The Impact of Cultural Familiarity on Students’ SM Usage in Higher Education
ᵅDhuha Al-Shaikhli, ᵇLi Jin, ͨAlan Porter, Andrzej Tarczynski
ᵅᵇDept of Computer Science and Engineering, University of Westminster, London, UK
ͨ Dept of Psychology, University of Westminster, London, UK
Dilshod Ibragimov, Deneze Bektasheva, Mikhail Shpirko
Dept of Business Information Systems, Westminster International University in Tashkent, Tashkent,
Using social media (SM) in Higher education (HE) becomes unavoidable in the new teaching and
learning pedagogy. The current generation of students creates their groups on SM for collaboration.
However, SM can be a primary source of learning distraction due to its nature, which does not
support structured learning. Hence, derived from the literature, this study proposes three learning
customised system features, to be implemented on SM when used in Higher Education HE.
Nevertheless, some psychological factors appear to have a stronger impact on students’ adoption of
SM in learning than the proposed features.
A Quantitative survey was conducted at a university in Uzbekistan to collect 52 undergraduate
students’ perception of proposed SM learning customised features in Moodle. These features aim to
provide localised, personalised, and privacy control self-management environment for collaboration
in Moodle. These features could be significant in predicting students’ engagement with SM in HE.
The data analysis showed a majority of positive feedback towards the proposed learning customised
SM. However, the surveyed students’ engagement with these features was observed as minimal.
The course leader initiated a semi-structured interview to investigate the reason. Although the
students confirmed their acceptance of the learning customised features, their preferences to
alternate SM, which is Telegram overridden their usage of the proposed learning customized SM,
which is Twitter. The students avoided the Moodle integrated Twitter (which provided highly
accepted features) and chose to use the Telegram as an external collaboration platform driven by
their familiarity and social preferences with the Telegram since it is the popular SM in Uzbekistan.
This study is part of an ongoing PhD research which involves deeper frame of learners’ cognitive
usage of the learning management system. However, this paper exclusively discusses the cultural
familiarity impact of student’s adoption of SM in HE.
Keywords: social learning, localised, personalized, privacy, SM, familiarization.
1. Social Learning Theory
Social learning theory has been proven intensively as the primary construct of learners cognition
(Bandura, A. and Walters, R.H., 1977). One of the fundamental learning key elements is peer social
interaction (Vygotsky, L.S., 1980), including the online learning field (Abrami, Philip C. et al., 2011).
Furthermore; cognitivist, who are fundamental learning theories specialists, suggest that the human
being is an active learner who select, filter and evaluate based on their needs and goals (Collins, a,
Greeno, J.G. & Resnick, L.B., 2001). Moreover, social learning overlaps with significant positive
learning skills such as learning self-regulation LSR. Learning self-regulation is described as a
“learners’ ability to independently and proactively engage in self-motivating and behavioural
processes that increase goal achievement” (Zimmerman, 2000). Self-regulated learners have the
ability to “initiate meta-cognitive, cognitive, affective, motivational, and behavioural processes in
order to take actions, achieve their learning goals and persevere until they succeed".
Peer collaboration is expected to increase when free and open SM (SM) is available (Veletsianos,
2017). For example; Twitter has been used in previous studies to support learners’ collaboration (Tur
and Marín, 2015). Twitter has default features that are advantageous for learning-oriented
socialisation. Twitter allows only 140 characters per tweet, which, in this study, is presumed to
increase peer collaboration effectiveness by reducing text length. Recent studies have empirically
demonstrated that using SM in learning improves learners’ self-regulation, cognition, and meta-
cognitive skills (Blaschke, 2014).
