more research exploring how the use of technology, and even subtle cues, may impact classroom dynamics, particularly in classrooms made up of mixed age groups.


The story about how technology has affected people and society is a long one. The invention of writing and mechanical calculation, for example, allowed people to offload some of their cognitive processes, such as long-term memory and calculation, onto external systems. More recently, the rise of the World Wide Web and pocket-sized devices for accessing it accelerated these cognitive shifts. The advent of the Internet permanently altered the landscapes of commerce, banking, socialization, and more, and higher education is no exception.

Although many viewed these changes as an unalloyed good, there is an equally long history of concern about these developments. Around 360 BCE, Socrates warned Phaedrus that “If men learn this [writing], it will implant forgetfulness in their souls; they will cease to exercise memory because they rely on that which is written, calling things to remembrance no longer from within themselves, but by means of external marks” (Hackforth 1952, p. 157). More recently, writers such as Nicholas Carr have expressed concern that the Internet was having detrimental effects on cognition, arguing that the superficial nature of the Web diminishes the capacity for deep concentration and contemplation (Carr 2008, 2010), cornerstones of higher education.

Arguably, one of the greatest benefits of the Internet is the way it has enabled communication between and among people, through texting, social media (e.g., Facebook), microblogging (e.g., Twitter), and more. However, even this benefit has a downside: There is evidence that even minor cues such as the presence or absence of a single emoji (even one that is not a face; Riordan 2017) mattered a great deal in how messages were perceived by recipients. As faculty increasingly integrate such online communication platforms into their classrooms (in estimates as high as 80%, found in a survey by Moran et al. 2011), an exploration of how even subtle cues may affect the perception of a message should be explored. The current study was undertaken to assess whether two such subtle cues, line breaks and periods, affect the perception of messages as well as whether this perception differs between older and younger age groups, who show consistently different preferences in how they use online communication.

Literature review

Since an extensive online world has only existed for about 20 years, some have argued that younger people have been more affected by it than those who reached adulthood before the rise of the Internet. This idea was given more definite form in an article published in 2001 by educator Marc Prensky. He coined the term “digital native” to describe those born after the mid-1980s and for whom the Internet and other online services have always been a part of their lives (Prensky 2001a). He goes so far as to claim that the brains of digital natives are “physically different” from those of older generations (Prensky 2001b, p. 1), whom he labels digital immigrants.

One might assume, therefore, that there exists a scientific consensus about the effects of networked and digital technology on these so-called digital natives, and the “digital divide” (Waycott et al. 2010) that exists between them and earlier generations. Surprisingly, however, these claims have garnered little support from researchers. Presnky’s sweeping claims have been criticized by many as overly simplistic. Both Bennett (2012) and Jones and Shao (2011) have emphasized the dangers of making broad generalizations about entire generations. Bennett et al. (2008) and Smith (2012) have questioned the nature of the debate itself, and in particular, the urgent call for making basic changes in instructional techniques. Even the terms employed by Prensky have been criticized as problematic. On the one hand, the term “native” implies that certain skills may be innate rather than learned (Palfrey and Gasser 2011). On the other, the negative connotations of the term “immigrant,” such as being unprepared for life in a new culture, have been noted (Bayne and Ross 2011). Neil Selwyn, for example, has argued that the engagement of young people with digital technology is “varied and often unspectacular” (Selwyn 2009, p. 364; see also Jones et al. 2010; Kennedy et al. 2008). And instead of being tied solely to one’s age, comfort with digital technology may be more a function of access and opportunity (Brown and Czerniewicz 2010), gender and education level (Demirbilek 2014, Helsper and Eynon 2010; Kennedy et al. 2010), and different types of users within the “Net generation” itself (van den Beemt et al. 2010).

It is worth nothing that Prensky himself has retreated somewhat from his initial claims, preferring to refer instead to the “digital wisdom” possessed by children and young adults (Prensky 2012). Nevertheless, many others have continued to raise generational concerns particularly with regard to traditional educational practices, which were originally designed for digital “immigrants” (i.e., those born before the mid-1980s; Prensky 2001a). Some have suggested that instruction would need to be substantially altered to accommodate the different learning styles of digital natives (e.g., Dede 2005). These and other issues concerning digital natives have been discussed widely in popular culture (e.g., Baron 2010; Negroponte 1995; Palfrey and Gasser 2008). A great deal of this commentary has been quite critical of younger people, as can be seen in books with alarmist titles like The Dumbest Generation: How the Digital Age Stupefies Young Americans and Jeopardizes Our Future (Bauerlein 2008).

Not surprisingly, later research on this topic painted a more nuanced portrait of the impact of digital technology. Survey research in Europe suggests that members of the first digital generation regard themselves as pioneers whereas the generation coming of age behind them is more likely to simply accept the digital world as a given (Fortunati et al. 2017). Attention has also shifted to the so-called gray divide that exists between younger and older adults (Friemel 2016). Finally, it may be the case that specific aspects of digital technology show greater generational differences. Synchronous (i.e., real time) communication, such as video phone calls and texting conversations, seem to be preferred by digital natives whereas digital immigrants show a preference for asynchronous communication (e.g., social networking sites and email) in which communication occurs at the convenience of the user (Taipale 2016). These differences may affect how these groups are able to interact effectively in both online and on-the-ground classrooms.

