systems
Article
A Knowledge Comparison Environment for
Supporting Meaningful Learning of E-Book Users †
Jingyun Wang 1, *, Hiroaki Ogata 2 and Atsushi Shimada 2
1
2
*
†
Research Institute for Information Technology, Kyushu University, Fukuoka 812-8581, Japan
Faculty of Arts and Science, Kyushu University, Fukuoka 819-0395, Japan;
[email protected] (H.O.); [email protected] (A.S.)
Correspondence: [email protected] or [email protected]; Tel.: +81-092-642-2297
This paper is an extended version of our paper published in the 23rd International Conference on
Computers in Education (ICCE 2015), Hangzhou, China, 30 November–4 December 2015
Academic Editor: Jon Mason
Received: 31 January 2016; Accepted: 28 April 2016; Published: 16 May 2016
Abstract: In this paper, we present an ontology-based visualization support system which can provide
a meaningful learning environment to help e-book learners to effectively construct their knowledge
frameworks. In this personalized visualization support system, learners are encouraged to actively
locate new knowledge in their own knowledge framework and check the logical consistency of
their ideas for clearing up misunderstandings; on the other hand, instructors will be able to decide
the group distribution for collaborative learning activities based on the knowledge structure of
learners. For facilitating those visualization supports, a method to semi-automatically construct
a course-centered ontology to describe the required information in a map structure is presented.
To automatically manipulate this course-centered ontology to provide visualization learning supports,
a prototype system is designed and developed.
Keywords: meaningful learning; critical thinking; knowledge structure; comparison skill;
course-centered ontology
1. Introduction
Nowadays, e-book systems are widely used together with Learning Management Systems in
the education field. For example, in the Arts and Science Department of Kyushu University Faculty,
not only Moodle but also the BookLooper e-book system developed by Kyocera Communication
Systems is used for supporting daily classroom teaching. Those two systems provide a platform
for instructors to easily organize and manage teaching materials; on the other hand, learners can
conveniently browse the learning resources. Especially, in the BookLooper system learners can even
mark text or add comments on any PDF files and the system can record their learning activities
(including which pages they read and how they switch between pages) and report to instructors [1].
However, as other existing systems, neither BookLooper nor Moodle provide any function to support
learners in constructing their knowledge framework effectively. When a learner acquires different
knowledge items, she/he may also compare those items and understand some relations between them
at the same time; those acquired knowledge items and their relations form the knowledge framework
of the learner. Knowledge framework development usually is not supported in the older systems, and
also, in those systems it is difficult to identify the relevant knowledge items that a learner possesses
before and after a learning activity.
Ausubel’s learning psychology theories [2–4] define the effective assimilation of new knowledge
into an existing knowledge framework as the achievement of “meaningful learning”. Those theories
suggest that knowledge finally gets incorporated into the human brain when organized in hierarchical
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frameworks and that learning approaches that facilitate this kind of organization significantly enhance
the learning capability of all learners. In contrast, in the case of rote learning, knowledge tends to
be quickly forgotten unless rehearsed repeatedly. Moreover, retained knowledge cannot contribute
to enhance the learner's knowledge framework and has a low possibility of being used in future
problem-solving [5]. However, how to move beyond rote learning and help learners to effectively
construct their framework is still an open issue.
In our previous research [6,7], the experimental results suggest that with the support of a system,
which provides a visual environment to encourage the comparison of related “knowledge points” so
as to foster meaningful learning, participants achieved significantly better learning achievement than
those without the system support. In this research, a Knowledge Point (KP) is defined as “a minimum
learning item which can independently describe the information of one certain piece of knowledge in
a specific course”; a learner can understand a KP by its own expression or can acquire it by practice.
As mentioned before, in our previous experiment we encouraged the students to use comparison
in their learning procedure. However, what do we mean by comparison exactly? “Comparison” is
an essential skill required by critical thinking which involves logical thinking and reasoning. This skill
refers to estimating, measuring, or noting the similarity or dissimilarity between two or more objects.