2. Learning Collaboration in SM
The learning process in higher education has changed in the SM era (Popescu, 2014). Students are
more interactive and present on SM platforms than any other platform (Selwyn, N., 2012). While
some researchers question whether SM can be used as a learning platform (Hrastinski, S. &
Aghaee, N. M., 2012), others believe it is widely adopted among learners in higher education, as
some students view it as a successful LMS, like Facebook (Ouya, S. et al., 2015) . Furthermore, it
can potentially cater to several (social) learning theories (Goodyear et al., 2014). For example, it is
an effective platform for the inquiry-based approach and is an ideal tool for peer collaboration, and to
be an effective platform for resources and peer knowledge sharing (Mazman, S.G., and Y.K. Usluel,
Some researchers believe that SM does not necessarily provide learners with cognitive learning, as
they use it more for socialising and non-academic activities (Selwyn, N., 2012). Furthermore, other
researchers have argued that only a minority of learners, in fact, utilise SM for precise learning
purposes (Prescott, J., S. Wilson, and G. Becket, 2013). These arguments have been changing
rapidly in the SM learning field, as SM integration into formal (and non-formal) education
investigating the integration of SM into LMS has limited empirical studies (Greenhow, C. and Lewin,
According to a recent study, some students show resistance toward using SM in their learning. The
qualitative survey reported that students tend to separate their personal life from their learning. Also,
they are concerned about their shared content’s judgment, and they are not keen on the extra time
and information constraints that SM might add (Jones, Norah. et al., 2010) . Also, other research has
identified privacy and anonymity as other hindering factors in students’ usage of SM in their learning
Authors have argued against the adoption of SM as a peer collaboration platform in formal learning.
For example, one study revealed that time spent on Facebook negatively impacted students’
achievements (Kirschner, P. A., and A. C. Karpinski , 2010) and assignment completion (Junco, R.,
and S. R. Cotton, 2013). However, it is worth mentioning that the LMS environment is continually
evolving towards more social connection and faster access to contents. For example, a recent LMS
is ‘Tagging’, which allows for personalised and more accessible collaboration among peers (Klašnja-
Milićević, A. et al., 2018).
3. Learning Social Engagement Features
Recent researches are investigating the integration of SM into HE (Cooke, 2017). However, the
nature of the SM platforms remains a questionable collaboration environment for students in HE.
Accordingly, this study proposes three features to be implemented on the integrated SM as they are
presumed, based on the literature, to enhance students adoption of SM in HE. The four features are;
localized collaboration, content personalised collaboration, and privacy self-management.
Researchers have investigated the potential of SM in formal learning, as it conserves a significant
amount of contents (Dabbagh and Kitsantas, 2012). Although SM does not support the pedagogical
approach to learning (Liu, Y., 2010), the recent generations of university students are using it as the
leading tool for content creation and reflection (Tess, 2013). Although SM has been described as a
new form of a decentralised learning platform (Junco, R., and S. R. Cotton, 2013), in HE, using SM
could be disadvantageous for the students’ learning process as it can easily cause students’
attention to drift (Abe, B. P. and Jordan, N. A., 2013).
The volume of shared content and the diversity of learners’ backgrounds can negatively influence
the learning experience on SM (Chen, X. et al., 2014). The decentralised learning process is another
drawback of using SM in HE, as the relocation of students from the LMS to an SM platform can
quickly isolate them from structured learning. Also, many students believe that all information should
be in one place when it comes to formal learning (Salmon, G. et al., 2015). Accordingly, this study
proposes an integration of SM platform (which is Twitter in this case) into an LMS (Moodle) with
three learning customised features. These features are discussed next.
3.1. Localised Collaboration LC
An LC is represented by implementing a localized SM collaboration panel in each section of the
LMS. It is presumed to improve learners’ perceived ease of use. Also, this may support students’
LSR skills (such as focus, time management), as they will have fewer tasks to manage themselves
(such as moving between the SM platform and the LMS).
3.2. Content Personalised Collaboration
One of the repeatedly reported barriers to learners’ use of SM in learning is content overload (Ri,
Son and Kyu, 2016). A large amount of SM user-generated content can limit its benefit as a source
of information. In this research, SM panel is enabled in each section, as discussed above. Moreover,
each section’s SM panel personalises the contents generated on it.
