What is the framework for building collaborative learning networks within higher educational institutions?


Evaluating Virtual Learning Communities
A fundamental task for educational technology professionals is to help companies and educational institutions run their businesses and stay competitive and responsive to the demands of their consumers. This requires you to be familiar with emerging teaching and learning tools to deliver collaborative courses and training. For this Discussion, you will analyze the concepts of sense of community and collaboration in eLearning environments. You will also evaluate virtual delivery systems to determine which system best suits the needs of a virtual learning community in your workplace.
To prepare:
Read A Framework for Collaborative Networked Learning in Higher Education: Design & Analysis, which explores the framework for planning, designing, and implementing collaborative learning systems. Review Using Social Network Analysis to Understand Sense of Community in an Online Learning Environment and the research findings of the unique interaction patterns for students in online courses. Finally, view the media piece in this modules Learning Resources with a focus on the Delivery section.
Consider the variety of virtual delivery systems, which exist for the creation of learning communities (such as, mobile and micro learning; social media; massive open online courses, called MOOCs; learning management systems/online learning spaces; and video conferencing). Research and choose one or a combination of delivery systems that you believe are best to create a virtual-learning community in your workplace.
Post the following by Day 7 of Week 7:
Explain how a strong sense of community can be established and maintained in an eLearning environment as well as contrast sense of community with collaboration. Explain why your choice of a virtual delivery system(s) is the best delivery model for your workplace. Include in your explanation how it maximizes access for your audience as well as how it enhances the ability of your audience to connect and share a sense of community. Defend your choices from personal experience, a learning theory, andat least oneresearch study (PhD and EdS student).
A Framework for Collaborative Networked
Learning in Higher Education:
Design & Analysis
Ghassan F. Issa, Haya A. El-Ghalayini, Ahmad F. Shubita, Mohammed H. Abu-Arqoub
University of Petra, Amman, Jordan
AbstractThis paper presents a comprehensive framework
for building collaborative learning networks within higher
educational institutions. This framework focuses on systems
design and implementation issues in addition to a complete
set of evaluation, and analysis tools. The objective of this
project is to improve the standards of higher education in
Jordan through the implementation of transparent, collaborative, innovative, and modern quality educational programs. The framework highlights the major steps required
to plan, design, and implement collaborative learning systems. Several issues are discussed such as unification of
courses and program of studies, using appropriate learning
management system, software design development using
Agile methodology, infrastructure design, access issues,
proprietary data storage, and social network analysis (SNA)
Index Termscollaborative learning; networked learning;
social network analysis; network design; learning community.
This paper is based on a proposal for the implementation of a major project that connects several higher educational institutions in Jordan in a collaborative networked
environment. The paper will formulate the implementation steps into a general framework that can be easily
adopted by similar projects.
Jordan is considered one of the leading countries in the
Middle East in higher education, and the first in the area
to implement privatization of quality higher education.
However, in the past few years, several factors have been
negatively affecting higher education in Jordan including
the incompatibility of learned skills and those required by
employers, outdated teaching and learning methods, and
the inefficient use of technology in education [1].
Development of Jordan society in recent years is going
through major transformations in terms of socio-economic
and political issues, change of priorities in life, moral concepts, and moral norms. Social changes posed new challenges for higher education requiring a revision of traditional teaching methods, technologies, and services. The
ongoing reform of the educational system in Jordan aims
at creating the necessary conditions for achieving high
quality learners, who will successfully interact in a national and international cooperative environment. However, we believe that reform efforts must take into account
some serious challenges related to learning, research,
community service, and innovation. Currently, higher
educational institutions in Jordan suffer from the lack of
collaboration or linkage with secondary educational system, industry, research groups and research centers, and
innovations. There are hardly any existing learning community spaces that engage policy makers, educators, civil
societies, or the new generation of millennium students.
Needless to say, there are virtually no existing learning
communities, nor a reasonable technological infrastructure that facilitate proper collaboration between existing
higher educational institutions.
To overcome the above mentioned shortcomings, this
paper presents a framework that takes advantage of the
latest development of Information and communication
technologies (ICT), and social networking to connect several higher educational institutions in Jordan in a collaborative networked environment. The implementation of the
proposed framework aims at enhancing higher education
from different perspectives including policy makers, administrators, teachers, researchers, and students.
The proposed framework employs ICT to enable educators to adopt new learning methods for the realization of
modern concepts in education based on learner-centered
approaches utilizing social constructivism learning theory
such as: network-based learning, problem-based learning,
collaborative learning, and competition-based learning
[2], that reflect the basic ideas and principles of humanistic pedagogy.
The work introduced in this paper is based on Vygotskys theory [3] which states that collaborative learning is
a cognitive development that depends on full social interaction which leads onto online learning communities that
include interactivity, social context, and technologies.
Additionally, this research relies on the community of
inquiry framework [4, 5] for defining blended learning. In
the next section we will shed some light on some of the
major issues that relate directly to our proposed project
including blended learning, collaborative learning, and
networked learning.
A. Blended Learning
Blended learning or as sometimes referred to as
“hybrid”, “technology-mediated instruction”, “webenhanced instruction”, or “mixed-mode instruction”, is a
student centered (constructivist) approach based on faceto-face and computer mediated learning which blends
collaborative and self-reflective learning activities.
Blended learning employs traditional methods such as
lecture and group-based learning with new approaches of
online and networked learning through social media, thus
observing individual differences in learning methods and
knowledge accumulation.
Hope [6] identifies a set of factors for the prevalence of
blended education such as: motivation of social communication among students, students and teachers, and teachers
themselves; enhancement of learning achievements of
students, by taking into account their pace, capacities and
circumstances at any time and place; enhancement of students’ ability to work in small groups and in a team spirit;
enhancement of the quality of teaching materials, and
teaching and learning skills.
Osguthorpe & Graham [7] on the other hand, suggest
that blended course structure varies depending on the
goals that are included in the design, where the goals can
be one or more of the following: pedagogical richness,
access to knowledge, social interaction, personal agency,
cost effectiveness, and ease of revision.
Community of Inquiry framework views blended learning from a socioconstructivist perspective [4, 5]. In order
to create a learning community, this model defines three
major elements: teaching, cognitive, and social presence.
According to this model teaching presence provides the
structure to a course which directs cognitive and social
Alternatively, the Blended Learning Curriculum (BLC)
model presented by Huang, Ma, & Zhang [8], defines
three characteristics of blended learning as follows: flexible learning resources, learning diversity, and e-learning
experiences. BLC model focuses on personalized learning
and just-in-time transfer of skills.
B. Collaborative Learning
Collaborative learning by its nature depends on the sociocultural and activity theories [2, 9]. There have been
several attempts by researchers to define collaborative
learning from different perspectives. Some researcher
defined it based on the skills that is shared and transferred
from one learner to an other in a group setting [10], others
proposed a definition based on knowledge creation and
sharing among learners [11]. There is also a definition
that focuses on the methodologies and environments that
facilitate sharing of common tasks [12]. One last definition that is worth mentioning states that collaboration
learning is a “coordinated, synchronous activity that is the
result of a continued attempt to construct and maintain a
shared conception of a problem. This definition assumes
that learning is greatly enhanced when the learners exist in
a collaborative environment [13]. All of these definitions
agree with the fact that people can learn through rich social interaction, as mentioned by [14].
A by-product of research on collaborative learning is
the concept of learning community (LC) which consists
mainly of three major components: interactivity, social
context, and technologies [15]. Accordingly, collaborative learning can be considered as interaction between a
learning community members for the purpose of learning
[16]. Learning communities is said to play a major role in
the success and persistence of higher education [17].
A separate field of research has emerged as a new educational paradigm, where computer-supported collaborative learning (CSCL) plays a significant role in enhancing
the learning process [18]. CSCL research is focused on
iJET Volume 9, Issue 8: “Learning in Networks”, 2014
how learners interact together using computer mediated
communication techniques. CSCL provides better understanding and more effective techniques to the applications
