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Are There Generational Differences?: Social Media Use and Perceived Shared Reality

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Are there generational differences in how social media influences our perceived reality of the world? Based on the survey results of 1,060 adults in the U.S., this article examines generational differences between so-called "digital natives" - millennial students who have grown up using information communication technologies - And earlier generations, who have adopted social media tools later in life. We examined both traditional and social media usage patterns to see if social media use plays a role in perceived shared reality-how people are influenced by their network. The results suggest that the two generations differ in terms of how different facets of social media use are correlated with their perception of shared reality. However, certain uses of social media, such as clicking links provided by social media contacts, contribute to perceived shared reality both for younger and older people.
Are There Generational Differences?
Social Media Use and Perceived Shared Reality
Brian J. Bowe
Western Washington University
516 High Street
Bellingham, WA 98225
1-360-650-4436
bowebria@msu.edu
Donghee Yvette Wohn
New Jersey Institute of Technology
University Heights, GITC5100
Newark, NJ, USA
1-973-596-5291
wohn@njit.edu
ABSTRACT
Are there generational differences in how social media influences
our perceived reality of the world? Based on the survey results of
1,060 adults in the U.S., this article examines generational
differences between so-called “digital natives” millennial
students who have grown up using information communication
technologies and earlier generations, who have adopted social
media tools later in life. We examined both traditional and social
media usage patterns to see if social media use plays a role in
perceived shared realityhow people are influenced by their
network. The results suggest that the two generations differ in
terms of how different facets of social media use are correlated
with their perception of shared reality. However, certain uses of
social media, such as clicking links provided by social media
contacts, contribute to perceived shared reality both for younger
and older people.
Categories and Subject Descriptors
K.4 Computers and Society Collaborative and Social Computing
design and evaluations Social network analysis, Knowledge
representation and reasoning, Law, social and behavioral
sciencesSociology, User Characteristics Age Seniors,
Adolescence
General Terms
Algorithms, Human Factors, Theory
Keywords
Digital Natives; Social Influence; Social Media; Millennials;
Shared Reality; Social Constructionism.
1. INTRODUCTION
Reality is socially constructed [5] and influenced by mass media
[1] and, more recently, social media [30]. The Crystallization
model [30] explains how social media influences perceived
reality: perception of reality begins with exposure to information
through various channels, including mainstream media, social
media (e.g., social network sites, blogs), and individuals in social
networks. Once an individual is exposed to information, they form
an attitude towards that information, which is also influenced by
the source from which the information was received [30].
Since reality begins with information exposure, it is important to
understand that the source of information could have a major
influence on an individual’s perception of reality—such as what is
important news around the world, topics of interest, and issues
that are important to society. As Information and Communication
Technologies (ICTs) become increasingly ingrained in day-to-day
life, some scholars notably Prensky [3] have posited that
there are significant generational differences between older and
younger users. If such generational differences exist in technology
usage, it may also play a role in creating inevitable generational
differences in perceptions of reality.
However, there are also scholars who argue against the existence
of such broad generational differences (e.g., [4,18]). It is thus
important to examine whether there are differences between
patterns of media use between the cohort known as “millennials”
and earlier generations, and if such differences, should they exist,
affect perceptions of reality.
2. GENERATIONAL DIFFERENCES
2.1 ‘Digital Natives’ Revisited
Prensky [26] popularized the concept of an emerging generation
of technologically-adept digital natives, placed in counterpoint to
older, less-fluent digital immigrants. A key part of Prensky’s [26]
argument was that early and frequent exposure to ICT changed the
thinking processes of digital natives, causing them to learn in
different ways than earlier generations. This concept has become
much cited in media and educational discourse, in part because
Prensky built his argument around the specific challenges such a
situation would create in classrooms, where teachers and students
typically belong to different generational cohorts.
The digital natives argument is not without its critics (e.g.,
[4,19,29]) who claim that it is overly simplistic, lacks rigorous
and transparent empirical basis, and has become a cliché or even
“faith-based religion” [4]. Some research indicates digital natives
are not more visually literate than their older counterparts [6].