3.3. Learners’ Privacy Self-Management
Tu (2002) describe privacy as the perception of respect across psychological, mental, cultural, and
conditional boundaries and dimensions’ (Tu, C.H., 2002). In the literature, user privacy in SM is
defined as an individual’s autonomy over his or her personal information, including any relevant
exchanged content (Shin, 2010). The current research investigates the main reported aspects that
have prevented learners from using SM in HE if any. Since SM is a two-communication-channel
platform, the present study examines common negative influence factors in each channel (inspired
by (Leonardi, 2017)); barrier factors that influence information contribution (post, reply, like), and
factors that affect information retrieval (read, search).
Based on the literature, one of the main (information contribution) barriers for learners to use SM in
learning is privacy concerns (Blaschke, 2014) . The behaviour of using SM is significantly influenced
by the individual’s perspectives of the SM community (Taddicken, 2014). This inhibits and restricts
user activity and interaction over the platform and might limit or divert their actual behaviour (Vitak,
2012). Few studies like (Prinsloo, P. and Slade, S., 2015) have investigated learners’ privacy self-
management methods to overcome their identity disclosure concerns in using SM. Hence, the
present research examines learners’ perception of privacy self-management in SM as a predictor of
their acceptance of using SM in HE.
4.1 Questionnair’s aim and objective
The aim of this questionnaire is to measure the students’ proposective feedback toward the research
proposed method. The method is an integration of SM in formal learning learning manangement
system. In addition, three features are proposed to be implemented on the integrated SM to support
learners’ engagement. In order to achieve the questionnaire aim, each feature were represented on
the questionnaire to enable the students’ view. In addition, the students were required to respond to a
standard technology acceptance model questionnaire items which were extracted from the literature.
These items empirically proved to measure high accuracy acceptance level of the sample.
The population is the entire group of individuals, events, or elements of focus that the researcher
intends to evaluate. A sufficient sample is selected from the wider population to investigate it
(Trochim, 2002). Current research population comprises the undergraduate students (male and
female) at the International University of Westminster in Tashkent (WIUT). The WIUT is a
partnership branch of the University of Westminster in UK, and it is based in Tashkent, Uzbekistan.
The participants of this questionnair have a similar age group of (17-22 years old) since they are
enrolled to the same undergraduate course. All of the students have previous experience with IT and
Moodle as they have used it in previous years for their learning.
Sampling is “the process of selecting a sufficient number of elements from the population” (Sekaran,
U., 2003). Sampling is important when it is surveying the entire research population is not achievable
due to its vast size, time frame limitation, or regional boundaries (Saunders, M. et al., 2009).
The targeted sample of this research is concerned within a case study of undergraduate students at
WIUT from Business Information system department. The sample is undertaking two computer
science courses. And they access their learning contents on Moodle. There will be two cohorts to
undergo the case study of the current research; prior experiment and post-experiment on the same
Moodle. The total number of student for the prior experiment, which is cohort 2019, is 457 students.
4.4. Data Collection
A quantitative questionnaire was used in this study in the form of an online survey which has the
advantage of approaching a more extensive range of the population. Also, it is time and effort valid.
However, it also eliminates the presence of the researcher on the site where the survey is being
conducted, which might negatively affect participants’ completion rate. Also, it eliminates the
potential direct communication between the researcher and the participants (such as providing
clarification on specific questions), which may disadvanteg the quality of the survey responses
(Cooper, C.J. et al., 2006). Furthermore, quantitative questionnaires are practical for large
populations. Finally, recently developed online survey tools provide enough support to collect a
sufficient quantitative data from a large sample.