of blended learning [19].
C. Networked Learning
Networked learning is a new form of CSCL that uses
what is called “Network Environment” (connections),
which assists collaboration between a group of learners;
instructors and learners; educators; a learning community
and its resources. Therefore, the network environment
can help the participants in extending and developing their
capabilities and understanding in ways that are meet their
aspirations [20]. Networked learning is related to theories
of distributed cognition [21, 22] and is rising with the
emergence of Web 2.0 technologies. Thus a learning environment is basically a physical environment that facilitates social interaction which allows learning to be distributed over space and time. From this concept, the central concern of networked learning is the argument about
the relationship between using a technology and designing
that technology [23]. The concept of networked learning
has been described as “social intelligence design” based
on three different aspects: mind, society, and matter [24].
Some researchers believe that the interaction and collaboration through a network transforms learning, memory,
and intelligence from the individual level to the social
network level [25, 26].
Building a collaborative networked learning system for
higher education is quite challenging. The process requires real collaboration and cooperation between involved universities. In order to proceed in such a system
properly, software engineering methodology, namely,
AGILE methodology is applied. The advantage of using
Agile rather than the more traditional Water fall approach
is that the former relies more on customer collaboration
rather than contract negotiation. This concept seems very
reasonable for our project which involves experts in education, administration, and information technology. In
summary, the resulting system reflects the collaboration
and cooperation efforts by those experts representing different higher educational institutions. The system’s requirements and objectives can be stated as follows: Design and implement a comprehensive networked learning
environment between several universities located in Jordan. The system must be able to create a learning community capable of collaborating at all levels: administration level, program level, instructor level, course level,
research level, and student level. Figure 1 illustrates a
general framework showing the interaction between major
systems components in a networked learning community.
The system must be designed bearing in mind proprietary data to be located at each university servers. New
contents generated as a result of collaboration such as
unified courses are to be located in the central server,
which is shared by all participants. The system must provide users with a full range of collaboration, communication, and learning tools. Security issues are important including user authentication, role assignments, and access
privileges. The system must be flexible, upgradable, interoperable, and must accommodate new users, and new
institutions that will be added in the future.
Network analysis tools must be designed and made
available to users and to administrators. Social networks
are used to examine how institutions interact with each
other, characterizing the many informal connections that
link instructors together, as well as associations and connections between individual students at different institutions. In addition to SNA other methods such as online
surveys and questionnaires are to be designed to measure
the satisfaction of users at all levels.
We can summarize the objectives of the proposed system as follows:
1. Construct a higher educational collaboration network
where decision makers, teachers, students, and researchers constitute a learning community.
2. Enhance learning pedagogy for undergraduate and
postgraduate courses.
3. Share existing study programs, courses, resources,
and activities.
4. Share and develop partnerships in research and project development.
5. Provide an environment for socialization.
6. Share news, events, and announcements.
7. Adopt best practices in teaching and learning according to national and international standards.
The next sections will introduce the design methodology using Agile approach.
The following, shown in Table 1, are the preliminary
project milestones and deliverables established for our
TG1: Epics are defined including architecture at high
level. Epics are a very large user scenario of the project
that is eventually broken down into smaller scenarios.
TG2: Overall scope of the project is defined and estimated and our team can start detailing the project plans.
B. Using Moodle 2.x LMS
Moodle is a free open source learning management
system written in PHP, and is deployable on most
operating systems including Unix, Linux, Windows, and
others. Moodle design is based on the constructivist and
social constructivist approaches of learning thus providing
a flexible environment for learning communities.
Developers using Moodle have full access to the source
code, Moodle Documents, and Help. There are several
aspects of Moodle that makes suitable for this project.
Moodle has an easy, responsive, and personalized user
interface suitable for desktop and mobile applications.
A comprehensive set of collaboration tools and activities
such as forums, wikis, glossaries, and database activities.
Given the problem definition in the previous section
along with the specified objectives, this section will focus
on the design process using the Agile software engineering development methodology. This Agile methodology
seems very suitable for our project as it relies more on
customer collaboration rather than contract negotiation.
A. Agile Software Development Methodology
The Agile movement proposes alternatives to traditional project management and software development methodologies. Agile approaches are typically used in software
development to help businesses respond to unpredictability [27].
One of the most important differences between the agile and sequential approaches is that sequential features
distinct phases with milestones and deliverables at each
phase, while agile methods have iterations rather than
phases [28]. Agile development methodology helps in
assisting the track of a project during the development
lifecycle. This is done through regular pieces of work
called iterations, which must present a balanced product
increment [27].
Agile project plans are based on features. Figure 2
shows the Agile software development milestones. Agile
plans projects when main features will be submitted to
construction and the most recent iteration tends to have
additional details [29].
Agile project plans are arranged into time-bound
iterations from 2 – 4 weeks in length [28]. All of the
compulsory work, from idea generating to a fully working
product, is completed without preventing the work from
being done in parallel. This gives stakeholders a better
idea of the project progress, as they can use the end result
when it becomes available [28].
Figure 1. Framework for Networked Learning Community
Figure 2. Agile Software Development Milestones [29]
Budget approved
for Speculation
Resource is assigned for Initiation
phase to be completed
Initial product
backlog complete
Epics (user scenarios) are defined
including architecture at high level
Speculate phase
Overall scope is defined and estimated, Resource available, team can
start detailing the plans
An advanced calendar management system which can be
automatically populated with new events, due dates and
deadlines and which works on the system’s level, user’s
level, or course level.
Advanced and simple to use features for administrators
that include: customable site design and layout, detailed
role and permission management, support open standards,
bulk course creation and easy backup, detailed reporting
and logs, high interoperability, and security.
Flexible course development features including:
choosing appropriate pedagogy (instructor-lead, selfpaced, blended, or online), course collaboration tools,
embedding external files, group management, multimedia
files, marking and grading flexibility and in-line marking,
peer and self assessment, and outcomes and rubrics.
Ability to observe peer’s activities using online users
block, and recent activities block.
Connection between participants using email, online
messages, chatting, and push notification.
One of the major advantages of Moodle 2.x is the
ability to connect several sites using Moodle as one
network, thus creating what is called Moodle Community
Hub (Fig. 3). Users can access all the nodes in the
network transparently but securely (based on their
privileges) in a single sign on.
C. Unified Course Approach
One of the objectives for the collaboration between different institutions is to learn and use best practices on all
levels of learning. Involved institutions sit together and
share their experience starting from the program of study
level, going through course contents, and ending with
course material. The result of this type of collaboration
should be transformed into what we call a unified course.
While the term unified could be misleading, we can
define it as follows:
“A Unified Course is the result of sharing experience
and expertise in all facets of a course. It is a generalized
course containing core topics as well as other optional
topics. It also covers pedagogy, course contents, course
supportive material and reference, assessments, and so
on. The course adheres to national and international accreditation bodies while satisfying the intended learning
outcomes that are directly aligned with program objectives.”
Working on the unified course is usually carried out
during the planning and design phase of the project. A
coordinator is assigned the task of putting together the
final unified course, upload the results to the learning
management system, and provide for future and continuous collaboration, maintenance, and updates. Figure 4
shows the unified course concept.
Other instructors from different universities are also
asked to upload their regular courses, and course material,
thus at this point the system will consist of a unified
course existing on a central location, along with a number
of other institution-specific courses located in different
spaces, connected all together through the Learning management system.
New institutions who wish to join the network at this
stage, can take advantage of the unified course to build
their own specific courses, and at the same time may provide the network with feedback and suggestions to update
the unified course.
iJET Volume 9, Issue 8: “Learning in Networks”, 2014
Figure 3. Collaborative network based on Moodle LMS
Figure 4. Collaborative network for the production of unified programs