Although the more recent generation of students use technology
more frequently, there is little evidence that they adopt radically
different learning styles [24]. Moreover, while lifelong exposure
to technology creates a generational gap, motivated older adults
are just as able to become fluent with digital technologies as
younger people [13]. Joiner et al. [17], offer a further
complication of the picture by identifying sub-generational
differences between older members of the millennial generation
and younger members. Due to these studies, some suggest that the
term digital natives is misleading and should be abandoned in
scientific writing [20].
Moreover, some scholars argue that simply providing students
with access to technological tools is insufficient. Just because
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students have similar access to technology does not necessarily
mean they use it in similar ways. In fact, there still exists a
“participatory divide” [16] that is sometimes unrelated to
technology. Jenkins and colleagues [16] argue that young peoples’
contributions to society through ICT are expressions of a
participatory culture, but access to the Internet alone does not
provoke the development of such a culture.
But even as scholars critique the overly simplistic discourse about
digital natives, it is clear that there remain important questions
underlying the concept worth studying. When describing the
digital native generation, Palfrey and Gasser [25] suggested there
is one thing we know for sure about them: “These kids are
different” (p. 2).
2.2 Generational Differences and Media Use
While previous research has suggested that this generational
divide is subtler than was earlier posited, it remains uncertain
whether such a divide exists not in how much of the
technology people of different generations are using, but in how
their use of technology affects other aspects of their lives.
There is some evidence of generational differences, but also of
rapid changes in how people use ICTs generally. In 2010, a Pew
report found that 73% of teens and 72% of young adults used
social network sites, while only 40% of adults 30 and older used
them [23]. By 2014, those usage figures had jumped to 89% of
people aged 18-29 and 82% of people aged 30-49 [8]. Our first
research question sets out to test assumptions of generational
differences regarding how much people consume different types
of Internet media, including social media, traditional news
websites, and blogs:
RQ1: Is there a generational difference in frequency of media use
between members of the millennial generation and earlier
generations?
3. Social Media and Social Influence
If there is (or is not) a difference in terms of how different
generations use media, the next question examines how media use
is related to social influence. Social influence theories suggest that
people are affected by others perceptions, regardless of whether
or not those perceptions are correct [2,10]. These theories explain
how people endeavor to adjust their attitudes in a manner
congruent with the group perceived to be the most desirable.
There are a number of different theories of social influence, the
most popular ones being conformity and compliance (for review,
see Cialdini & Goldstein [7]).
3.1 Perceived Shared Reality
Higgins [14] had a more specific stance on social influence,
explaining social influence as a communicative process. He
described the act of communication as a social act that is a
manifestation of an individuals desire to achieve shared reality,
[14]. Echterhoff et al. [9] said that for individuals to achieve
shared reality, this reality has to be about a target referent of
knowledge, or a specific topic of conversation. Shared reality is
not just a replication of another person’s inner state, but about a
specific piece of information that is a combination of fact and
opinion. Once a shared reality is established, it can remain stable
even if there are competing shared realities [12].
3.2 Social Media and Shared Reality
As mentioned earlier, Wohn and Bowe [31] mapped out a model
for how social media would influence shared reality. This model,
however, does not take into account the sheer volume of social
media use. It may be that those with greater social media use are
more influenced by social media in forming their perception of
shared reality. Shared reality could also depend upon with whom
one communicates on social media and the types of activities one
performs. Our second research question asks whether the extent of
social media use and with whom users communicate on social
media have an effect on shared reality. The question examines
how much people are influenced by their social network regarding
what issues they think are important and what they think in
general.
RQ2: What is the relationship between different types of usage of
social media use and individuals perception of shared reality?
4. METHODS
An online survey was emailed to all faculty, staff, students, and
select alumni (N=27,298) at a mid-sized public university in the
Midwest, asking them to participate in a survey about social
media use. There were 2,032 total respondents (response rate=
7.4%) who started the survey and 1,060 who completed 80% of
the survey. This included 1,306 undergraduate and graduate
students, 255 faculty and staff members, and 9 alumni and friends
of the university who have university-issued email accounts. All
respondents were asked basic demographic questions such as age,
gender, student or faculty status, and a series of questions related
to their media use and social influence. Most of the participants
were female (69.3%), followed by male (30.2%) and transgender
(.5%). The median age was 32 when the survey was fielded. Thus
age comparisons were made based on being 32 or older, or
younger than 32. This cutoff separates respondents born in 1979
or earlier, and those born in 1980 or later. This age cutoff was
consistent with Palfrey and Gasser [25], who identified digital
natives as individuals born after 1980, which was when social
digital technologies, such as Usenet and bulletin board systems,
were available online (p.1). We would like to note, however, that
there have been some other interpretations of when the millennial
generation begins [11,21].