The human thinking skills “compare” and “contrast” appear in both “comprehension” and “analysis”
levels within the cognitive domain (in total six identified levels: “knowledge”, “comprehension”,
“application”, “analysis”, “synthesis”, and “evaluation”) according to Bloom’s taxonomy of intellectual
behavior in learning, which contained three overlapping domains: the cognitive, psychomotor and
affective domains [8]. From the educator’s perspective, knowledge comparisons could significantly
support learner comprehension of the new KP [9–11]. However, the existing e-learning systems, which
organize the knowledge of a curriculum in a tree structure based on the textbook chapters or the class
schedule, usually do not support the effective construction of knowledge frameworks by encouraging
knowledge comparison because they cannot characterize essential relations between KPs. For example,
in a Moodle system there are two KPs, “d” and “t”, which are located in Lesson 1 and Lesson 10,
respectively. In Lesson 10, to compare the KP “t” with the prior KP “d”, the instructor has to indicate
the location “d” (this can be done with a hyperlink) and explain the relation between “d” and “t”.
Even so, it is still difficult for the learners to locate the learning materials which directly address the
relation between these two KPs, unless they look through all the learning material in Lessons 1 and 10.
The searching will be even more time-consuming if the learner is comparing three or more KPs in
a course at one time.
To resolve these difficulties, an ontology-based Visualization Support System for e-book users
(VSSE), which uses a hierarchical map structure to manage the KPs of a curriculum, was developed in
this research to encourage the cultivation of comparison skills and foster meaningful learning.
2. Visualization Supports for both Learners and Instructors
2.1. A Visualization Support for Learners Using E-Books
For the learners in an e-book system, normally in one activity, they read several pages of a file.
As shown in Figure 1, after one preview activity (for example, studying pages 10–13, which cover
seven new knowledge points) in BookLooper, the learner can log in to VSSE, the Visualization Support
System for E-book users, to check the new knowledge points just studied. Our system will try to
encourage the learner to understand the relations between the new KPs visually. Furthermore, the
system also will make use of the quiz results of learners to identify the learner’s acquired KPs and
then encourage the learner to compare the new KPs with related acquired ones visually. Finally, the
system is also expected to recommend one KP or some KPs based on individual knowledge structure
and guide learners to personalized learning paths. With this kind of visualization support, learners
using BookLooper are expected to build up their knowledge framework more effectively.
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Figure 1. Visualization support for learners using e-books.
Figure 1. Visualization support for learners using e-books.
Figure
1. Visualization
support
for learners using e-books.
2.2. A Visualization Support
for Instructors
of E-Book
Users
2.2. A Visualization
Support
for Instructors
of E-Book
Users
instructor Support
of
e-book
we also
intend Users
to provide several visualization supports. Firstly,
2.2. AFor
Visualization
forusers,
Instructors
of E-Book
the instructor
can
visually
check
all
the
learner’s
knowledge
(including preview
statuses
For instructor of e-book users, we also intend to providestructures
several visualization
supports.
Firstly,
For instructor
of e-book
users,
we also
intendthe
to provide
several
visualization
supports.
Firstly,
and
quiz
results)
through
our
systems.
Secondly,
system
will
identify
the
most
difficult
KPs
or
the instructor can visually check all the learner’s knowledge structures (including preview statuses
and
the
instructor
can
visually
check
all
the
learner’s
knowledge
structures
(including
preview
statuses
relations
based
on the
frequently
keywords
and the
willmost
highlight
torelations
attract
quiz results)
through
our most
systems.
Secondly,marked
the system
will identify
difficultthem
KPs or
and quiz results)
through our systems. Secondly, the system will identify the most difficult KPs or
instructors’
attention.
based on the most frequently marked keywords and will highlight them to attract instructors’ attention.
relations
based on the the
most frequently
marked keywords
and will ofhighlight
them
to attract
Most
Most importantly,
importantly, the system
system will
will make
make use
use of
of the
the quiz
quiz results
results of learners
learners to
to identify
identify the
the
instructors’
attention.
learners’
learners’acquired
acquiredKPs
KPsor
orrelations
relationsand
andthen
thensupport
supportthe
theinstructor
instructorin
indeciding
decidinggroup
groupdistribution
distributionfor
for
Most importantly,activities
the system will
make useknowledge
of the quiz results ofFor
learners to as
identify the
collaborative
collaborativelearning
learning activitiesbased
basedon
onlearners’
learners’ knowledgestructures.
structures. Forexample,
example, asshown
shownin
in
learners’
acquired
KPs
or relations
and
then
support
theto
instructor
incollaborative
deciding group
distribution
for
Figure
2,
assume
an
instructor
of
a
given
course
plans
organize
learning
groups
to
Figure 2, assume an instructor of a given course plans to organize collaborative learning groups to
collaborative
learning
activities
based
on learners’
knowledge
structures.