Involved instructors from different universities can now
enjoy a collaborative environment and can share teaching
and research experiences. Students on the other hand, can
communicate with their counterparts from within their
institution and with other students from different universities. They can also communicate with other instructors,
and get involved with lessons administered by different
D. Collaboration from Student’s Perspective
Students are considered the main actors of the proposed
learning system. Accordingly, special attention must be
placed to provide students with an easy to use collaborative environment which can be useful and fun to use. Students can collaborate with fellow students from different
universities using different activities. They can also collaborate with their instructors as well as other instructors
from different universities. Students also have access to
the unified courses and their resources. Figure 5 depicts
student’s access and collaboration.
E. Collaboration from Teacher’s Perspective
Teachers have the opportunity to share their experience
and teaching philosophy, not just on the course level, but
also at the program level. They have access to the unified
course as well as to other fellow teacher courses from
different universities. Experienced teachers as well as
newcomers can share teaching pedagogy, course text
books, course outline, resources and references, and research ideas. Research groups amongst interested teachers
can be easily formed, thus providing a fertile ground for
future collaboration in research. Teachers can also stay in
touch with their own students, as well as other students
from different universities.
F. Other issues related to design
One of the strongest features of using Moodle LMS in
the implementation of networked learning is its flexibility
in controlling user access. The system permits administrators, teachers, and content creators to totally control who,
what, when, and how others access their contents. Using
this advanced feature of Moodle truly helps in organizing
the most complex network. For any user to have access
his/her username (profile) must exist within the system’s
database which can be accomplished in a variety of methods such as direct uploading, LDAP authentication, or
online registration. Once a user profile exists within the
system’s database, it can be assigned any role by the administrator.
In order to manage users and their roles within a complex network consisting of users from different universities, we view the user database as a hierarchy.
A. Social Network Analysis(SNA)
The second part of the framework is the analysis of the
participants interactivity that involve in the collaborative
network learning within and among the institutions. This
part intends to utilize the social network analysis technique that assists in understanding the interactivity of participants networked learning. SNA aims to identify the
relationships based on how the actors are connected with
each other [30, 31]. For example, Moore (1989) [32] identifies that students interactivity are three types of interactions in learning environments: learner-content, learnerinstructor, and learner-learner.
We propose that SNA can help in representing and
mapping participants relationships in the NL. This leads
on to generating additional analytical data about the interactivity between the members of NL, and understanding
behavior in NL environments. In our case, SNA is used to
understand the flow of information, and the exchange of
resources or activities among members of NL. Our primary focus in the analysis part is to study the exchange messages in the course activities and resources.
SNA can be used to visualize and represent the social
environment as a network based on the relationships by
creating a graphical representation called a sociogram.
The nodes of the sociogram represent the participants and
the edges represent the connections between the nodes.
The major analytical data from SNA calculates the cohesion of a network. The most important measures of SNA
are density and centrality. Density as a measure is a
degree of the overall connections between the participants. The density of a network is defined as the number
of edges in a network divided by the maximum number of
possible edges [30]. The value of density varies between 0
Figure 5. Collaboration Diagram showing a sample of two institutions,
one program of study, and an instance of one course for each
and 100% which means that the higher the density value
is, the more interactions exist within the network [30, 33].
Centrality as a measure provides the participant that plays
the central role in the network [31]. In our, case, this can
reflects on the institution, course activity, resource, instructor, and student.
This centrality can be done for each participant in the
network by measuring the number in-degree and outdegree values. In-degree centrality counts the number of
messages received to certain participant, whereas outdegree counts number of messages a participant has sent
to other participants [33].
The SNA technique is used commonly in sociology and
organizational studies, but it has been utilized in varies
NL/CSCL research to study group interaction and, communication [34]. For example, Martinez, Dimitriadis, Rubia, Gomez, and de la Fuente (2003) [35] found that
teachers presence has an effect on the network density.
Daradoumis, Martinez-Mones, and Xhafa (2004) [36]
used SNA to assess and identify the performance of the
most effective virtual learning groups.
B. Evaluation for collaborative learning network
The evaluation stage of the collaborative learning network frameworks intends to quantitatively examine the
relationships between perceived collaborative learning
with participants satisfaction in different unified courses.
For example, LaPoint and Gunawardena (2004) [37] denote that student satisfaction can be considered as an effective measure of learning outcomes. This will be implemented by preparing a questionnaire that will discover
what participants’ opinions and attitudes are towards the
unified course contents, activities and resources. Participants will be people with various roles, including students,
from different institutions involved in the networked
learning. Results will be analyzed using different statistical techniques.
The work presented in this paper has been guided by
the authors’ vision to enhance higher education in Jordan
through collaborative work involving a number of local
universities. The enhancement of higher education was
translated into several goals and objectives to be satisfied
through the implementation of a system which is conceived, planned, and implemented as a result of shared
knowledge, experience, expertise, and resources of participating universities. In order to arrive at a comprehensive
system which successfully connects hundreds of stakeholders, starting from top policy makers down to the student level, the authors realize the need to convert the actual implementation steps into a general framework. The
framework which is based in principle on previously studied learning theories provides interested researchers with
general guidelines and procedures which can be easily
customized to fit future learning projects.
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Ghassan F. Issa, Haya A. El-Ghalayini, Ahmad F.
Shubita, and Mohammed H. Abu-Arqoub are with
University of Petra, Amman, Jordan.
Submitted 22 April 2014. Published as re-submitted by the authors 26
May 2014.
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University of Missouri-Columbia
Yale University
University of Missouri-Columbia
This study uses social network analysis (SNA) in an innovative way to
describe interaction and explain how interaction influences sense of community of students in online learning environments. The findings reveal
differences on sense of community between two similarly structured online
courses, and show unique interaction patterns for students in the two courses.
These results should prove to be interesting to researchers of online learning
both on a theoretical and methodological basis.
The most popular format of distance learning today is online learning, which is
defined as any class that offers its entire curriculum in the online course delivery
mode, thereby allowing students to participate regardless of geographic location,
independent of time and place (Richardson & Swan, 2003). Compared to
2008, Baywood Publishing Co., Inc.
doi: 10.2190/EC.39.1.b
18 / SHEN ET AL.
traditional education formats, online learning is generally considered more costeffective and more convenient for learners. However, since online learning
environments lack face-to-face communication among students and between
students and instructors, online learners may experience feelings of isolation
which can lead to dropping out (Cereijo, Young, & Wilhelm, 2001; Curry, 2000;
Rovai & Wighting, 2005). Feelings of isolation have been related to a low
sense of community (Haythornthwaite, Kazmer, Robins, & Shoemaker, 2000).
Based on a qualitative study Eastmond (1995) argues that feelings of isolation
can be alleviated when students work together in a community of learners and
support one another. Understanding how a community of learners form and how
social interaction may foster a sense of community in distance learning is
important for both building theory about the social nature of online learning
and for making practical recommendations to online instructors for methods to
counter isolation.
Rovai (2002b) demonstrated that building a sense of community can affect
student satisfaction and learning. Interaction has been shown to be a critical
element that influences sense of community in a learning context, because community members promote learning through dialog among members (Cunningham,
1992). Many researchers have examined interaction in online education and
highlighted the importance of interaction for successful online learning (Palloff
& Pratt, 1999; Shale & Garrison, 1990; Su, Bonk, Magjuka, Liu, & Lee, 2005).
Most of the available research has employed interview and survey methods to
study interaction, probing the perception of students; or employed content analysis
to analyze posts in discussion forums. While these methods study individual
students perspectives and expressions; there exists a different approach that
studies the group or community through the interaction patterns among individuals so as to understand the types and nature of interactions that occur in online
contexts. Social network analysis (SNA) provides a quantitative representation of
the patterns of relationships among online members; it also provides visualization
of patterns of interaction among members in networks, and enables quantitative