Frequency of media use was six categories: once a day, multiple
times a day, once a week, multiple times a week, once a month,
multiple times a month. Individuals were asked about three types
of media use: news websites, blogs, and social media. More
granular social media usage questions included with whom they
connected with on social media (family, friends, colleagues,
classmates, brands, and organizations) and if they clicked on links
shared by their contacts on social media.
Our operational measure of social influence was perceived shared
reality (M= 3.2, SD= .95), which was an original three-item scale
(Cronbachs alpha= .82) measuring the extent of social influence
in the individuals perception of what social topics are important.
The items were: My social network influences what issues I feel
are important, My social network influences what I think, and
My social network teaches me about world events. Participants
rated these items on a 5-point Likert-type scale ranging from
Strongly Disagree to Strongly Agree.
5. RESULTS
5.1 Generational differences in Media Usage
To examine generational differences in media usage patterns
(RQ1) we ran independent-samples t-tests between the older and
younger cohorts (mean split, age 32) in terms of frequency of
different types of media use (Table 1). Millennial participants
reported significantly higher frequency of social media usage than
older participants (t(1030)=8.12, p<.001). Older participants,
however, reported significantly higher frequency of visiting news
websites (t(1030)= -9.74, p<.001) and reading blogs (t(1030)=-
3.13, p<.01). Millennial participants reported higher frequency of
clicking on links that their contacts post on social platforms such
as Facebook and Twitter (t(1030)=2.68, p<.01).
Table 1. Comparing media use between generational cohorts
Item
Age group
N
Mean
SD
Frequency of Social
Media Use ***
Under 32
878
5.72
.765
32 +
154
5.08
1.49
Frequency of News
website visit **
Under 32
878
4.24
1.51
32 +
154
5.46
.79
Frequency of reading
blogs **
Under 32
878
3.21
1.88
32 +
154
3.72
1.91
Frequency of
clicking links **
Under 32
878
2.29
1.04
32 +
154
2.89
1.31
Statistically significant differences between groups are noted with *p<.05,
**p<.01, ***p<.001
5.2 Social Media Use and Perceived Shared
Reality
To examine RQ2 about the relationship between social media use
and individuals perceived shared reality, we ran three Ordinal
Least Squares regressions, with perceived shared reality as the
dependent variable and media usage as independent variables.
First, we looked at users above the age of 32. We used two
models to see the added effects of media usage in general and
more detailed social media usage after controlling for age and
gender (Table 2). The final model was statistically significant,
F(12, 148)= 6.60, p<.001, adjusted R2= .31. The R square changes
between the three models were all significant at the p<.001 level.
Just looking at frequency of media usage (Model 1 in Table 2), we
found that there is a positive effect of frequency of social media,
meaning that the more frequently older users use social media, the
more they believe their social network influences what they think
and what topics are important. The frequency of reading blogs
was also significantly associated with perceived shared reality,
though visiting news websites was not.
When we looked at the more granular uses of social media in
Model 2, the frequency of social media use was no longer
statistically significant. Instead, we found that for older users,
interacting with colleagues on social media was negatively related
to perceived shared reality (p<.001) but that clicking on
links that contacts posted on social media was positively related
(p<.001) with perceived shared reality. Interacting with
family members (= -.20, p=.05) was borderline significantly
associated with perceived shared reality. Within this generational
cohort, differences in age or gender did not affect perceptions of
social influence.
We saw some similarities and differences in data of millennial
users. The final model for users under the age of 32 was
significant, F(12, 845)= 26.24, p<.001, adjusted R2= .26 (Table
3). Frequency of social media use and reading blogs were both
positively associated with perceived shared reality (Table 3,
Model 1) but the effect of social media use frequency persisted
even when more detailed uses of social media were added to the
model (Table 3, Model 2). Family was negatively associated with
social influence (=-.13, p<.001), but using social media to
communicate with family (=.05, p<.05) and interact with brands
(=.09, p<.001) was also a negative predictor. Clicking on links
provided by friends on social media was a significant positive
factor associated with (=.37, p<.001) social influence.
