Evolution and Development of Virtual Learning Communities based

on a Visual Analysis

Junhong Sha

, Kaiquan Chen

and Shijun Dong

Department of Education, Ocean University of China, NO.238 SongLing Road, QingDao, ShanDong Pro, China

Teaching Center of Fundamental Courses, Ocean University of China,

NO.238 SongLing Road, QingDao, ShanDong Pro, China

Keywords: Virtual Learning Communities, Visual Analysis, Knowledge Co-Construction, Learning Experience,

Identity Affiliation, e-Learning.

Abstract: With the development of technology, communities are not restricted to the conception of geographical

entities, virtual learning communities have gotten a lot of attention in the past decades. In this research, the

evolution and development of virtual learning communities are interpreted by means of Citespace, a

software which can extract keywords from articles indexed from specified database and draw visual graphs.

Through the visual operations including co-citation and co-occurrence cluster as well as timezone evolution

illustration, there are three important domains emerged which consists of knowledge sharing, learning

experience optimization and online environment constructed by technology and the paper aimed to

explaining high-frequency phrases and central articles to analyze virtual learning communities’

development process. It indicates that technology serves for education and the deep integration of

technology and education improves learning experience efficiently.

1 INTRODUCTION

The coming of information era brings extremely new

chance to reform traditional modes of instruction,

and with the presentation of new words like MOOC,

online virtual communities based on network

technology rise rapidly, especially virtual

communities of shopping-online which have

developed wearable technology and mature

community rules inside. However when it comes to

virtual learning communities in educational field,

there are several definitions emphasizing different

features. For example, in view of social relationship,

virtual learning community is like a huge network

that provides environment for users and acquire,

produce, analyze and construct their dialogues and

behaviors while it can also stress the research of how

human behave in communities from the point of

interaction. Besides, virtual learning community can

be described as a place where members share ideas,

experience and resources to reconstruct collective

knowledge and promote outcomes by means of all

kinds of platforms and applications.

In general, virtual learning community is a

technology-mediated online group which focuses on

affiliation and learning improving. Differ from

traditional class virtual learning community does not

adopt communication face to face but shares

knowledge in distance and through this way, it

weakens the role of teacher and encourages

members to participate in interaction among human,

online environment and resources. Therefore it’s

used to be treated as informal learning

supplementing traditional class but gradually

occupies the forefront education through the way of

integrated a part of blending learning recently. This

paper aims to analyze and illuminate the evolution

and development of virtual learning communities in

a visual and quantized way.

2 DATA PROCESSING

2.1 Visual Tool: Citespace

The visual software in the research is named

Citespace(Citation Space), programmed by an

Sha, J., Chen, K. and Dong, S.

Evolution and Development of Virtual Learning Communities based on a Visual Analysis.

DOI: 10.5220/0006694903690376

In Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), pages 369-376

ISBN: 978-989-758-291-2

369

academic team led by professor Chaomei Chen, and

the first vision is compiled in Java in 2004. The

origin idea of Citespace is to draw visual graphs and

assist to analyze the potential knowledge contained

in scientific analysis comprehensively while after

constantly amendment, its core function can achieve

the visualization of a special keyword based on

articles written in different stages through co-

citation analysis, co-occurrence analysis, timezone

evolution analysis and so on. The working principle

of Citespace is the basis of a certain field consists of

the co-citation primary data and the advanced

knowledge is reflected by articles which quote the

co-citation data in some degree, therefore the

evolution process of the specific field is composed

of research basis/past, development, hot topics and

latest state. It’s CiteSpaceV(5.0.R2 SE) Version to

process origin data combined with qualitative and

quantitative analysis to handle the text content in

this paper.

2.2 Data Source

Statistics in the research is derived from Web of

Science

Core Collection Database, “Science

Citation Index Expanded (SCI-EXPANDED)” and

“Social Sciences Citation Index (SSCI) search”

specifically. Data type is set as “article” only, the

time span is chosen from 2000 to 2016, and the

deadline is 2nd December, 2016. Search “virtual

learning community” as keyword in text box and

516 related articles are resulted shown in Figure 1 as

follow. In the figure, the number of papers varies in

different years and generally the tendency is upward

which means more and more researchers have paid

their attention to virtual learning communities since

century.

Figure 1: Number of papers varies in different years.