comparisons between different networks or groups of people. The SNA approach
offers a quantification and visualization of relations and interaction patterns that
are not provided through survey or content analysis.
This article describes the nature of interaction patterns in two online courses,
which were similarly structured and were perceived by students to have different
levels of sense of community. Given that prior survey and content analysis
research establishes an association between student interaction and sense of
community, the purpose of the current study is to determine if the ways that
interaction is articulated in SNA are also associated with sense of community.
Further the study examines the SNA representations to develop insight about
the social nature of online learning and the potential value of using SNA to build
theory and suggest improvements in online practices. The data are from surveys
of students sense of community and log files detailing the interaction among
the online students, instructor, and teaching assistant. The interaction type (read,
post, and revision, etc.), the time of the interaction, the target object of the
interaction, the actor, and the author of the target object were all recorded; in other
words, the system captured the who, when, how, and where details of interaction.
Sense of Community and Interaction in Online Learning
Sense of community is defined as a feeling that members have of belonging,
a feeling that members matter to one another and to the group, and a shared faith
that members needs will be met through their commitment to be together
(McMillan & Chavis, 1986, p. 9). In online learning, Rovai (2002b) defines
learning community as a social community of learners who share knowledge,
values, and goals; he also concluded that sense of community in online learning
is comprised of two components: 1) connectedness, which refers to students
feelings of cohesion, spirit, trust, and interdependence (p. 325), and 2) learning,
which is students feelings of the extent to which their learning goals and
expectations are satisfied.
The contribution of sense of community to online learning has been investigated
by numerous researchers. Researchers have shown that sense of community in
online learning environments contributes to student motivation and increased
learning (Ertmer & Stepich, 2004; Palloff & Pratt, 1999; Wilson, 2001).
Researchers have also shown that sense of community helps reduce feelings
of isolation (Tinto, 1993) and facilitates learning by making more resources
available in the form of other learners from whom learners can seek help (Frymier,
1993; Gibbs, 1995). Ertmer et al. (2004) also stated that establishing a sense of
community is valuable because it helps provide the social context in which
learning occurs.
Sense of community is closely associated with interaction. According to
Carabajal, Lapointe, and Gunawardena (2003), there are three dimensions of an
online community: technological, task, and social; the social dimension refers
to a sense of belonging, social-emotional bonds, and good relationships through
frequent interaction during the online learning process. Tu and Corry (2002)
propose a similar theoretical framework by stating that an eLearning community
is comprised of instruction, social interaction, and technology. These frameworks
of online learning indicate that interaction is important in a community. As
summarized by Ertmer et al. (2004), Lipman (1991) identified five specific ways
that interaction in a community is important and promotes learning: 1) listening
to one another with respect; 2) trying to identify one anothers assumptions;
3) challenging one another to supply reasons for unsupported opinions;
4) building on one anothers ideas; and 5) assisting each other in drawing inferences from what was said. A study by Arbaugh (2000) confirms the relationship
20 / SHEN ET AL.
between interaction and the sense of learning in a community. The study
examined five Internet-based MBA courses and found that students perceptions
of learning were associated with the instructors emphasis on interaction, ease of
interaction, and classroom dynamics. Rourke, Anderson, Garrison, and Archer
(2001) argue that perceived interaction with others is one of the cornerstones
for the development of online learning communities. However, little empirical
research exists that examines the ways interaction influences the formation and
sustenance of sense of community in online learning environments. Furthermore,
previous research has employed traditional method, for example, using students
self-reported data and artifacts of communication. New methods such as social
network analysis may be helpful in articulating interaction in online learning and
the relationship between interaction and sense of community.
Social Network Analysis
SNA is a regularly used method within sociological and organizational studies
for exploring human and social dynamics. However, there is a growing interest in
using SNA in the field of education and online learning to determine information
sharing patterns, understand social capital formation in networks, and implement
program evaluation (e.g., de Laat, Lally, & Lipponen, 2004; Harrer, Zeini, &
Pinkwart, 2006; Nurmela, Palonen, Lehtinen, & Hakkarainen, 2003).
SNA is a method for representing and quantifying interaction which can
augment the two approaches traditionally used by researchers to study interaction in online learning: content analysis (Anderson, 2003) and learner feedback
(perception of students) (Rovai, 2002b). SNA is both a theoretical perspective and
a methodology involving quantifying and visualizing observations of relational
ties of members. There exist two distinct approaches to SNA, egocentric (ego
network analysis) and sociocentric (complete network analysis). The egocentric
approach focuses on individuals and is concerned with identifying and
generalizing the relationships of core members; the sociocentric network analysis
involves the quantification of relationships between people within a defined
group, focusing on measuring the structural patterns of interaction between
members, and consequently, how the patterns influence outcomes. In our study,
we analyze data from the sociocentric approach.
The measures of SNA in this article include:
Network Density represents the actual number of ties in a network as a ratio
of the total maximum ties that are possible with all the nodes of the network.
A fully dense network has a network density value of 1 (above one when data
contain edge weights, e.g., tie strengths), which indicates that all nodes are
connected to each other. Since our analysis includes all the ties (weighted ties)
between nodes the density values will often exceed 1. A network with a density
value near 0 indicates that it is a sparsely-knit network. For an undirected graph
with N nodes and M ties the density D is defined as D = 2M / N (1).
Centrality Degree is the number of ties to other actors in the network. It is
measured by indegree and outdegree. Indegree indicates the amount of people
that interact with a certain student. Outdegree is the amount of interaction a
student initiates with others.
Network Centralization is the degree of inequality or variance in the network
as a percentage of that of a perfect star network of the same size. Measure of
centralization is an expression of how tightly the graph is organized around its
most central point (Scott, 2000, p. 89).
Context of the Study and Participants
The courses examined in this study were about the design of educational
technologies and were taught to graduate students in a program for design and
development professionals. Both of the courses taught design skills using similar
methods and were fully online and delivered using the course management
system Sakai. Both courses also had the same instructor and each had a teaching
assistant that facilitated the course. There were 10 students in Course A who
chose to participate and 15 participants in Course B. Table 1 reports student
Table 1. Demographic Information of Participants
Course A
N (%)
Course B
N (%)
3 (30%)
7 (70%)
7 (46.7%)
8 (53.3%)
Over 36
1 (10%)
2 (20%)
7 (70%)
2 (13.3%)
3 (20.0%)
7 (46.7%)
3 (6.7%)
Academic status
9 (90%)
1 (10%)
8 (53.3%)
5 (33.3%)
1 (6.7%)
1 (6.7%)
Demographic variables
22 / SHEN ET AL.
demographic information. The participants in the two courses were adult learners,
with an average age of more than 26. Only one participant in Course A and two
in Course B reported their age as 21-25.
Part I: Survey Study
The students were given an online survey following each of three different
class activities (units of the course which lasted approximately two weeks). The
activities represent three different task structures: individual activity, group
activity, and peer review activity. In the individual activity, students were
required to complete a learning task individually, however, they could post to
discussion boards asking questions or offering help to others. In the group activity,
students were randomly assigned to teams of three members. They needed to
communicate, negotiate and accomplish a task for which they would be graded as
a team. In the peer activity, students worked as dyads to finish learning tasks.
The dyads were randomly assigned by the instructor. The assignment called for
dyad partners to review and offer feedback in a systematic fashion to their
partners, but each student was graded separately for their own work. Each of
the three activity types included social interaction via questions asked by the
instructor of the students for response on the class discussion boards. The
individual activity lasted two weeks, and the group and peer review activity
each took three weeks.
Students were asked to complete the survey and submit it electronically.
All students were given the option of participating in this research project,
and students who voluntarily completed the consent form became the sample of
this study.
The survey instrument measures sense of community with 6 items of the
original 10 items assessing connectedness from the Classroom Community Scale
(Rovai, 2002a). The Cronbachs coefficient alpha was .93, indicating a high level
of reliability. Students were asked to rate the level of agreement on a 7-point Likert
scale where 1 represented strongly disagree and 7 represented strongly agree.
The instrument was administered three times, immediately after each activity.
A two-way ANOVA was conducted using the individual courses and activity
types (individual activity, group activity, and peer review activity) as independent
variables and the final score of sense of community from the survey as the
dependent variable. The results of the ANOVA are shown in Table 2.
Table 2. Analysis of Variance for Sense of Community by
Courses and Activity Type
Sum of
Partial eta