Table 2. Regression examining relationship of media use and
perceived shared reality among individuals aged 32 and up
Model 1
Gender
-.11
Age
-.01
Frequency of social media use
.40***
Frequency of news website visit
-.08
Frequency of reading blogs
.19*
Use social media to communicate
with
Family
Friends
Colleagues
Classmates
Brands
Organizations
-.20
.11
-.15*
-.06
.05
-.02
Clicking on links on social media
Adjusted R2
.21
Note. Coefficients are standardized, *p<.05, **p<.01, ***p<.001
Table 3. Regression examining relationship of media usage
and perceived shared reality among individuals under age 32
Model 2
Model 3
Gender
.09**
.05
Age
.04
.02
Frequency of social media use
.27***
.11**
Frequency of news website visit
-.02
-.03
Frequency of reading blogs
.12**
.08*
Use social media to communicate
with
Family
Friends
Colleagues
Classmates
Brands
Organizations
-.09**
.01
.00
-.01
-.06
-.15***
Clicking on links provided by social
media contacts
.35***
Adjusted R2
.10
.26
Note. Coefficients are standardized, *p<.05, **p<.01, ***p<.001
6. DISCUSSION
Examining media usage patterns, we found that users in the older
cohort (32 and above) were more likely to obtain information
through news websites and blogs than younger users, and the
younger cohort was more likely to spend more time on social
media than older counterparts.
When looking at how media use correlated with perceptions of
what social issues are important, the mere extent of social media
usage was not a significant factor for older users. For younger
users, however, there was a significant relationship between the
frequency of social media use and individuals perception of what
is important and what topics they should think about. When
examining more detailed relationships between specific features
of social media usage, we found that using social media to interact
with family and organizations was negatively associated with
perception of shared reality for younger users. For older users,
there was no statistical significance in terms of with whom they
were communicating on social media and their perception of
shared reality. For both groups, clicking on links provided by
contacts on social media positively contributed to perceived
shared reality.
These results suggest that the information that people receive
through social media is strongly associated with what type of
social topics they think is important. Consistent with the model
proposed by Wohn and Bowe [30], this provides some evidence to
support the fact that our connections on social media are
becoming neo agenda setters, providing us with cues regarding
what we should think about and how to think about it. Moreover,
now that more people are getting news through social mediaa
recent Pew survey reported that about half of all users on Twitter
and Facebook consume news through that site [15]the role of
social media may even become more prominent in shaping our
perceived shared reality in the future.
Even though there is a lack of evidence of a deep generational
gap, our results suggest that there are some small differences.
Thus, it is important to continue to examine usage patterns among
users with different characteristics; it could be that individual
differences account for more variance than generational
differences. At the same time, it would be more useful to examine
with whom individuals are interacting using social media, as
certain types of social interactions may have larger effects on
perceptions than others. In this study, using social media to
communicate with friends, colleagues, or classmates had no
influence on how much individuals thought their social network
influenced their perceptions on what is important. There are
several possible explanations for this. Firstly, individuals mainly
use social media as a means for relationship maintenance [22]
rather than for professional purposes. It could be that the
interactions within social media are so lightweight as not to have a
profound or meaningful impact on ones thoughts. Another
explanation could be that information being provided by the social
network is too personal for someone else to internalize as their
own. However, it could also be that even among friends,
colleagues, and classmates, people are influenced by some and not
others. Because of this, lumping people into one category may
conceal the nuances of different relationships.
The results of this study counter that there appear to be
differences between older and younger generations with regard to
how much they use technology and with whom they connect
through social media. However, these differences do not
fundamentally change the nature of how social media influences
them. This reinforces Jones’ [20] assertion that even if there are
generation-related differences, they “are mediated by the active
appropriation of technology by young people who act purposively
and in relation to influential institutional contexts” (p. 367).
This study contains several limitations, the most notable of which
is that sample is not representative of the larger population, thus
inferences cannot be drawn to the general public. This population
was drawn from a university community, and thus possesses
particular characteristics that differentiate it from the general
public. A university population is composed of people who are
undergoing or have finished a postsecondary education, making it
more educated than the public at large. This raises the important
question of to what extent level of education has relative to
generation. Furthermore, because the survey was presented as a
survey about social media use, respondents may have been a
particularly interested group. Future researchers should triangulate
this data with other methodological approaches, such as focus
groups or user observation, to provide deeper insight into the
patterns and relationships at work.