Technology in the classroom

The debate regarding the effect of digitalization on education is not without merit. Digital platforms are invading higher education classrooms more than ever: in 2011, 57% of those who took a class in person used a laptop, smartphone, or tablet during class time (Pew Research Center 2011). In an increasing number of cases, the classroom itself is online: 46% of college students between 2001 and 2011 took an online course. Furthermore, this format may be the classroom of the future: Of those who reported taking an online course, 39% reported that the education provided by the online course was equal in value to that received in a classroom environment (Pew Research Center 2011). It is thus not surprising that 50% of college presidents surveyed by Pew Research Center (2011) predicted that most of their students would take classes online and most undergraduate textbooks would be digital by 2020. Indeed, in Fall 2014, nearly a quarter of undergraduate students in the United States were distance learners (taking online courses, or using other online methods of communicating with instructors), with 12% exclusively enrolled in distance courses. This change is not necessarily perceived as being for the better: The majority of surveyed college presidents reported that plagiarism in student papers was on the rise, and of those who said it was increasing in prevalence, 89% blamed computers and access to the Internet (Pew Research Center 2011).

Paralleling these developments is a trend of increasing numbers of nontraditional age students. In Fall 2013, only 53% of undergraduate students enrolled full-time in a public or private college were within the traditional ages of 18–22. Another 27% were 23–29 years old, 11% were 30–39 years old, and 9% were age 40 or older (National Center of Education Statistics 2014). In other words, traditional age students are no longer the vast majority; demographics have shifted such that older students are increasingly represented in higher education. As education becomes increasingly digitalized and student demographics become increasingly older, it is also increasingly important to explore the experiences that students of different ages have in online classrooms, particularly in light of evidence showing that natives and immigrants have different preferences regarding how to use technology to communicate with others (Taipale 2016).

Digital natives and immigrants in the classroom

Although several technology behaviors that are different between natives and immigrants are not necessarily part of the classroom, such a generational divide in technology use suggests ramifications that extend to the classroom. In a survey, Jones et al. (2010) found that younger students (i.e., the “Net generation”) considered Internet access more important for accessing course information and study materials than older students. Younger students were also more likely to upload and download materials from the Internet than older students, particularly TV or video, and were more likely to use social networking sites (although not necessary for class purposes).

Although some instructors perceive the Internet to be a distraction from class activities and content, other faculty have been eager to adopt different forms of online communication for their classrooms. Ajman and Hartshorne (2008) found that 32% of university professors surveyed felt that the use of social media led to greater student satisfaction in classes, and 56% felt it enhanced interaction among classmates. Both McCarthy (2010) and Junco et al. (2011) found that students who used Facebook and Twitter (respectively) for course activities felt more engaged in the course. Furthermore, the use of online components of the classroom enhanced student grades in a study by Pasek et al. (2009).

Many digital natives are more technologically savvy than their digital immigrant peers, conducting multiple concurrent text message conversations, using photo tags, hashtags, streaming video, audio mash-ups, hypertext, podcasts, and the like. Those who are not as adept with technology (often including the teacher him or herself, as in Vie 2008) may be unable to adequately understand or engage in these activities. As educational delivery becomes increasingly online, communication between classmates, and between students and the teacher, is moving online as well, through discussion forums, online grading, blog posts, emails, and more. Indeed, the utility of microblogging platforms such as Twitter, communication forms such as texting, and other forms of digital software in both online and on-the-ground classrooms from elementary to college education has been widely researched over the past decade, although the conclusions have varied (e.g., Grosseck and Holotescu 2008; Librero et al. 2007).

The current study

Much of the debate about digital natives has been focused on their cognitive abilities and purportedly different educational needs, and the topic of digital communication has remained strangely underexplored, even though Prensky (2004) specifically claimed that differences in communication style have arisen as well. An important question, therefore, is whether this digital divide extends to computer-mediated messages. If it does, this would have far-reaching implications, since so much communication now occurs via texts, tweets, e-mail, and status updates. Furthermore, as educational delivery becomes increasingly online, communication between teachers and students is likely to become increasingly digital as well.

These disembodied modes of interaction are impoverished with regard to the expression of affect, and as a result, new conventions have evolved to disambiguate the actual meaning of messages (Kreuz and Roberts 2017). Digital natives, in particular, may make use of communicative conventions that are simply unknown, or are at best relatively unfamiliar, to digital immigrants. This mutual lack of understanding suggests that communication across a digital divide may be even more problematic than it typically would be between older and younger language users.