< .05 47.382 2 23.691 .868 > .05
> .05
activity_type * course_id
Note: R squared = .099 (Adjusted R squared = .027)
The overall R square is .099 (Adjusted R squared = .027). The main effect
of course was statistically significant, F(1, 62) = 5.134, p < .05. Students in Course B had a significantly higher level of perceived sense of community than students in Course A. The main effect of activity type was not statistically significant. Part II: Social Network Analysis Part I analysis shows that students have a significantly different sense of community across the two classes and that this sense of community (in this context) does not vary between activities in the course even when they have different pedagogical structures. Describing the interaction patterns that characterize the two classes may reveal insights as to how sense of community can be understood and facilitated. Logs of all student activity in the system were automatically captured by the Context-aware Activity Notification System (Amelung, 2005).
Interaction patterns and frequencies of interaction in the two courses were
examined to articulate their relationship with sense of community. Since the two
courses had a similar structure (each composed of similar objectives and activities), and were supervised by the same instructor, visualizing the interactions in
activities in the two courses may help explain why students had different levels
of sense of community in the two courses.
SNA Graphs
NetDraw 2.0 was used to create interaction diagrams and visualize interaction
patterns for three activities in each course. The following three types of interactions were captured:
Content.read, which is reading a document posted in a shared folder in the
24 / SHEN ET AL.
Forum.read, which is reading posts from the instructor or other students in
discussion forums in the system;
Forum.reply, which is replying to a message posted by another individual.
The interactions between each individual were counted (see Table 3). The
light lines indicate the unidirectional interactions, and the heavy lines indicate
reciprocal interactions. Nodes with the same shape indicate partners for peer
review activity. The diamond shape represents course instructor and the teaching
assistants (TA). The Social network graphs attempt to represent the strength of
social ties between nodes.
SNA Measures
The courses density, centralization, and centrality degree measure for each
student were computed using UCINET 6.0, and the results are displayed in
Tables 3 through 8.
Network density indicates to what extent all nodes are connected to each other.
Table 4 uses weighted ties to show that the density of all three activities were
high when the instructors (instructor and TA) were included in the analysis; it
decreased when excluding instructors which indicates that the instructor has
a substantial impact on activity in the networks. Additionally Table 4 shows the
densities of the two social activities in course B were higher than in course A
when the impact of the instructor was removed from the network.
Table 5 shows the means of indegree and outdegree when including the
instructor and provides information about the activity level of members in their
network. Indegree indicates that people have read a message from a certain
person. Outdegree represents that a person has sent messages. To identify if
there were significant differences on indegree and outdegree between Course A
and Course B, two separate two-way ANOVA (Table 6) with course and activity
as independent variables and outdegree and indegree as dependent variables were
performed. The results indicate that participants in Course B had significantly
higher levels of outdegree than the participants in Course A, F(1, 79) = 33.27,
p < .01. Also, activity type had a significant effect on outdegree levels, F(2, 79) = 3.84, p < .05. Participants in peer activity had significantly higher levels of outbound interaction than in individual activity. In addition, there was a significant interaction effect of course and activity type. The outdegree in peer activity was the highest in Course B, while in Course A, it was the lowest. Similar results were found for indegree. There was a main effect of course, F(1, 79) = 6.03, p < .05, with participants in Course B having significantly higher levels of indegree than Course A. There was also a main effect of activity type, F(2, 79) = 3.30, p < .05. The indegree for the peer activity was significantly higher than individual activity. There was no interaction effect for course and activity type for indegree. Both Course A and Course B participants had similar patterns of indegree across activity types, for example, they both had the highest indegree SOCIAL NETWORK ANALYSIS / 25 in peer activity while the lowest indegree in the individual activity. And, when conducting ANOVA analysis for outdegree and indegree excluding the instructor, the results were similar for both of the two dependent variables. In addition, to further examine if the instructor influenced centrality degree, four sets of paired t-tests were performed to see if participants outdegree and indegree changed significantly with and without instructors in their network in Course A and Course B. Table 7 shows that the outdegree decreased significantly when instructors were excluded in both courses, t(35) = 5.99, p < .001, t(48) = 10.00, p < .001, respectively. For the measures of indegree, the difference was not significant between including instructor and excluding instructor in either course. Network centralization indicates to what extent a network is centered around or dominated by a few nodes (Scott, 2000). Table 8 shows that outdegree centralization in Course B was lower than Course A, meaning more people in Course B were active in communicating with others. Table 8 also shows the change of indegree centralization between with instructor and without instructor in course B was more dramatic than in Course A, which may indicate that students in Course B read the posts of the instructor more often than students in Course A. FINDINGS The ANOVA results on sense of community indicated that students doing similar activities in Course A and Course B had different levels of sense of community, and that differences on sense of community within a course across activity were not significant. Overall, both the visual graphs and measures of SNA demonstrated that students in Course B had more frequent interaction and more information exchange than students in Course A, which aligns with the statistical result that students in Course B perceived higher levels of sense of community than students in Course A. Visually, the SNA graphs of Course B for all activity types show more ties/lines/connections among members, with or without the instructor in the graph. The SNA graph shows that in Course B, all students are fully interconnected. However, in Course A there is one student that is not connected to other nodes in the network and is isolated from other students. The SNA measures convey a consistent story of interaction differences between the two courses. First, Course B had higher levels of network density than Course A. Also, the ANOVA results for centrality degree indicated that Course B had significantly higher levels of outdegree and indegree than Course A, showing that more interaction took place in Course B than in Course A (e.g., participants in Course B initiated and viewed more messages than those in Course A). In addition, the network centralization (Table 8) of the two courses indicates that the network of Course A is more focused around several essential nodes than Course B, implying that the overall interaction stretch of Course A is not as expanded as in Course B. 26 / SHEN ET AL. Table 3. SNA graphs for three activities (graphs for one activity were included). SOCIAL NETWORK ANALYSIS / 27 28 / SHEN ET AL. Table 4. The Density of the Interaction for Each Activity Course A Course B With instructor Without instructor With instructor Without instructor Individual activity 1.1479 0.4028 1.0842 0.1708 Group activity 1.7396 0.3889 1.5351 1.0500 Peer activity 0.9231 0.7179 2.6144 2.0875 Table 5. Descriptions of the Degree of Centrality Individual activity Group activity Peer activity Combined Outdegree Course A 15.50 (5.70) 23.42a (13.55) 11.50a (6.38) 16.81 (2.10) Course B 23.63b (8.99) 28.65 (14.98) 45.94b (18.03) 32.74b (1.80) Combined 20.14 (8.65) 26.48 (14.40) 31.18 (22.32) Indegree Course A 4.00c (2.09) 4.75 (5.05) 11.33c (5.13) 6.70d (4.96) Course B 2.56d (2.22) 30.35 (63.09) 35.31d (19.27) 22.74d (4.25) Combined 3.18c (2.24) 19.76 (49.49) 25.04c (19.07) Note: Means in the same row sharing the same letter superscript differ at p < .01. SOCIAL NETWORK ANALYSIS / 29 Table 6. ANOVA Result for Outdegree and Indegree by Courses and Activity Type Source Sum of squares df Mean square F Partial eta squared p 33.27 .30 < .01 Outdegree 5265.18 course 5265.18 1 activity_type 1216.29 2 608.14 3.84 .09 < .05 activity_type * course 3571.63 2 1785.82 11.29 .22 < .01 12501.49 79 158.25 Error Indegree course 5342.96 1 5342.96 6.03 .07 < .05 activity_type 5844.43 2 2922.21 3.30 .08 < .05 3169.95 2 1584.97 1.79 > .05
activity_type * course
Note: R squared = .46 (Adjusted R squared = .42) for Outdegree analysis; R squared =
.19 (Adjusted R squared = .13) for indegree analysis.
The SNA graphs and measures show some similarities about interaction
patterns across the two classes. First, the instructor played a pivotal role in both
classes. The instructor provided information to get the work started and initiated
interactions. The students tended to read what the instructor had posted and this
is demonstrated by comparing the graphs of with and without instructor. Also,
paired t-tests indicated that the outdegree for the two courses decreased significantly when removing instructor ties from calculations. This indicates that
a fair amount of outbound interaction by participants was toward the instructors.
Second, interaction patterns are influenced by the task types. For instance,
students had substantially less reciprocal interactions in individual activity than
in peer review activity in both classes, in a common sense way this makes sense,
but it also suggests that research on interaction and community in online learning
needs to pay more attention to the type of tasks given to students, as task type
shapes activity. The role of task type and its impact on interaction in online
learning needs further research. In addition, students interactions during peer
review and group activities go beyond their assigned partners. They interacted
with other classmates both accessing resources from others and providing
resources to others. Third, the network centralization table shows that the