7. CONCLUSION
Our research shows that there are indeed differences between
millennials and older generations in terms of media consumption
patterns, but the differences may not be as pronounced as popular
wisdom would lead us to believe. Congruent with other studies
(e.g., [3,28]) our results found both support for and arguments
against the idea of a divide between digital natives and
immigrants. We found significant differences in patterns of media
consumption, including social media. Social media usage was
correlated to an individuals perceived sense of shared reality
how much they think their social network influences what they
think is importantbut the correlation patterns were somewhat
different between older and younger generations.
Since his initial argument, even Prensky [27] has suggested
moving beyond the native vs. immigrant distinction toward a
concept of digital wisdom that stems from a symbiosis of the
brain and the enhancements to cognition made possible by
technology. But even when such arguments abandon simple
generational dichotomies, there remains a strong undercurrent of
technological determinism [4]. So, while we cannot recommend
completely jettisoning the idea that people who grew up
surrounded by ICTs may be influenced by them in different ways
than people who did not, we can certainly say that the reality is
more complex than popular wisdom would lead us to believe. Our
results support Wang, Myers and Sundaram’s [30] assertion that
there is a continuum rather than a rigid dichotomy between the so-
called digital natives and immigrants.
Perhaps this continuum is the source of the problem with the
digital natives discourse. As Thompson [28] notes, it might be that
the popular press coverage of the concept takes an overly
deterministic view of these differences and exaggerates their size.
In our study, we found that regardless of age, clicking on links
shared by social media contacts was strongly and significantly
related to participants’ perceived shared reality. This finding
suggests that examining characteristics of people’s networks and
the content that they share may be a more useful approach at
understanding individual differences rather than age.
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... Sin embargo, el término de 'nativos digitales' también ha sido cuestionado (Bowe & Wohn, 2015;Jones et al., 2010); las principales críticas se basan en que no define claramente el rango de edad para considerar a una persona como nativo digital, aunque la literatura internacional sobre el tema se enfoca en jóvenes nacidos al principio de la década de 1990 o posteriormente (Barak, 2018;Bowe & Wohn, 2015). ...
... Sin embargo, el término de 'nativos digitales' también ha sido cuestionado (Bowe & Wohn, 2015;Jones et al., 2010); las principales críticas se basan en que no define claramente el rango de edad para considerar a una persona como nativo digital, aunque la literatura internacional sobre el tema se enfoca en jóvenes nacidos al principio de la década de 1990 o posteriormente (Barak, 2018;Bowe & Wohn, 2015). ...
... El autor esquematiza tres tipos de habilidades: operacionales, de información y estratégicas.Esta dimensión se puede vincular con el planteamiento de la existencia de nativos y migrantes digitales, ya que dicha propuesta enfatiza que los nativos digitales poseen mayores habilidades digitales por haber crecido en un entorno dominado por las tic, mientras que los migrantes digitales han desarrollado menos habilidades para usarlas. No obstante, algunos estudios han cuestionado esta relación directa entre ambas dimensiones argumentando que los migrantes digitales tienen motivos para usar las tic y que esto a su vez permite que desarrollen habilidades críticas en el entorno digital(Bowe & Wohn, 2015;Friemel, 2014;Hargittai & Dobransky, 2017).La línea argumentativa de las cuatro dimensiones que componen la bdp no es lineal. Las dimensiones interactúan entre ellas y al mismo tiempo se refuerzan. ...
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La brecha digital es concebida como la desigualdad en el acceso, habilidades, uso y disponibilidad de dispositivos o plataformas digitales. En el contexto actual de digitalización de muchas de las tareas cotidianas, esta desigualdad está cobrando cada vez más relevancia. El presente trabajo tiene como objetivo analizar las dimensiones de accesibilidad, motivación y uso de internet en México y Uruguay desde el marco interpretativo de la brecha digital profunda propuesto por Van Dijk (2005). Este análisis se enfoca en los factores socioeconómicos asociados a esta desigualdad, tales como el estrato socioeconómico, el grupo generacional (nativos o migrantes digitales) y el sexo. Como principales hallazgos se tiene que en ambos países continúan algunas brechas digitales, por ejemplo: en México el uso de internet es equitativo entre sexos, pero no así entre estratos socioeconómicos; mientras que en Uruguay la brecha digital es menor, aunque sobresale una mayor proporción de usuarios mayores de 45 años (catalogados como migrantes digitales) y que en estratos altos hay un grupo de personas que no usa internet por falta de interés. Estos resultados avanzan en el estudio de la brecha digital desde una perspectiva más detallada y de corte internacional para la región de América Latina.