Although emoticons, kaomoji, bitmoji, stickers, and emoji have emerged as new aspects of online communication (e.g., Sakai 2013), other, more subtle differences have been proposed as well. In the realm of texting, for example, it has been claimed that the use of periods can signal irritation or hostility (Crair 2013). Gunraj et al. (2016) found that text messages that ended in a period (versus text messages without a period) were considered to be less sincere, which is perhaps why 61% of text messages do not end in periods (Ling and Baron 2007). Gunraj et al. (2016) suggest that such results demonstrate the significance to the reader of even minor elements such as the inclusion of a period to a message. Crair (2013) asserts that the period has been replaced with a line break, such that rather than writing two sentences divided by a period, one writes two sentences as two different texts:

I am much more likely to type two separate messages without punctuation:

sorry about last night

next time we can order little caesars

Than I am to send a single punctuated message:

I’m sorry about last night. Next time we can order Little Caesars.

And, because it seems begrudging, I would never type:

Sorry about last night.

Next time we can order little caesars. (Crair, 2013, n.p.)

In the current study, the meaning of a period at the end of a text message, as well as the existence of a line break in the message, is explored. As platforms such as and other group messaging platforms become more popular in educational contexts, a better understanding of possible generational differences in writing and interpreting text messages is necessary. While many researchers have argued for a digital divide based on access, opportunity, and other factors, here we explore whether this generational divide is a factor when access to and familiarity with digital platforms is the same for all participants. It is hypothesized that a generation-specific digital divide between natives and immigrants will lead to different affective interpretations of line breaks and periods in text messages.

Experiment 1


One hundred forty-two participants (70 male, 72 female) with a mean age of 33.05 years (SD = 9.43) participated in this online experiment. Each participant was compensated $1.00. The experiment was posted to Mechanical Turk and was visible to workers in North America who had a 95% or higher approval rating. Mechanical Turk is an online crowdsourcing platform for which researchers can post tasks for users to complete for a small amount of money. As previous studies have found that Amazon Mechanical Turk ( data provide a much broader age range than traditional student populations (e.g., Riordan and Kreuz 2010), this study utilized the Mechanical Turk population in order to ensure that the sample would vary enough to conduct age comparisons. Mechanical Turk was also used to ensure a sample of participants who had access to and familiarity with digital platforms.


Sixteen text message exchanges were generated for this experiment. Each exchange consisted of one text message followed by a positive or neutral response. The response either contained a line break or not, and a period or not, in a fully crossed design which generated four possible responses to each text message (see Appendix, for an example). In this manner, four sets of each text message exchange were generated in which each text message was followed by one of the four possible responses.

Each of the four sets contained four instances of a line break (present or absent) by period (present or absent) combination. As such, a single set had four text message exchanges with a line break present and a period present, four text message exchanges with a line break present and a period absent, four text message exchanges with a line break absent and a period present, and four text message exchanges with a line break absent and a period absent. Thus, each participant saw each condition four times.


After agreeing to participate in the experiment, participants were asked to specify age, gender, and race. These questions were followed by one set of 16 text message exchanges, randomly selected out of the four prepared sets.

Participants rated all 16 exchanges in the set on a scale of 1 (very negative) to 12 (very positive), and indicated how confident they felt about their rating on a scale of 1 (not confident at all) to 7 (very confident). Lastly, participants answered questions regarding the frequency with which they text on a mobile device (1 = never, to 7 = daily) and how often they use common text abbreviations (1 = never, 7 = very frequently). This last question was included as a way of assessing the participants’ knowledge of “net speak” and its conventions.


Because each of the four sets of text message exchanges included all conditions, they were expected to be comparable to each other in ratings. A linear mixed-effects model was generated to determine whether the four sets differed from each other in affect ratings. No differences in affect ratings were found among the four sets (− 0.003, p = 0.66; intercept = 2.02), as expected.

The effect of a period and a line break for all participants

To generate an assessment of the overall effect of a period and a line break on affect ratings, a linear mixed-effects model was necessary. A linear mixed-effects model accounted for the fact that all participants saw all possible conditions of a period and a line break, but saw only one of the four possible response conditions for each individual text message exchange. All scores were log-transformed to fit model assumptions. The model assessed whether the presence or absence of a period and a line break affected ratings on the negative–positive affect scale. A main effect was found for a period (− 0.05, p < 0.01) but not a line break (− 0.02, p = 0.29), nor the combination of the two variables (0.03, p = 0.39; intercept = 2.04). Across all participants, the presence of a period led to more negative affect ratings.

Digital divide

The primary hypothesis of this paper is that younger people interpret text message exchanges differently than older people. In order to assess this hypothesis, participants were split into two groups. The term “digital native” has been applied to those born after 1980 (Palfrey and Gasser 2008), after 1982 (Helsper and Eynon 2010; Oblinger 2003), after 1985 (Laverne 2014), and before 1991(Oblinger and Oblinger 2005), although most determine the divide exists in the mid-1980s. In the current study, 1985 was chosen as the dividing year. It should be noted, however, that these estimates really only apply to First World countries in which digital technologies became widespread in the mid-1990s (International Telecommunication Union 2013), which describes our sample.