networks of the two courses were not perfect star networks, which means interactions among students and instructor are not equally distributed; some students
were more active than others; and this situation occurred in both classes.
30 / SHEN ET AL.
Table 7. Paired T-Test Result on Degree Measures with Instructor
and Without Instructor
Sig. (2-tailed)
Course A Outdegree
(n = 36)
< .001 .69 5.89 1.70 35 > .05
Course B Outdegree
(n = 49)
< .001 Course B Indegree (n = 49) 22.90 16.69 1.18 48 > .05
Course A Indegree
(n = 36)
Further, the SNA shows differences in interaction patterns between the two
classes. In Course A, students with high interaction scores generally are the ones
that initiated interactions with other class members rather than waiting to be
interacted with by others. While in Course B, students with high interaction scores
generally are the ones that were interacted with by other students rather than those
that initiated interactions. This may imply that students who had high interactions
in both classes played different roles. Hypothetically in Course B, students who
had high interactions might have been those that had influence on the flow of
information in the class, that is, other students interacted with them because the
quality of their work was superior to others or their postings/ideas were more
meaningful than others. In other words, other students purposively selected to
interact with them because of their role as a contributor to the social construction
of the learning products.
The findings of the study show that interaction is strongly associated with
students perceived sense of community in this online learning environment. The
use of both traditional statistical analysis and innovative SNA techniques revealed
that students in the course with higher interaction frequency perceived higher
levels of sense of community, which supports the thesis that interaction plays
a crucial role in forming students sense of community in the online course. This
result is consistent with previous research, but the SNA representations extend
Individual activity
Group activity
Peer activity
With instructor
Without instructor
Course A
Without instructor
Course B
With instructor
Table 8. Network Centralization
/ 31
32 / SHEN ET AL.
the previous findings to show how ways of interacting, not just levels of
interaction have value for understanding interaction and how it relates to sense
of community. The SNA vocabulary of ties, density, centrality and indegree and
outdegree provide richness in describing interaction. This richness fills a
void between purely qualitative analysis that has contextual grounding and
simply quantitative measures that may not represent important distinctions
within the findings. Our reflection on our results also leads us to speculate that
perhaps SNA techniques can be better attuned to educational networks. For
example, the sequence of activity or distinctions among roles could become
part of the mathematical formulation or visual representation so as to provide
additional and important information. In sum the findings suggest that by
providing visual graphs and quantitative representations of the patterns and
density of interaction, SNA helps articulate the way interaction unfolds in
classes and shows potential to be a valuable method to study interaction in online
learning environments.
The findings have implications for understanding how interaction influences
sense of community in online learning environments. First, with the SNA
technique, the role of the instructor in fostering interaction in online learning
environments can be parsed out. For instance the visualizations in graphs 2 and
4 in Table 3 and the statistics of Table 4 show how the density of the social
experience is substantially formed around the instructor and that the instructors
role has greater relative impact in Course A than in Course B. Second, while no
significant differences were found for sense of community between different
activities types, the SNA graphs and centrality degree measures indicate that
the frequency of interaction for peer review and group activity is higher than
individual activity. One explanation may be that the sample size was too small to
highlight differences, but another explanation may lie in further examination of
the types of connections between members, such as in the differences between
high interaction students in courses A and B. Further studies with larger sample
sizes and more course activities may provide explanations for why different
patterns across courses are significant but not within courses. Third, SNA graphs
show that some students were left out of the communication network, which
influenced the information flow. These three implications suggest that more
explicit monitoring of how students attend to instructor communication may lead
to better online learning processes; that collaborative activities provide students
more opportunities and obligations to communicate and work together and thus
learn from one another; and that instructors need help for early identification of
students who are lagging in their participation.
SNA visualizations may be helpful for educators to the extent they can become
part of the interface for online learning to provide a dynamic overview of class
interaction such as are some students dominating and others lurking. These
visualizations may provide instructors with quick feedback on instructional
efforts to further engage students in the class, to get more even distribution of
student initiative and breadth of who talks to whom, as well as providing feedback
on efforts to promote different forms of information flow and interaction in the
class. In addition to visual representations which may help the instructor see
what is going on, the SNA metrics may be useful for providing indicators relative
to standards for class interaction that an instructor wants to maintain. For example,
an instructor might target a density of 2.0 for a particular unit. Of course these
forms of metrics for pedagogy seem foreign to us now, but as we implement
tools with more precision for understanding social interaction we may find new
ways of assessing instructional practices.
The results of this study should prove interesting to researchers of online
learning both on a theoretical and methodological basis. The construct of sense of
community has been shown by other researchers to be a replicable and important
element in online education, and interaction is proving to be a key influence in
promoting sense of community. The current study supports and extends previous
research results by examining a new learning context and using new measures.
Further, the results of our study demonstrate and provide support for social
network analysis as a methodology for understanding interaction and its contribution to sense of community. As a powerful analytic and visual tool, SNA helps
reveal the structural characteristics of the group, identifying the connections and
possible disconnects among participants in online courses. In addition, rather
than being limited to one form of representation for activity, the data from the
present study represents multiple ways to assess the digital experience of learners.
This type of examination is made possible by new tools such as SNA for doing
the analysis, but also tools for monitoring behavior in online environments such
as the Context-aware Activity Notification System (Amelung, 2005) that traced
and recorded all interactions in the online environment.
An interesting aspect of our findings is that we characterize the two courses
as similar because they have the same instructor applying a common structure
and instructional approach to similar course topics (learning to design) for similar
students (graduate students in the same program). However the students interaction patterns were substantially different. What could cause this difference?
One possible cause may be that early on in the course different events occurred
that started the students behaving differently. For example, perhaps a discussion
in one course was particularly engaging and viewed by students as empowering,
whereas in the other class a discussion may have led to acrimony among students.
Another factor that may have contributed to the different interaction patterns could
be individual differences. While the students were all graduate students in the
same program, students in one course may have had higher motivation or been
more socially dependent than those in the other course. While SNA does not show
us why students in one course behaved differently than the other, the results of
indegree and outdegree analysis show that the instructor was more effective in
Course B in getting nearly all students to interact with one another at a relatively
high level. Although a number of students in Course A similarly interacted at a
34 / SHEN ET AL.
high level, the density and type of interactions differed between the courses. Did
the instructor fail to address some problems in Course A or was he particularly
brilliant in Course B? Unfortunately SNA analysis cannot answer these questions.
This complexity of the findings suggests that future studies may want to include
content analysis of interactions and some measures of individual differences
with SNA to provide more comprehensive understanding of the social nature of
the online learning experience.
Finally, although the quantitative analysis and SNA together provide an innovative way to examine interaction and sense of community in online learning
environment, there are limitations. The data used in the study captured online
interaction in the course management system; however, students may communicate using other tools outside of the course management system, such as e-mail.
Therefore, the data may not be comprehensive in representing all the interaction
of students. Future studies may benefit from efforts to capture information about
communication that happens outside of the course tools and by combining SNA
approaches with traditional approaches of surveys and content analysis.
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Direct reprint requests to:
Dr. Demei Shen
School of Information Science and Learning Technologies
University of Missouri-Columbia
303 Townsend Hall
Columbia, MO 65201
e-mail: dmsry6@mizzou.edu
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In today’s dynamic business environment, educational institutions and companies must adapt to the ever-changing demands of consumers to stay competitive. Emerging teaching and learning tools are crucial to delivering interactive and collaborative courses to enhance the learning experience. This discussion focuses on evaluating virtual delivery systems to determine the optimal platform to create a virtual learning community that is best suited to meet the specific needs of your workplace.