... and Communication Technologies (ICT) in any social and educational context (Ojando et al., 2017). Current university students can be considered as "net generation" or "digital natives" (Thompson, 2013;Bowe & Wohn, 2015). However, even if said students are called "digital natives", this does not ensure that they have developed digital competence, and even if they have, it would be necessary to find out the level of acquisition that they possess (Barak, 2018). ...
... According to Mirete and colleagues (2015), knowing students' attitudes, knowledge and use of ICT can facilitate their inclusion in educational processes and the transition towards an educational model centred on the student. Although current university students can be considered as "net generation" or "digital natives" (Thompson, 2013;Bowe & Wohn, 2015), the results obtained in this study reflect that the level of digital competence of university students is medium (M = 75.34 over 132 points). ...
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To evaluate the digital competence of pre-service teacher, three sub-scales must be considered: attitude, knowledge and use. However, the degree of acquisition may vary depending on different variables. The main objective of this research is to find out the level of digital competence of university students based on these three sub-scales, and, as secondary objectives, to find out whether any differences exist in relation to students' educational modality and gender. A non-experimental design has been used (ex post facto) with a sample of 675 students from the Pontifical University of Salamanca. The results revealed that the level of digital competence of the pre-service education teacher is medium, with no significant differences in gender. However, differences were found in the Blended Learning modality.
... In a complementary sense, due to the perceptual and attitudinal differences recognized in the entrepreneurship literature by demographic conditions (Minniti, 2010;Bosma and Schutjens, 2011) and the gender and age discrepancies in the use of social media (Fietkiewicz et al., 2016;Karatsoli and Nathanail, 2020), it is reasonable to state that the relationship between social media use and perceived subjective norms should vary according to students' gender and age group. This approach is also supported in that the selection of content and the way people relate to their environment vary by age and gender (Kimbrough et al., 2013;Bowe and Wohn, 2015). Consequently, the following research hypotheses are proposed. ...
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Despite studies published in the last decade linking entrepreneurship and the use of social media, to date, no study has analyzed in depth the relationship between the frequency of use among different social media sites and the intensity of entrepreneurial attitudes and perceptions. Due to the increasing use of social media as a teaching tool in higher education, this study seeks to address this knowledge gap. Consequently, the objective of this research is to analyze whether the frequency of social media use, among Twitter, Facebook, Instagram, WhatsApp and YouTube, is linked with perceptual discrepancies regarding entrepreneurial subjective norms, attitudes toward entrepreneurship and the intention to undertake, differentiating this relationship by gender and age group. A total of 246 business and engineering students from Chile were evaluated through online surveys, and Mann–Whitney and Spearman’s correlation tests were performed. The findings highlight that higher exposure on these social media platforms is related to better entrepreneurial attitudes and perceptions, and this association differs according to students’ gender and age group; in particular, Facebook shows more positive correlations in younger and male students, and WhatsApp and YouTube show more positive correlations in older and female students. This research extends the understanding of the relation between exposure to social media and variables with a considerable influence on undertaking entrepreneurship. Universities should integrate social media use as a teaching tool among students to aid in their entrepreneurial learning and strengthen new ventures. The main limitation of the study is that the sample includes business and engineering students in Chile. Future studies should ask students from different countries; likewise, it is important to use qualitative methodologies to determine why social media may affect entrepreneurial perceptions and attitudes.
... Sumuer, 2018), the United States (e.g. Bowe & Wohn, 2015;Gierdowski et al., 2020;Dabbagh et al., 2019;Thompson, 2013Thompson, , 2015, and the UK (e.g. Margaryan et al., 2011;Newman & Beetham, 2017). ...