Participants were split into digital natives (those born in 1985 or later) and digital immigrants (those born in 1984 or earlier), which created a dataset with 43.7% digital natives and 56.3% digital immigrants. A linear mixed-effects model was conducted to determine whether the groups rated the text message exchanges differently on the negative–positive affect scale. A main effect was found such that digital natives rated messages as more negative than digital immigrants (0.14, p < 0.001; intercept = 1.93). This result means that younger people found the text message exchanges to be more negative overall than older people did. A second linear mixed-effects model found no difference in these ratings by sex (0.09, p = 0.08; intercept = 1.80).

For digital natives, the presence of a period led to more negative affect ratings (− 0.07, p < 0.05), but a line break had no effect (− 0.03, p = 0.42) and neither did a combination the two variables (0.03, p = 0.50). For digital immigrants, there was no effect of the presence of a period (− 0.04, p = 0.14), a line break (− 0.02, p = 0.50), or both (0.02, p = 0.57; intercept = 2.10) on affect ratings. This result means that younger people interpreted messages with a period more negatively than messages without a period, but older people did not have this same interpretation.

Texting experience

Texting experience may affect the perception of text message exchanges, as those who text more frequently may become more confident about their abilities to correctly interpret text messages. The digital immigrants were significantly more confident in their affect ratings (0.30, p < 0.05; intercept = 5.51) than natives were. However, there was no significant difference between the groups in rates of self-reported text messaging frequency (Natives M = 6.43, SD = 1.41; Immigrants M = 6.17, SD = 1.62; two-tailed t(142) = 1.01, p = 0.32), or in rates of self-reported text abbreviation use (Natives M = 5.00, SD = 1.82, Immigrants M = 4.17, SD = 2.21; two-tailed t(142) = 1.53, p = 0.13). These results suggest that although both natives and immigrants had the same level of access to and familiarity with digital platforms, immigrants felt more confident with their ability to correctly interpret affect in the messages.


Despite having the same access to and familiarity with digital communication, digital natives consistently rated text messages as more negative than digital immigrants and also interpreted the presence of a period with more negativity. In Experiment 2, we test whether this same effect is true when the messages themselves are negative in valence.

Experiment 2

Experiment 2 is a replication of Experiment 1 with a new group of participants using the same stimuli in the same sets but with negative or neutral responses in the text exchange, rather than the positive or neutral responses in Experiment 1. In addition, participant understanding of texting abbreviations was assessed to complement Experiment 1’s question about self-reported abbreviation use.


Seventy-one participants (42 male, 29 female) with a mean age of 31.88 years (SD = 9.97) participated in the online experiment via Mechanical Turk. Each participant was compensated $1.00.


The same text messages used in Experiment 1 were again used in the current experiment. However, the responses to the text messages were changed to be negative or neutral, rather than positive or neutral as in Experiment 1 (see the Appendix).


The experiment was conducted in the same manner as Experiment 1. In addition, after rating the exchanges, participants were presented with eight texting abbreviations and asked to provide translations for each. These text abbreviations were: l8r (later), nm (never mind or not much), pos (positive, piece of shit, or parent over shoulder), tmi (too much information), gtg (got to go), and atm (at the moment, automatic teller machine) and were drawn from Kovaz et al. (2015). This measure was included to assess participants’ familiarity with conventions used in texting to accompany Experiment 1’s assessment of participants’ self-reported frequency of using the abbreviations.


Linear mixed-effects models were conducted in the same manner as in Experiment 1. All scores were log-transformed to fit model assumptions. As in experiment 1, no difference among the four sets was found (− 0.006, p = 0.53; intercept = 2.02), as expected.

The effect of a period and a line break for all participants

The presence of a period led to more negative affect scores (− 0.07, p = 0.03), but affect scores were not affected by a line break (− 0.03, p = 0.28) or the presence of both (0.06, p = 0.17; intercept = 1.05). These results are the same as in Experiment 1, such that across all participants, the presence of a period led to more negative affect ratings.

Digital divide

Participants were again split into natives and immigrants as in Experiment 1, creating a dataset with 45.6% natives and 54.5% immigrants, comparable to Experiment 1. Digital natives rated messages as more negative than digital immigrants (0.16, p < 0.005; intercept = 2.13). No effect of sex on affect ratings was found (− 0.004, p = 0.94; intercept = 2.11). These results are also the same as in Experiment 1, such that younger people found the text message exchanges to be more negative overall than older people did.

For digital natives specifically, there was no effect of the presence of a period (− 0.05, p = 0.27), or a line break (0.02, p = 0.70), but the combination of the two variables led to more negative affect scores (− 0.002, p < 0.05; intercept = 2.12). For digital immigrants specifically, there was no effect of the presence of a period (− 0.09, p = 0.06), a line break (− 0.09, p = 0.07), or both (0.11, p = 0.07; intercept = 1.99), a finding that replicates the results from Experiment 1. This result means that younger people interpreted messages with a period and line break more negatively than messages without them, but older people did not have this same interpretation.