Statisticians have discussed five criteria of a sufficient questionnaire; these concern respondents’
attributes, respondents’ impact, false respondents, sample size, sample type, and how many items
are in the questionnaire. A technique that makes a questionnaire successful is to conduct a pilot
survey to obtain feedback on the design and the comprehension of the survey questions (Robin
By implementing the criteria above for a sufficient questionnaire, an online self-reporting Likert-scale
questionnaire was used since this study is conducted remotely from London. Also, an online survey
has been chosen because of the large sample size as well as the number of survey items is
4.5. Questionnaire development
4.5.1. Validity and Reliability
Questionnaire design is vital to the data collection phase as it can impact data response scale, data
validity, and data reliability (Heale and Twycross, 2015). Questionnaire validity is concerned with the
accurate presentation of the data to be measured, which is the researcher responsibility.
On the other hand, a questionnaire is reliable if it requests the same specific type of data using the
same approach and standardised format through different periods and across the various
environment, and if it collects the responses to the questionnaire using one unified method of data
collection (Richardson, J.T., 1990). Finally, a questionnaire is reliable and valid when the items are
understood and perceived by the respondent precisely as intended by the researcher. Conversely,
the collected data should be perceived by the researcher precisely as intended by the respondent
(Mark S. Litwin, 1995).
4.5.2. Survey Items
In the current study, the questionnaire evolved through several phases to establish its validity and
reliability. Fifty-two items were developed to cover all model variables. Although the questionnaire
items were driven from the literature, they have been through several phases of editing and re-
wording to remain within this research context.
4.5.3. Survey Scale Type
The second phase of the questionnaire development was to determine the survey scale type. Likert
scale requires individuals to respond to a direct statement with a range of agreeing to disagree
answers; the scale can be on five or seven points. In this study, a five-point Likert scale was
adopted, with strongly disagree as to the lowest score and strongly agree as to the highest one.
Furthermore, each question is formed as a statement with which the students must indicate whether
they strongly agree or strongly disagree using five scale points.
4.5.4. Survey Design and Tools
The third phase focused on the design of the questionnaire layout. Questionnaire attributes such as
question layout, general presentation, and short and simple question formation (considering
simplicity and specificity) have a significant impact on completion rate and on minimising error (Lietz,
2010). The questionnaire in this study was developed as an online survey using the Qualtrics online
survey tool. Moreover, Rada (2005) highlights the effects of a well explained cover letter on survey
response rate (Rada, 2005). In corespondance to that, the invitation email of this study described the
aim of the research and the intention of the survey, the voluntary contribution, and the personal
privacy code of the collected data.
4.5.5. Questionnaire Pre-Testing Phase
The last phase of the questionnaire development involved pre-testing for validity, reliability, errors,
and mistakes (Presser, S. et al., 2004). The questionnaire went through the following pre-testing
phases. The preliminary version of the questionnaire was examined by the supervisory team of this
study. Feedback was suggested concerning the questionnaire design, layout, and the use of more
straightforward language. Based on the received recommendations, the questionnaire was revised.
The next pre-test method was a pilot study. A pilot study is an essential practice as it mimics the
data collection process to detect potential pitfalls in order to improve and prepare the survey for the
actual data collection phase (Van Teijlingen et al., 2001). In this study, the pilot study was operated
by recruiting postgraduate psychology students from the University of Westminster, UK. A total of 16
responses were collected, of which seven did not include missing data. The sample size suggested
in the literature for a pilot study ranges from100 to 200 responses; however, this size was not
satisfied, which is one of the limitations of this study.
The final approach to examine the questionnaire was performed by the PhD researcher, PhD
supervisory team, and the involved instructors of the Moodle signed up a course. The researcher
reduced the survey size while maintaining the same number of items by using the matrix answer
format in the survey. This approach decreases the amount of text and images. The survey design,
matrix response format, and the included consent cover letter were reviewed and confirmed as
ready for the actual data collection phase of the study.