2.3 Parameter Setting

First of all, all papers need to be filtrated in case of

repetition, which shows no repetition and cut

516articles into sections in chronological order.

Then import all papers into CiteSpaceV and create a

new project of this research. Time span of the

interface is set from 2000 to 2016 and one year a

slice, keeping accordance with data source. Next

choose “Title”, “Abstract”, “Author

Keywords(DE)”, “Keywords Plus(ID)” in Term

Source TabControl, “Cited Reference” of Node

Types and adjust parameter “C”, “CC”, “CCV” as

(2,2,20), (3,3,20), (5,3,20) to demonstrate the best

visual graph. Finally run the software to draw the

visible graph of 516 related articles like Figure 2, in

which sharpness and size of words have significant

meanings explained in section 3.1.1.

Figure 2: Co-citation knowledge graph.

3 VISUALIZED ANALYSIS

In this section, an elaborate description is required

for diverse visual graphs, such as cluster graph,

timezone view and detailed tables. Three subsections

including co-citation analysis, co-occurrence

analysis and timezone evolution analysis are

introduced to support the evolution and development

of virtual learning communities in different stages

associated with technology.

3.1 Co-citation Analysis

Co-citation analysis confirms that co-citation

network appears if two articles or two authors are

quoted by one certain paper and there is always

something in common among quoted articles in

conception, theory and method, which is shown in

the relationship among nodes of the visual

knowledge graph. Besides, the basis of one field is

approximately equal to the aggregate of co-citation

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articles and the frequency of quoted articles reflect

the importance of the article in the co-citation

network.

3.1.1 Co-citation Knowledge Graph

In Figure 2, different sharpness and size of words

have varied meanings. The deeper the color is and

the larger the size is, the more significantly the

keywords influence in the co-citation knowledge

graph. It finds out that the three most important

articles focus on topics about learning exchange,

knowledge sharing as well as identity affiliation of

members in communities. Some of the points are not

totally same to virtual learning communities we talk

about in this paper but can be regarded as the origin

of how virtual learning communities work in early

time. Taken WENGER E’s paper as an example,

which is indeed a book concerning learning and

communication in community entities, WENGER E

expounds the process of learning and identity

affiliation and trusts negotiation makes participation

come true. He thinks it’s not absolutely same to

require members’ behaviors in community entities

but must formulate definite rules to regulate

conversation and behaviors suitably and legally. As

for learning, he’s favor on the opinion that the

ability of valuable knowledge sharing and

acquirement is much more important than know-all

individually alone and the learning exchange can

assist to predict members’ performance. WENGER

E’s study emphasizes the importance of meaningful

knowledge construction and identity affiliation,

which provides reference for subsequent research.

3.1.2 Co-citation Clusters

Cluster analysis gathers high homogeneity articles

and circles as well as lines in different colors means

different clusters in Figure 3. Two parameters

demonstrating the cluster result is valid are

Modularity Q=0.7616 and Silhouette=0.6. There are

10 cluster labels in the co-citation cluster graph and

the serial number starts from No.0, in which the

smaller the serial number means more articles. For

instance, 0# cluster is marked “computer based

training” and it possesses the largest number of

articles, 22. Check the clusters’ content and it finds

out that most articles in early time are based on

survey in community entities and social

communities where learning usually generates in

experience exchange process. With the coming of

information technological revolution, Internet

and computer technologies prosper and bring

motivation into communities’ development.

Integrated in communities, a large quantities of

online platforms appears and the prosperity of

constructivism theory which advocates learners

oriented sparing no efforts requires technologies to

offer better learning experience ulteriorly.

Combining with pedagogy theory and psychology

theory, more and more technologies are applied as

supplement of traditional class in virtual learning

communities.

Figure 3: Co-citation cluster graph.

After co-citation analysis, we can conclude that

elements of community entities such as knowledge

sharing and identity affiliation are similar to them of

virtual learning communities and studies on

community entities contribute to research about

virtual learning communities. With the development

of computer technologies, community entities are

“carried” on the Internet and expedite online

communities which break the restriction of time and

region and make experience exchange and

communication more conveniently. Later on, online

communities directed to learning arise such as

forums and discussion board in informal learning.

It's obvious that technology applied to education

improve the learning efficiency and convenience,

and technology construct online communities and

then transform communication as well as traditional

teaching networked in early times.