For example,
as shownthe
in
study
newKP
KP
which
eight
related
KPs already
in theTocourse.
To encourage
study aa new
which
hashas
eight
related
KPs already
taught taught
in the course.
encourage
the cooperation
Figure 2, assume
an instructor
a given
course the
plans
to organize
collaborative
learning
groups
to
cooperation
between
learners
inofthe
same
group,
system
will identify
Learners
B and
C,
who
between learners
in the
same group,
the
system
will identify
Learners
A, B and
C, whoA,acquired
several
study
a
new
KP
which
has
eight
related
KPs
already
taught
in
the
course.
To
encourage
the
acquired
several
KPs (overlap
is allowed)
related
to respectively,
the target KP,and
respectively,
andthat
recommend
that
KPs (overlap
is allowed)
related
to the target
KP,
recommend
the instructor
cooperation
between
learners
in the same
group,
the
system will
identifyin
Learners
A, Bsimply
and C,know
who
the
instructor
put
those
three
learners
in
one
group.
Otherwise,
if
learners
one
group
put those three learners in one group. Otherwise, if learners in one group simply know the same
acquired
several KPs,
KPs (overlap
is allowed)
related
to the
target
KP,
and recommend
that
the
sameKPs,
related
it is difficult
fortothem
to realize
theof
rest
therespectively,
related
knowledge
reach
related
it is difficult
for them
realize
the rest
theofrelated
knowledge
and and
reach
the the
full
the
instructor
put
those
three
learners
in
one
group.
Otherwise,
if
learners
in
one
group
simply
know
full
understanding
of the
target
effectively
during
learning
process.
Based
on this
principle,
understanding
of the
target
KP KP
effectively
during
the the
learning
process.
Based
on this
principle,
the
the same
related
KPs,
it is difficult
for themsupport
to realizeinstructors
the rest of the
related knowledge
and reach for
the
the
system
willdesigned
be designed
to visually
in group
deciding
group distribution
system
will be
to visually
support instructors
in deciding
distribution
for collaborative
full understanding
of activities.
the target KP effectively during the learning process. Based on this principle,
collaborative
learning
learning activities.
the system will be designed to visually support instructors in deciding group distribution for
collaborative learning activities.
Figure 2. Visualization support for instructors of e-book users.
Figure 2. Visualization support for instructors of e-book users.
3
Figure 2. Visualization support for instructors of e-book users.
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3. A Course-Centered
Ontology for the Visualization Learning Support System
3. A Course-Centered
Ontology
for the Visualization
3.1. Semi-Automatically
Built
Course-Centered
Ontology Learning Support System
To
facilitate
the visualization
supports mentioned
3.1.
Semi-Automatically
Built Course-Centered
Ontology in the previous section, the description of the
information about all the KPs and the relations between KPs is required for the system. In this paper,
To facilitate the visualization supports mentioned in the previous section, the description of the
we present
a method
develop
a course-centered
toIn
describe
all the
information
aboutto
allsemi-automatically
the KPs and the relations
between
KPs is required forontology
the system.
this paper,
required
fromtothe
knowledge in courses.
we information
present a method
semi-automatically
develop a course-centered ontology to describe all the
Firstly,
weinformation
automatically
the information
from the “Syllabus system” of Kyushu University
required
fromexact
the knowledge
in courses.
and createFirstly,
the basic
of the
course-centered
ontology.
shown insystem”
Figure 3,
example,
weframework
automatically
exact
the information
from theAs“Syllabus
of for
Kyushu
and createin
thethe
basic
framework
of theDepartment,
course-centered
ontology.
As shown
Figure 3, in
for theUniversity
Kikan Education
Arts
and Science
there
are 3730
coursesinregistered
for example,
for the
Education
in there
the Arts
Science
Department,
are 3730 courses
the syllabus
system.
ForKikan
one course
title,
areand
several
courses
taughtthere
by different
professors
registered
in
the
syllabus
system.