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Responding to the lack of longitudinal analyses on media usage in higher education, this study explores the changes of higher education students’ access to and use of technology for learning in 2012, 2015 and 2018. Using an online questionnaire, a total of 5,572 German higher education students participated. Via descriptive and inferential analysis, the data show a clear trend towards using flexible, location-independent devices, accompanied by a rapid increase in the use of instant messaging. This is in line with an increasing demand for digital and flexible learning opportunities such as web-based training and lectures as podcasts or vodcasts, which is not met by higher education institutions. On that basis, improvements in development and application of these digital tools seem crucial for German higher education institutions and should be considered by educational technologists and decision makers. Of particular relevance against the background of the COVID-19 pandemic that started in Spring 2020, this longitudinal analysis provides a framework for the ongoing development and implementation of digital media in teaching and learning at higher education institutions.
... They may prefer online education given the right situation (Yu & Suny, 2020;Maleesut, Piyawattanaviroj & Yasti, 2019;Tick, 2019). The utilization of ICT in their daily tasks has become their routine (Bowe & Wohn, 2015;Prensky, 2010), and using technology in the learning and teaching (LnT) process helps to boost their motivation (Thang et al., 2014). The perception between them and the current acceptance of online learning among educators is distinctive (O' Bannon & Thomas, 2014;Kite et al., 2020). ...
Technical Report
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This report focuses on the experience of the Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia (FSSH UTM) with regards to the shift from the traditional face-to-face (f2f) teaching and learning to Online Learning (OL) before, during and after the Coronavirus 2019 (COVID-19) pandemic.
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Much psychological research depends on participants’ diligence in filling out materials such as surveys. However, not all participants are motivated to respond attentively, which leads to unintended issues with data quality, known as careless responding. Our question is: how do different modes of data collection—paper/pencil, computer/web-based, and smartphone—affect participants’ diligence vs. “careless responding” tendencies and, thus, data quality? Results from prior studies suggest that different data collection modes produce a comparable prevalence of careless responding tendencies. However, as technology develops and data are collected with increasingly diversified populations, this question needs to be readdressed and taken further. The present research examined the effect of survey mode on careless responding in a repeated-measures design with data from three different samples. First, in a sample of working adults from China, we found that participants were slightly more careless when completing computer/web-based survey materials than in paper/pencil mode. Next, in a German student sample, participants were slightly more careless when completing the paper/pencil mode compared to the smartphone mode. Finally, in a sample of Chinese-speaking students, we found no difference between modes. Overall, in a meta-analysis of the findings, we found minimal difference between modes across cultures. Theoretical and practical implications are discussed.
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This study examined if professional development needs of digital natives and immigrant teachers differed for technology integration in a Chinese education setting. Quantitative and qualitative data was collected from 500 teachers at six schools in China. The digital native teachers and immigrant teachers were compared in terms of their different technology use behaviors and integration skills. The findings indicated that even though the digital native teachers had greater comfort with basic technology than the digital immigrant teachers, they still required training for effective integration of technology in their teaching. The digital immigrant teachers needed more basic technology operations training connections between technologies and teaching. Future technology professional developments in developing countries should: 1) consider the different needs of digital native teachers and digital immigrant teachers; 2) prepare them to make meaningful connections between technologies and their teachings; and 3) adopt individual coaching with on-site designated specialists.
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Humans have a fundamental need to experience a shared reality with others. We present a new conceptualization of shared reality based on four conditions. We posit (a) that shared reality involves a (subjectively perceived) commonality of individuals' inner states (not just observable behaviors); (b) that shared reality is about some target referent; (c) that for a shared reality to occur, the commonality of inner states must be appropriately motivated; and (d) that shared reality involves the experience of a successful connection to other people's inner states. In reviewing relevant evidence, we emphasize research on the saying-is-believing effect, which illustrates the creation of shared reality in interpersonal communication. We discuss why shared reality provides a better explanation of the findings from saying-is-believing studies than do other formulations. Finally, we examine relations between our conceptualization of shared reality and related constructs (including empathy, perspective taking, theory of mind, common ground, embodied synchrony, and socially distributed knowledge) and indicate how our approach may promote a comprehensive and differentiated understanding of social-sharing phenomena.