3.2 Co-occurrence Analysis

Co-occurrence analysis is a method quantifying

common information of selected articles from all

kinds of information carrier. It’s to extract keywords

in common characteristics to visualize and get

generalities by mean of Citespace in scientific

studies.

Evolution and Development of Virtual Learning Communities based on a Visual Analysis

371

3.2.1 Co-occurrence Keywords

Adjust parameter, select keywords frequency is

above 15 and separate co-occurrence keywords into

two parts: interaction relied on technologies and

knowledge sharing.

 Technologies realize interactive environment

Figure 4 shows the change of keyword “virtual

community ” and the trend is upward especially after

2012. The sharp up is related to the flourish of

MOOC (Massive Open Online Courses) and the

integration of cloud technology and instruction,

which drives the blending of mobile learning,

electronic learning and traditional teaching.

Interaction can be divided into two types: man-

machine interaction and person to person interaction,

and the former provides basic functions like data

recording, data mining and data analyzing to make

better interaction while the later derives company

agent to filtrate companies with same interests or

goals for learners and recommends the most suitable

companies as much as possible, taken the inner

world such as self-efficacy and personality

characteristics of learners into consideration.

In addition, learner-oriented requires technological

majorization for better learning experience. On the

one hand, Three-dimensional (3D) technology

brings virtual simulation into not only entertainment

but the construction of education which encourages

learners to interact and collaborate in communities.

On the other hand, a new form of game named

Serious Game with exquisite environment grows and

is admired by educational professors recently, which

is treated as attraction for learners to insist on

learning and working efficiently. For example,

Second Life uses virtual character that user builds to

experience different life in online virtual community.

Furthermore, the construction of simulation

environment is appreciate for educational

experiments such as chemical experiments in danger

and physical experiments under ideal conditions.

Figure 4: History of “virtual community ”.

 Conditions motivate knowledge sharing

Articles in co-occurrence graph focus on factors

influencing knowledge sharing and measures of

creating conditions to motivate knowledge sharing,

which explicate why and how to promote the

awareness of knowledge sharing and participative

behaviors. According to the consequence of co-

occurrence keywords selected by Citespace and

related references, factors are separated into internal

motivation and external influencing factors.

Internal motivation consists of emotional

commitment (including trust, self-efficacy, attitude,

etc.) and individual’s emotional response to

technology which means whether learner is adaptive

for technology or not. Furthermore, external

influencing factors cover the ease of access to

information, social relationship, reward mechanism,

open technical environment that guarantee

accessibility of platforms and effective community

rules. All above play positive role on knowledge

sharing and we can take measures involved

improving emotional commitment, enhancing self-

efficacy and satisfaction of learners for virtual

learning communities, widening channels of

informational access and social networking by

means of technologies, implementing democratic

community rules, and establishing effective reward

mechanism to meet the needs of learners and ensure

persistent participation furthest in virtual learning

communities.

3.2.2 Co-occurrence Clusters

Two parameters Modularity Q=0.6452 and

Silhouette=0.6727 indicate the high reliability of co-

occurrence clusters. There are 13 clusters classified

by size of article number and Table1 as follow

shows the ten largest clusters of the cluster graph

produced by LLR algorithm. From the Mean(Year),

most co-occurrence clusters appear after 2010 which

represents studies on virtual learning communities

make significant progress after 2010 and we analyze

the cluster content through clicking the node to

check the detailed citing articles and the analysis

result of these important articles attached to different

cluster labels.

Of all clusters, 0# cluster owns the largest

number of articles, and the label reveals it’s

associated to the distributed learning environment

where social media embeds online communities and

expands the range of interdisciplinary

communication. Other clusters pay attention to

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Table 1: Labels of clusters.

Cluster

Size

Silhouette

Mean

(Year)

Label(LLR)

0.63

2010

perspective; role;

distributed learning

environment;

0.808

2014

learning environment;

online learning; self

efficacy;

0.76

2011

simulator; technology;

wiki;

0.763

2011

knowledge sharing;

online community;

trust;

0.872

2014

drug discovery;

descriptor; virtual

screening;

0.795

2012

technology

acceptance;

communities of

practice; twitter;

0.756

2014

children; randomized

controlled trial;

nursing student;

0.78

2013

virtual environment;

script; community

pharmacy;

0.78

2013

sense; virtual world;

curriculum;