5. Data analysis
This section provides the students’ perspectives on the proposed features. It starts with the students’
perception of the localized SM on each section on the Moodle; then it discusses the topic of the
personalized collaboration feature of the integrated SM. The third presented data analysis is the
students’ perception of SM privacy self-management. The last discussion in this section tackles the
students preferred SM platform for learning which explains why the integrated Twitter was not
utilised by the students even when it is integrated on their Moodle.
5.1. Students perception of Localized Twitter and Topic personalised collaboration
Similarly, the students’ perception of the localised integrated SM panel on each section on the
Moodle was highly positive. The acceptance range of 52 students was between 82%-95%. However,
this feedback is not aligned with the students’ actual interaction with the localised Twitter panel on
Moodle of this study. As discussed earlier, the students are not familiar with Twitter, this was the
main reasons for not interacting with it even when it provided a learning customized features. Based
on the students’ feedback, the learning customized features have been perceived by the same
students to support their learning positively. Below is data visualisation of the students’ responses on
the questionnaire followed by a summary table of their acceptance answers on the theorised
Survey Items Localised Twitter
panel on each
It makes my learning more effective 86% 98%
I can block out most other distractions when I use it 82% 92%
It is easy to access 90% 94%
It is useful for my learning. Therefore I use it 91% 94%
I utilise it as I find it is easy to use 93% 90%
I use it because I can block out most other
distractions when I interact with it
I find it useful for my study skills as it helps me to
plan for my learning tasks
It helps me to remain focused which improved my
learning regulating skills such as study time
management and study tasks planning
It helps me with setting my learning goals and to
remain motivated toward learning
5.2. Students’ perception of Privacy self-management
The students’ perception of the SM privacy self-management feature was a little bit lower than the
previously discussed SM features. However, it remains high perception as the affirmative
acceptance ranged between 79%-89%. The following summary table provides further insight into
SM ID privacy management survey items Percenta
I feel less anxious when I can control my twitter ID privacy on Moodle 79%
I feel free to contribute on the Moodle-Twitter when I can control my twitter ID 81%
I use the integrated Twitter as I feel less anxious when I can control the privacy of my
Twitter ID on Moodle
I use the integrated Twitter as I feel free when I can control the privacy of my Twitter
ID on Moodle
5.3. SM for learning based on cultural preferences
In the literature, the cultural impact on users’ adoption of SM has been explored from different
aspects; such as political, economic, and the fundamental rules of internet usage of the country
(Bolton, R.N., 2013). Accordingly, it is clear to identify a preferred SM in each country. For example;
Facebook and Twitter are used by the majority of people in the USA, while in China, the majority of
people use Weibo and RenRen (Forbush, E. and Foucault-Welles, B., 2016).
The last question on the survey explored the preferred SM by the WIUT students. The students
provided positive perspectives on the proposed features of the embodied Twitter on Moodle.
However, their actual usage was significantly limited. As discussed earlier, the student's qualitative
feedback revealed that they are not familiar with Twitter. This was confirmed in the survey as they
voted for Telegram, followed by Facebook as their preferred SM platform for learning. Furthermore,
the course leader reported that the students created their own Telegram groups as they are familiar
with it for collaborative learning.
6. Conclusion and future work
The result of this study worths further investigation as it touches significant factor of learners’
acceptance of SM in formal learning. This factor is Cultural familiarity of SM acceptance in formal
learning which seems to override a positively accepted customized learning environment in
unfamiliar SM. The WIUT students chose to collaborate on external SM platform even when they
had an integrated (learning customised) Twitter on their Moodle. Moreover, regardless of their
positive perspectives of the Twitter features, their familiarity with Telegram derived them to use it
A wider sample population, and more case studies are required to be covered in order to formalize a
conclusion on how much cultural traits influence SM acceptance in formal learning. The question this
study rais is which feature that can be technically manipulated to segnificanlty influence learners’
acceptance of new SM environment for collaborative learning? Future studies requires further
investigation on the personal and cultural traits that can significantly influence learners’ acceptance
of SM in formal learning.
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