Texting experience

Digital natives (M = 6.54, SD = 1.03) reported texting significantly more often than digital immigrants (M = 5.77, SD = 2.01; two-tailed t(71) = 2.12, p < 0.05); however, both groups texted quite frequently. As in Experiment 1, no significant differences were found between natives (M = 2.90, SD = 1.81) and immigrants (M = 2.93, SD = 1.95) in self-reported use of text messaging abbreviations (two-tailed t(71) = − 0.07, p = 0.95).


In Experiment 1, no difference was found between immigrants and natives self-reporting their use of texting abbreviations. In the current experiment, an assessment of the understanding of these abbreviations was carried out by asking participants to translate a set of common abbreviations. Translations of the abbreviations were coded both strictly and loosely. When coded strictly, the exact wording had to match; when coded loosely, the approximate meaning had to match. For example, when coded strictly, the abbreviation “atm” would be correct if the participant wrote “at the moment.” When coded loosely, “at this minute” would also be correct. However, “after the meeting” would be incorrect in both codings.

In each case, the number of abbreviations that were correctly interpreted by each participant was calculated and served as the dependent variable. When using loose coding, natives (M = 5.91, SD = 0.51) were significantly more accurate when translating abbreviations than immigrants (M = 5.31, SD = 1.08), t(71) = 2.68, p = 0.01 (two-tailed). This difference was even more significant when using strict coding (t(71) = 3.54, p = 0.001, two-tailed), with means of 4.26 (SD = 0.62) for natives and 3.56 (SD = 0.73) for immigrants. This result means that although natives and immigrants self-reported that they used texting abbreviations with the same frequency, natives were more accurate at interpreting such abbreviations.


Although both groups were frequent texters, digital natives again rated text messages as more negative than digital immigrants, and perceived more negativity when a period and line break were present than immigrants did. Digital natives were also more accurate at interpreting common texting abbreviations, despite the fact that both groups self-reported frequently using such abbreviations.

In combination, Experiments 1 and 2 allow the conclusion that digital natives interpret a period to be a marker of negative emotion, while digital immigrants do not. The experiments also allow the conclusion that a line break does not carry strong affective content.

Experiment 3

Experiment 3 tests whether the same effect is found for a period when the participant is the writer of the message, rather than a reader who is unknown to the writer. In other words, digital natives use a period to interpret affect, but do they use a period to produce affect? And does this also vary between natives and immigrants?


Seventy-two participants (44 male, 28 female) with a mean age of 33.18 years (SD = 10.89) participated in the online experiment via Mechanical Turk. Each participant was compensated $1.25.


The same text messages used in Experiments 1 and 2 were again used in the current experiment, except the responses to the messages were removed (see the Appendix).


The experiment was conducted in the same manner as Experiments 1 and 2, except instead of rating an experimenter-provided response, participants were asked to produce a response to each of the 16 messages and rate the valence of the response they wrote on the negative–positive affect scale. In addition, participants were asked to indicate which specific cues they used to communicate the tone of their responses.


All participant responses were coded for the presence or absence of a period. As in prior experiments, affect ratings were log-transformed to fit model assumptions.

The effect of a period across all participants

None of the participants used a line break when producing a response, leaving this hypothesis untestable. A linear mixed-effects model, however, showed that affect ratings were significantly more negative when a period was present (− 0.07, p < 0.05; intercept = 2.13). Sex did not significantly impact affect ratings (− 0.04, p = 0.35; intercept = 2.23). These results are the same as Experiments 1 and 2.

Digital divide

Participants were again split into natives and immigrants, as in prior experiments, creating a dataset with 54.2% natives and 45.8% immigrants. Digital natives and digital immigrants were equally likely to use periods in their text messages (− 0.03, p = 0.71; intercept = 0.69); however, affect ratings were more negative for digital natives than immigrants (0.09, p < 0.05; intercept = 2.13), suggesting that natives are more likely to view the period as a marker of negative affect than immigrants.

Texting experience

There was no significant difference between groups in rates of self-reported text messaging frequency (Natives M = 6.54, SD = 0.97, Immigrants M = 6.09, SD = 1.76; two-tailed t(70) = 1.37, p = 0.18), or in rates of self-reported text abbreviation use (Natives M = 3.82, SD = 1.99, Immigrants M = 3.45, SD = 1.80; two-tailed t(70) = 0.81, p = 0.42). This result mirrors Experiments 1 and 2, showing that access to and familiarity with digital platforms was the same for both natives and immigrants.


In Experiment 1, no difference was found between immigrants and natives self-reporting their use of texting abbreviations. In Experiment 2, natives were more accurate than immigrants at translating common abbreviations. In the current experiment, it is tested whether natives are more likely to use texting abbreviations.

The supplied responses were coded for both the use of abbreviations and the use of emoticons. There were no significant differences between groups in rates of abbreviation use (Natives M = 1.77, SD = 2.74; Immigrants M = 2.52, SD = 3.42; two-tailed t(70) = − 1.03, p = 0.31) or in rates of emoticon use (Natives M = 1.26, SD = 1.88; Immigrants M = 0.79, SD = 1.47; two-tailed t(70) = 1.16, p = 0.25).