0.8

2012

impact; climate

change; student;

identity affiliation and the virtual simulation of

online platforms. For example, “self efficacy” of 1#

cluster, “trust” of 3# cluster and “technology

acceptance” of 5# cluster put their points on how to

enhance learners’ identity affiliation(including trust,

self-efficacy, attitude, etc.) and improve learning

experience by technology application. However,

words like “simulator” and ”virtual” in 2#, 4# and

7# cluster demonstrate the virtual simulation makes

learners immerse in learning. On the one hand, the

simulation lab guarantees real and safe experience

for learners which keeps dangerous situations like

hazardous chemicals explosion away. On the other

hand, ideal conditions such as zero friction and

vacuum state that can hardly happen in reality could

be constructed well in physics simulation

experiments and then makes learners feel

appreciable through operation in person. Moreover,

some clusters with small size concentrate on the role

of leader that greatly determines the development

direction of a certain virtual learning community

through resource management, experience

transmission and rule execution, which insures the

virtual communities efficiently and orderly.

On the whole, co-occurrence keywords consistent

with co-occurrence clusters identify the influence of

technological interaction and knowledge sharing,

and the former involves relationship among learners

and human-to-environment and how to create

conditions to improve interactive experience like 3D

serious games and virtual simulation while the latter

analyzes the factors including internal motivation

and external influencing factors affect sharing sense.

Little by little, with the degree of hybrid of

education and technologies deepened, simple

experience exchange and platform construction are

transited to high-level learning skills and deep

interaction.

3.3 Timezone Evolution Interpretation

The view of timezone evolution of virtual learning

communities in Figure 5 is made up of lines and

circles in different colors. The deeper the rose color

is, the more important the labels of nodes are and the

denser the lines are, the more important the labels of

nodes are. In Citespace, center analysis in which

centrality is regarded as important reference of

evaluating nodes’ position in knowledge map

network is to analyze the circles with rose color

outside the circle rim. According to Figure 5, nodes

with high-centrality include “virtual environment”,

“design”, “self efficacy”, “social presence”, “social

media”, “knowledge sharing” and so on. Three

keywords are clear as follow to interpret the

contextual research hotspots of virtual learning

communities in the view of timezone evolution.

Figure 5: View of timezone evolution.

3.3.1 Knowledge Co-construction Beyond

Knowledge Sharing

There’s no doubt that Knowledge sharing is the

internal impetus for the development of virtual

learning communities where learners of totally

Evolution and Development of Virtual Learning Communities based on a Visual Analysis

373

different backgrounds join, gather and communicate

for same or similar interests and purposes. It's

obvious that knowledge sharing is the important

basis for virtual learning communities whether the

experience exchange in early time or communication

and knowledge co-construction beyond knowledge

sharing afterwards, therefore the key to ensure

effective participation and shape the communities is

to offer what members need indeed in the

mechanism of knowledge sharing. To get the goal,

on the one hand, internal motivation and external

influencing factors are analyzed to create conditions

to inspire members especially learners with

abundant valuable learning resources to share on

their own initiative and improve their willing to

share knowledge even co-construct knowledge

beyond sharing which means learners need to create

new knowledge based on the integration with

original experience; on the other hand, the result of

knowledge co-construction must be positive and

bidirectional. Positive result is to build rules to

forbid plagiarize or other intellectual property

infringement, and the ideal positive knowledge

sharing is to promote meaningful knowledge co-

construction beyond knowledge sharing which needs

to consolidate existing knowledge and motivate

knowledge co-construction on the basis of

experience exchange and communication instead of

remember or download resources from others.

Bidirectional knowledge co-construction is supposed

to make knowledge and resources flow adequately to

generate dynamic communication network in which

learners of virtual learning communities are

available for requisite information and the

knowledge co-construction expands the knowledge

repository for communities to attract more and more

learners and strengthen communities in return.

3.3.2 Friendly Social Relationship

Friendly social relationship guarantees good

performance of learners and there are some people

elder and some new in virtual learning communities.

The elder usually hold the post of leaders who shape

community culture, guide the new to fit the

communities and resolve contradictions and the

responsibility drives leaders to make and execute

rules, collect and share resources, warm the

communication atmosphere, provide members with

identity affiliation and so on.