The discussion analyzes the concepts of collaboration and sense of community in eLearning environments using relevant research studies. The focus is on understanding how a strong sense of community can be established and maintained in a virtual environment. The paper “A Framework for Collaborative Networked Learning in Higher Education: Design & Analysis” discusses a framework for planning, designing, and implementing collaborative learning systems. It emphasizes the importance of unifying courses and programs of study and utilizing appropriate learning management systems and infrastructure design. Social network analysis techniques are also discussed.

Participants in the discussion will research and choose one or a combination of delivery systems such as MOOCs, mobile and micro-learning, learning management systems/online learning spaces, social media, and video conferencing. The selected system(s) will be evaluated based on access to learners, the ability to connect and share a sense of community, and how it maximizes learning outcomes. The choices will be defended using personal experience, learning theory, and research studies.

1. To analyze the concepts of sense of community and collaboration in eLearning environments.
2. To evaluate various virtual delivery systems and choose one or a combination of systems for creating a virtual-learning community in the workplace.
3. To understand how to establish and maintain a strong sense of community in an eLearning environment.
4. To contrast sense of community with collaboration.
5. To defend the choice of virtual delivery system(s) based on personal experience, a learning theory, and at least one research study.

Learning Outcomes:
1. Summarize the major steps required to plan, design, and implement collaborative learning systems.
2. Evaluate and analyze virtual delivery systems based on their ability to maximize access for the audience and enhance their ability to connect and share a sense of community.
3. Compare and contrast the concepts of sense of community and collaboration in the context of eLearning environments.
4. Apply a learning theory and at least one research study to defend the choice of virtual delivery system(s) for creating a virtual-learning community in the workplace.
5. Develop strategies for establishing and maintaining a strong sense of community in an eLearning environment.