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More than 10 years have passed since the first introduction of the term "digital natives" in Prensky's (2001a, 2001b) two seminal articles. Prensky argues that students today, having grown up in the Digital Age, learn differently from their predecessors, or "digital immigrants". As such, the pedagogical tools and methods used to educate the Natives are outdated. Consequently, many educational professionals became convinced that the ways in which today's students think and learn have been qualitatively changed by their use of information and communication technology (ICT). Indeed, the analogy introduced by Prensky is very appealing, however, no significant empirical evidence exists to support this conjecture and neither facts nor evidence tested in everyday practice have been provided. This paper aims to critically examine the underlying "digital native" theory by reviewing some recent studies questioning the existence of digital natives and presenting some of the current findings from a major case study. The study involves Irish secondary school students and their approach and use of new technologies for language learning. By monitoring and interviewing the students and their teachers, it is intended to provide evidence and information to reflect on some key topics such as the use of ICT for language learning during and outside the class, the analysis of students' skills (as putative digital natives) within language learning, and the attitude of teachers and tutors toward technologies. Overall, it is intended to examine if the current evidence resulting from this study validates Prensky's digital native theory. Keywords: digital natives, digital immigrants, ICT, education, language learning.
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We propose Crystallization as a framework for understanding how reality is socially constructed in the age of social media by incorporating network attributes to agenda-setting theory. Reflecting the affordances of contemporary technology and the psychological underpinnings of social influence, Crystallization suggests that social media facilitates information produced or relayed by the members of our social networks, who become neo agenda setters. People's perceptions of reality will develop through their social networks and everyone will perceive that the information their social network produces reflects reality, but at the macro level, we will see an ever-diverging cacophony of socially constructed realities.
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A fundamental human need is to experience a shared reality with others. We present a new conceptualization of shared reality based on four conditions. We posit that (a) shared reality involves a (subjectively perceived) commonality of individuals’ inner states (not just observable behaviors); (b) shared reality is about some target referent; (c) for a shared reality to occur, the commonality of inner states must be appropriately motivated; (d) shared reality involves the experience of a successful connection to other people's inner states. In reviewing relevant evidence, we emphasize research on the saying-is-believing effect, which illustrates the creation of shared reality in interpersonal communication. We discuss why shared reality provides a better explanation of the findings from saying-is-believing studies than do other formulations. Finally, we examine relations between our conceptualization of shared reality and related constructs (including empathy, perspective-taking, theory of mind, common ground, embodied synchrony, and socially distributed knowledge), and indicate how our approach may promote a comprehensive and differentiated understanding of social sharing phenomena.
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The so-called millennial learners who currently populate college classrooms are purportedly digital natives whose repeated exposure to a host of new technologies has allegedly resulted in enhanced skills in several areas, including those related to technology and visual communication. By extension, the argument has been made that digital natives have a significant degree of visual literacy. This article reports the results of an empirical study that examines these claims by assessing post-secondary students’ use of visually-oriented technologies and their interpretation of visual material. The survey data suggest that participants are not particularly adept at producing and interpreting visual communication.
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The so-called millennial learners who currently populate college classrooms are purportedly digital natives whose repeated exposure to a host of new technologies has allegedly resulted in enhanced skills in several areas, including those related to technology and visual communication. By extension, the argument has been made that digital natives have a significant degree of visual literacy. This article reports the results of an empirical study that examines these claims by assessing post-secondary students' use of visually-oriented technologies and their interpretation of visual material. The survey data suggest that participants are not particularly adept at producing and interpreting visual communication.
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This study investigated the claims made in the popular press about the “digital native” generation as learners. Because students' lives today are saturated with digital media at a time when their brains are still developing, many popular press authors claim that this generation of students thinks and learns differently than any generation that has come before, but the evidence to support these claims is scarce. This study used a survey to gather data on the technology use of university freshmen, the degree to which they identified with the claims being made about their approaches to learning, and the productiveness (in terms of focused attention, deep processing, and persistence) of their approaches to learning.Valid surveys were received from 388 freshmen at a large Midwestern land grant university. A factor analysis was used to identify meaningful patterns of technology use, and descriptive statistics, analysis of correlations, and extreme group t-tests were used to explore the relationships between technology use patterns and learning characteristics. The findings indicate some positive correlations between use of digital technology and the characteristics ascribed in the popular press to the digital native learners, and negative correlations between some categories of technology use and the productiveness of student learning behaviors. Overall, however, the small to moderate relationships suggest a less deterministic relationship between technology and learning than what the popular press writers claim.