Differ from traditional class, the identity

affiliation of members in virtual learning

communities reflects from online behaviors such as

comments and discussions in forums rather than talk

face-to-face in classroom. Generally, the identity

affiliation represents members’ subjective feelings

like self-efficacy and trust. Members with diverse

prior experiences grow the sense of belonging

through communication with the elder after joining

the communities and reduce independent actions

gradually. To celebrate the process, the elder

introduce rules and members to new member and

help he/she feel the collective honor, good leader

convinces new members and the warm atmosphere

in virtual learning communities prompts members to

construct trust in shorter time. Trust among

members will intensify all identity affiliation,

enhance the willing of participating activities and

knowledge co-construction and then push the virtual

learning communities to a higher stage.

3.3.3 Technology Optimizes Learning

Experience

As an essential element of virtual learning

communities, technology provides members with

convenient learning platforms and channels to gather

and communicate synchronously or asynchronously,

and achieve members’ participation break the

limitation of time and space. In other words,

multiform virtual learning communities is based on

the technological construction and technology makes

the upload, download and sharing of resources as

well as evaluation of learning performances

measurable, in which process technology deepens

the friendship and social collaboration. Frankly

speaking, technology restricts the development of

virtual learning communities to some extent, for

instance, it shuts the door against people who can

not afford the network or use technologies, but

investment from the government finance has focused

on educational equality and closed the gap step by

step. Besides, how to optimize learning experience

and make communication convenient with the

easiest technological operations have become

research hotspots of virtual learning communities

recently and studies on artificial intelligence applied

in education are underway. With the development of

technology, deep integration of technology and

education will contribute on learner-oriented

learning experience and improving learning

outcomes, and some new multiform virtual learning

communities such as mobile learning communities

and 3D virtual learning communities rise to

consummate the research framework of virtual

learning communities in ubiquitous learning

environment.

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4 DISCUSSION

The purpose of this paper is to visualize the

evolution and development of virtual learning

communities by means of Citespace while selected

articles are limited in Web of Science

Core

Collection Database, “Science Citation Index

Expanded (SCI-EXPANDED)” and “Social

Sciences Citation Index (SSCI) search” specifically

and keywords Citespace extracted are from titles,

abstracts and keywords of articles instead of the

whole papers, which might makes the visual

consequence unclear in some degree. Although we

have read and checked the typical articles in detail,

it’s inevitable that some cases are ignored for that

516 related papers in this research can not be read

completely in person. In addition, taken account of

the limitation, the research analyzes the evolution

and development of virtual learning communities on

the macro level but not microscopically. Obviously,

there are still much work to do with the analysis of

the benefits of instructional technology applied for

better learning and optimized learning experience in

communities.

5 CONCLUSIONS

In conclusion, the paper combines pedagogy theory

and technology application to illustrate the evolution

and development of virtual learning communities

based on visual graphs drawn by Citespace. From

the co-citation analysis, technology constructs the

online platforms and provide channels like Q&A

communities and forums to make learning efficient

and convenient in early times. After carrying

community entities on the Internet, more and more

interactions are requested through technological

update and virtual environments are applied in

communities to optimize the learning experience as

well as learning interests. Quantities of studies focus

on learners’ subjective feelings and present that it’s

essential to create internal and external conditions to

motivate knowledge sharing and improve learning

performances. Afterwards, the view of timezone

evolution interprets the contextual research hotspots

of virtual learning communities from three angles

which are knowledge co-construction beyond

knowledge sharing, friendly social relationship and

optimized learning experience by technology.

However, the visual analysis also finds that

cooperation among researchers and institutions is

insufficient and the network in Figure 6 shows the

independent research with low contact and joint.

Figure 6: Graph of co-operating institutions.

It must be realized that professional research teams

are essential and cooperation researches promoting

the evolution and development of virtual learning

communities are feasible. Therefore, cooperation

astride countries and research institutions and

sharing the research achievements under the

background of technology integrated education will

devote to pushing virtual learning communities to

develop higher level and make technology serve

education authentically in further research.

ACKNOWLEDGEMENTS

I’d like to thank my supervisor, associate professor

Kaiqun Chen, for his encouragement and assistant

throughout the development of this paper. Besides, I

have to appreciate the humanities and social science

research project “New Research Model Based on E-

SCIENCE” (Project ID Number: 15BTQ057) for our

work is funded by this project which belongs to

ministry of education and the support of the

undergraduate teaching reform program of

Shandong Pro in 2016 which is named “Learner-

centered Model to Explore Collaborative Learning

Support”.

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