General discussion

Taken as a whole, the three experiments suggest that digital natives are more sensitive to the presence or absence of small linguistic cues, particularly periods, which may alter the social or pragmatic information of a text message. This effect cannot be attributed to experience with texting or to the use of “textspeak” (measured here as abbreviations; see Crystal 2008), although digital immigrants are somewhat less likely than digital natives to interpret abbreviations correctly when reading text messages. These results suggest that simply being born in the Internet age has an effect on how one interprets communications on digital platforms, even when level of access to and familiarity with these platforms is comparable (see also Lugano and Peltonen 2012).

Although Gunraj et al. (2016) found that a period is interpreted as insincere in text messages, they did not find the same effect in handwritten messages. As such, it is possible that there are several small linguistic conventions (the use of the period among them) that have different meanings, depending on which side of the digital divide a person happens to be on. The communication conventions employed during the formative years of the digital immigrants and those that rose to prominence during the same years for digital natives may differ in subtle ways, as we have demonstrated in these experiments. In other words, digital immigrants speak with an “accent” because the digital language differs in subtle ways from the linguistic conventions that they grew up using.

It is also worth noting that in Experiment 1, the digital immigrants were more confident in their judgments of positivity and negativity when compared to the judgments of digital natives. This can be interpreted as a lack of awareness on the part of the natives with regard to the pragmatic significance of using cues like periods. The natives, on the other hand, may be more attuned to such subtle gradations of meaning (and also the inability of immigrants to understand them). As a result, they may have been more conservative in their confidence judgments.


The current studies provide only provisional evidence for these conclusions, given that only two linguistic elements were explored, and that only one was found to differ in interpretation between digital natives and immigrants. Whether the differences between these two groups in computer-mediated language use are limited to the use of the period alone is a question for future research. There may also be other mitigating factors at play. Riordan (2016), for example, did not find a difference between digital natives and immigrants in their interpretations of affect in text messages that employed emoji. However, as Riordan noted, emoji are a more recent and continually changing addition to digital communication, and as such even digital natives may be immigrants when it comes to using them fluently or having familiarity with them.

Just as people learning a new language in adulthood may speak the new language with a noticeable accent, there may be a similar imperfect mastery in making use of the subtle typographical cues employed by digital natives. In this case, of course, the accent is pragmatic in nature, and not phonological (Kreuz and Roberts 2017). In addition, such a pragmatic accent interferes with the successful decoding of messages received from digital natives. And if natives are unaware of the immigrants’ imperfect mastery of these conventions, the chances for miscommunication are heightened even further.

As with any nonnative language learner, the digital immigrant may become more fluent in using digital language with more experience, particularly when practiced along with native speakers, and they may be able to learn subtle distinctions such as employing or not employing a period at the end of text messages. However, as with any nonnative speaker, the pragmatic accent that remains from the first language is unlikely to fully disappear, and minor linguistic elements that subtly affect the meaning of a message may never be fully mastered.


This study suggests that as education is delivered increasingly online, and elements of communication between classmates and between teachers and students become increasingly computer-mediated, both teachers and students need to be sensitive to differences in interpretation of digital communications. Even students who have regular access to digital platforms, and who frequently use them, may develop different accents that alter interpretations of written communication in online forums or platforms, leading to misunderstandings between teachers and students, or among students themselves. Classrooms utilizing messaging services for group projects, social media for collaboration, or platforms such as that allow teachers and students to text each other, may need to be cognizant of the fact that misunderstandings may occur, particularly negatively, and even as the result of something seemingly as minor as the use of a period. Negative perceptions of classroom communications can have any number of repercussions to classroom dynamics, although such repercussions are not yet researched.

As college students are increasingly older in age, the digital divide between traditional age students and older nontraditional age students, as well as the digital divide between teachers and students, should garner more attention and research, particularly with regard to possible differences in experience within the increasingly common online classroom. Future research might explore how the use of such platforms may affect student perceptions of class dynamics (e.g., peer connectedness, teacher–student interaction) and of instructor characteristics (e.g., openness, caring) and whether these perceptions affect learning experiences.


  1. Ajman, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. The Internet and Higher Education, 11, 71–80.

  2. Baron, N. (2010). Always on: Language in an online and mobile world. Oxford: Oxford University Press.

    Google Scholar 

  3. Bauerlein, M. (2008). The dumbest generation: How the digital age stupefies young Americans and jeopardizes our future (or, don’t trust anyone under 30). London: Penguin Books.

    Google Scholar 

  4. Bayne, S., & Ross, J. (2011). ‘Digital native’ and ‘digital immigrant’ discourses: A critique. In R. Land & S. Bayne (Eds.), Digital difference: Perspectives on online learning (pp. 159–169). Rotterdam: Sense Publishers.

    Google Scholar 

  5. Bennett, S. (2012). Digital natives. In Z. Yan (Ed.), Encyclopedia of research on cyber behaviour (Vol. 1, pp. 212–219). Hershey, PA: Information Science Reference.