Solution 1: Social Media as a Virtual Delivery System for a Sense of Community in eLearning Environments

One suitable virtual delivery system that can be used to create a virtual learning community in workplaces is social media. Social media platforms such as Facebook and Twitter provide an online space for collaborative learning and sharing among students and teachers. Social media offers a broad range of multimedia tools, including chat, discussion boards, video conferencing, and blogs, that support communication, interaction, and collaboration among learners.

A strong sense of community can be established and maintained in an eLearning environment with social media by actively engaging learners in group discussions, collaboration, and interactions. Learners can share their experiences and knowledge within their learning networks and engage in real-time conversations that generate multiple perspectives and insights into the subject matter.

Sense of community and collaboration are interrelated concepts necessary for effective eLearning in virtual communities. Sense of community refers to the feeling of belongingness and emotional connection amongst learners and teachers in the learning community. On the other hand, collaboration involves the process of working together towards a common goal or task.

Social media as a virtual delivery system for eLearning enhances access and engagement among learners. Social media platforms can provide multiple channels for learners to access and share learning materials, knowledge, and resources. Through social media, learners can access relevant content, engage in real-time conversations, and interact with other learners, irrespective of their location.

Research has shown that using social media in eLearning environments enhances learner’s collaboration, engagement, interaction, sense of community, and ultimately, their academic performance. Thus, social media offers the best virtual delivery model for creating a virtual learning community in the workplace.

Solution 2: Learning Management System/Online Learning Spaces as a Virtual Delivery System for a Sense of Community in eLearning Environments

Another suitable virtual delivery system that can be used to create a virtual learning community in workplaces is a learning management system/online learning space. Learning management systems are web-based applications that provide learners with access to course content, communication tools, and resources to support collaborative learning.

A strong sense of community can be established and maintained in an eLearning environment with LMS/online learning spaces through active interaction, engagement, and collaboration among learners. LMSs provide various tools such as discussion forums, chat sessions, blogs, wikis, and other collaborative tools that support communication, interaction, and collaboration among learners.

Contrasting sense of community with collaboration, sense of community involves learners having a common bond or shared identity, whereas collaboration refers to learners working together towards a common goal.

The use of LMSs as a virtual delivery system maximizes access and enhances the ability of the audience to connect and share a sense of community. Learners can access learning materials, resources, and communicate regardless of their location. Additionally, LMS allows learners to access and share knowledge in real-time, which enhances interaction, collaboration, and critical thinking.

Research has shown that the use of LMSs in eLearning environments enhances learner’s engagement, interaction, sense of community, and academic performance. Hence, LMS is a suitable virtual delivery model for creating a virtual learning community in the workplace.

Suggested Resources/Books:

1. “Designs for Learning Environments of the Future: International Perspectives from the Learning Sciences” by Yannis Dimitriadis and Nancy Law.
2. “Teaching Crowds: Learning and Social Media” by Jon Dron and Terry Anderson.
3. “The Virtual Community: Homesteading on the Electronic Frontier” by Howard Rheingold.

Similar asked questions:

1. What are the best virtual delivery systems for creating a learning community?
2. How can an eLearning environment establish and maintain a sense of community?
3. What is the difference between sense of community and collaboration in virtual learning environments?
4. How can social network analysis be used to understand sense of community in online learning environments?
5. How can technology be used to enhance collaborative learning in higher education institutions?

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