    Google Scholar 

  6. Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British Journal of Educational Technology, 39, 775–786.

    Article  Google Scholar 

  7. Brown, C., & Czerniewicz, L. (2010). Debunking the ‘digital native’: Beyond digital apartheid, towards digital democracy. Journal of Computer Assisted Learning, 26, 357–369.

    Article  Google Scholar 

  8. Carr, N. (2008, July/August). Is Google making us stupid? The Atlantic.

  9. Carr, N. (2010). The shallows: What the Internet is doing to our brains. New York: W. W. Norton.

    Google Scholar 

  10. Criar, B. (2013, November). The period is pissed: When did our plainest punctuation mark become so aggressive? The New Republic. Retrieved July 10, 2015, from

  11. Crystal, D. (2008). Txtng: The gr8 db8. Oxford: Oxford University Press.

    Google Scholar 

  12. Dede, C. (2005). Planning for neomillennial learning styles. EDUCAUSE Quarterly, 28, 7–12.

    Google Scholar 

  13. Demirbilek, M. (2014). The ‘digital natives’ debate: An investigation of the digital propensities of university students. Eurasia Journal of Mathematics, Science & Technology Education, 10, 115–123.

    Google Scholar 

  14. Fortunati, L., Taipale, S., & de Luca, F. (2017). Digital generations, but not as we know them. Convergence: The International Journal of Research into New Media Technologies, 1–18.

  15. Friemel, T. N. (2016). The digital divide has grown old: Determinants of a digital divide among seniors. New Media & Society, 18, 313–331.

    Article  Google Scholar 

  16. Grosseck, G., & Holotescu, C. (2008). Can we use Twitter for educational activities? Paper presented at the 4th International Scientific Conference for eLearning and Software for Education, Bucharest.

  17. Gunraj, D. N., Drumm-Hewitt, A. M., Dashow, E. M., Upadhyay, S. S. N., & Klin, C. M. (2016). Texting insincerely: The role of the period in text messaging. Computers in Human Behavior, 55, 1067–1075.

    Article  Google Scholar 

  18. Hackforth, R. (translator). (1952). Plato’s Phaedrus. Cambridge: Cambridge University Press.

  19. Helsper, E. J., & Eynon, R. (2010). Digital natives: where is the evidence? British Educational Research Journal, 36, 503–520.

    Article  Google Scholar 

  20. International Telecommunication Union. (2013). Measuring the world’s digital natives. In Measuring the information society: 2013 (pp. 127–158). Geneva: International Telecommunication Union.

  21. Jones, C., Ramanau, R., Cross, S., & Healing, G. (2010). Net generation or Digital Natives: Is there a distinct new generation entering university? Computers & Education, 54, 722–732.

    Article  Google Scholar 

  22. Jones, C., & Shao, B. (2011). The net generation and digital natives: Implications for higher education. York: Higher Education Academy.

    Google Scholar 

  23. Junco, R., Heiberger, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27, 119–132.

  24. Kennedy, G. E., Judd, T. S., Churchward, A., Gray, K., & Krause, K. (2008). First year students’ experiences with technology: Are they really digital natives? Australian Journal of Educational Technology, 24, 108–122.

    Google Scholar 

  25. Kennedy, G., Judd, T., Dalgarno, B., & Waycott, J. (2010). Beyond natives and immigrants: Exploring types of net generation students. Journal of Computer Assisted Learning, 26, 332–343.

    Article  Google Scholar 

  26. Kovaz, D., Wilson, J. D., Rogers, J. W., Dahlke, L. A., Black, R. K., Sable, J. J., & Kreuz, R. J. (2015, November). Are you laughing when you lol?: Examining emotion in texting shortcuts using event-related potentials. Poster presented at the 56th annual meeting of the Psychonomic Society, Chicago.

  27. Kreuz, R., & Roberts, R. (2017). Getting through: The pleasures and perils of cross-cultural communication. Cambridge, MA: MIT Press.

    Google Scholar 

  28. Laverne, L. (2014). Born before 1985? Then you’re a ‘digital immigrant.’ The Guardian. Retrieved July 10, 2015, from

  29. McCarthy, J. (2010). Blended learning environments: Using social networking sites to enhance the first year experience. Australasian Journal of Educational Technology, 26, 729–740.

  30. Librero, F., Ramos, A. J., Ranga, A. I., Trinona, J., & Lambert, D. (2007). Uses of the cell phone for education in the Philippines and Mongolia. Distance Education, 28, 231–244.

    Article  Google Scholar 

  31. Ling, R., & Baron, N. S. (2007). Text messaging and IM: Linguistic comparison of American college data. Journal of Language and Social Psychology, 26, 291–298.

    Article  Google Scholar 

  32. Lugano, G., & Peltonen, P. (2012). Building intergenerational bridges between digital natives and digital immigrants: Attitudes, motivations and appreciation for old and new media. In E. Loos, L. Haddon, & E. Mante-Meijer (Eds.), Generational use of new media (pp. 151–170). London: Routledge.

    Google Scholar 

  33. Moran, M., Seaman, J., & Tinti-Kane, H. (2011). Teaching, learning, and sharing: How today’s higher education faculty use social media. Babson Survey Research Group. Retrieved from

  34. National Center of Education Statistics (2014). Characteristics of postsecondary students [Data file]. Retrieved from

  35. Negroponte, N. (1995). Being digital. New York: Vintage Books.

    Google Scholar 

  36. Oblinger, D. (2003). Boomers, gen-Xers and millennials: Understanding the new students. EDUCAUSE Review, 38, 37–47.

    Google Scholar 

  37. Oblinger, D. G., & Oblinger, J. L. (2005). Educating the net generation. Boulder, CO: EDUCAUSE.

    Google Scholar 

  38. Palfrey, J., & Gasser, U. (2008). Born digital: Understanding the first generation of digital natives. New York: Basic Books.

    Google Scholar 

  39. Palfrey, J., & Gasser, U. (2011). Reclaiming an awkward term: What we might learn from “Digital Natives”. Journal of Law and Policy for the Information Society, 7, 33–55.

    Google Scholar 

  40. Pasek, J., More, E., & Hargittai, E. (2009). Facebook and academic performance: Reconciling a media sensation with data. First Monday, 14(5). Retrieved from

  41. Pew Research Center. (2011). The digital revolution and higher education. Retrieved March 2017 from

  42. Prensky, M. (2001a). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6.

    Article  Google Scholar 

  43. Prensky, M. (2001b). Digital natives, digital immigrants, part II: Do they really think differently? On the Horizon, 9(6), 1–9.

    Article  Google Scholar 

  44. Prensky, M. (2004). The emerging online life of the digital native: What they do differently because of technology, and how they do it. Retrieved July 10, 2015, from

  45. Prensky, M. (2012). From digital natives to digital wisdom: Hopeful essays for 21st century learning. Thousand Oaks, CA: SAGE.

    Google Scholar 

  46. Riordan, M. A. (2016). Appear happier: Text with emojis. Presentation at the 57th annual meeting of the Psychonomic Society, Boston, MA.

  47. Riordan, M. A. (2017). The communicative role of non-face emojis: Affect and disambiguation. Computers in Human Behavior, 76, 75–86.

    Article  Google Scholar 

  48. Riordan, M. A., & Kreuz, R. J. (2010). Emotion encoding and interpretation in computer-mediated communication: Reasons for use. Computers in Human Behavior, 26, 1667–1673.

  49. Sakai, N. (2013). The role of sentence closing as an emotional marker: A case of Japanese phone e-mail. Discourse, Context and Media, 2, 149–155.

    Article  Google Scholar 

  50. Selwyn, N. (2009). The digital native—myth and reality. Aslib Proceedings: New Information Perspectives, 61, 364–379.

    Article  Google Scholar 

  51. Smith, E. E. The digital native debate in higher education: A comparative analysis of recent literature (2012). Canadian Journal of Learning and Technology, 38(3).

  52. Taipale, S. (2016). Synchronicity matters: Defining the characteristics of digital generations. Information, Communication & Society, 19, 80–94.

    Article  Google Scholar 

  53. van den Beemt, A., Akkerman, S., & Simons, P. (2010). Patterns of interactive media use among contemporary youth. Journal of Computer Assisted Learning, 27, 103–118.

    Article  Google Scholar 

  54. Vie, S. (2008). Digital divide 2.0: “Generation M” and online social networking sites in the composition classroom. Computers and Composition, 25, 9–23.

    Article  Google Scholar 

  55. Waycott, J., Bennett, S., Kennedy, G., Dalgarno, B., & Gray, K. (2010). Digital divides? Students and staff perceptions of information and communication technologies. Computers & Education, 54, 1202–1211.

    Article  Google Scholar 

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Appendix: Examples of text message exchanges

Appendix: Examples of text message exchanges

Experiment 1: You are visiting a city where an old friend lives. You send him this text, hoping to see him:

hey im in town this week wanna meet up?

He/she replies (one of the four replies was presented):


we could do that (line break, no period)


we could do that. (line break, period)

yes we could do that (no line break, no period)

yes. we could do that. (no line break, period)

Experiment 2: You are visiting a city where an old friend lives. You send him this text, hoping to see him:

hey im in town this week wanna meet up?

He replies (one of the four replies was presented):


probably cant this week


probably cant this week.

sry probably cant this week

sry. probably cant this week.

Experiment 3: And old friend is visiting the city where you live. He sends you this text, hoping to see you:

hey im in town this week wanna meet up?

Imagine that you are in this scenario and write a text message that you would send in response:

[Text box provided]

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Riordan, M.A., Kreuz, R.J. & Blair, A.N. The digital divide: conveying subtlety in online communication. J. Comput. Educ. 5, 49–66 (2018).

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  • Digital divide
  • Texting
  • Computer-mediated communication