For
one
course
title,
there
are
several
courses
taught
by
different
or instructors; those courses may share some of the same keywords or have completely different
professors or instructors; those courses may share some of the same keywords or have completely
keywords. One keyword also can be shared by courses which have different course titles. For example,
different keywords. One keyword also can be shared by courses which have different course titles.
“Differential equation” is taught in both “KIKAN Physics I A” and “Calculus and exercises” courses.
For example, “Differential equation” is taught in both “KIKAN Physics I A” and “Calculus and
Another
typical example
“Entropy”,
which
is not only
taught in Thermodynamics
exercises”
courses. is
Another
typical
example
is “Entropy”,
which is not onlycourses
taughtbut
in also
in Information
theory
courses.
In
this
step,
the
information
of
“course
title/numbering
code”,
“course
Thermodynamics courses but also in Information theory courses. In this step, the information of
code”“course
and “keywords”
fromcode”,
the syllabus
automatically
exacted
used forare
theautomatically
construction of
title/numbering
“course are
code”
and “keywords”
fromand
the syllabus
exacted
and usedfor
forontology.
the construction of the basic framework for ontology.
the basic
framework
Figure
3. The
framework
of of
thethe
course-centered
basedononsyllabus
syllabus
information.
Figure
3. basic
The basic
framework
course-centered ontology
ontology based
information.
However, for most of the courses in the syllabus system, less than 10 keywords are described;
However, for most of the courses in the syllabus system, less than 10 keywords are described;
the basic ontology framework built on those syllabus keywords is sufficient to provide the
the basic ontology framework built on those syllabus keywords is sufficient to provide the
visualization support described in the previous section. Therefore, in the second step, we encourage
visualization
support described
in themodify
previous
in the second
step,
we[12]
encourage
professors/instructors
to manually
the section.
ontology Therefore,
using the ‘Protégé’
ontology
editor
and
professors/instructors
to
manually
modify
the
ontology
using
the
‘Protégé’
ontology
editor
[12] and
then upload the modified ontology with the description of its related PDF file IDs in BookLooper.
then upload
modified
ontology
with theand
description
related design
PDF file
IDs in described
BookLooper.
Forthe
building
a demo,
we applied
adjusted of
theits
ontology
method
by
For
building
applied and ontology
adjustedofthe
ontology
designscience
method
described
Wang
et al. [7] atodemo,
developwe
a course-centered
an existing
computer
course,
called by
ontology
of computer science
(COCS).
Weexisting
analyzedcomputer
the learning
materials
of this
Wang course-centered
et al. [7] to develop
a course-centered
ontology
of an
science
course,
called
computer
science
course,
extracted
about
100
KPs
and
about
20
kinds
of
relations,
and
defined
them
course-centered ontology of computer science (COCS). We analyzed the learning materials of this
in COCS.
Figure
4 illustrates
some
KPs100
andKPs
theirand
relations
will describe
way inthem
computer
science
course,
extracted
about
aboutin
20COCS.
kinds We
of relations,
andthe
defined
which COCS is designed and developed to show professors/instructors of other courses how to
in COCS. Figure 4 illustrates some KPs and their relations in COCS. We will describe the way in
build up their course-centered ontology. Without doubt, how to combine all the course-centered
which COCS is designed and developed to show professors/instructors of other courses how to build
ontology made by different professors/instructors into one ontology will be another issue we need to
up their
course-centered
discuss
in the future. ontology. Without doubt, how to combine all the course-centered ontology
made by different professors/instructors into one ontology will be another issue we need to discuss in
4
the future.
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Figure 4. The course-centered ontology of computer science (COCS).
Figure 4. The course-centered ontology of computer science (COCS).
In the last step, we made a tool to automatically identify the location (including the file ID and
In the
last step,
a tool
automatically
identify
the location
(including
the file ID
and
the page
number)
ofwe
themade
KPs in
the to
BookLooper
system
and put
those location
information
details
the
page
number)
of
the
KPs
in
the
BookLooper
system
and
put
those
location
information
details
into
into the ontology.
the ontology.
Based on these three steps, the course-centered ontology will be developed semi-automatically.
Basediton
steps,
course-centered
ontology
willtime-consuming.
be developed semi-automatically.
However,
is these
worththree
noting
thatthe
maintaining
this ontology
is still
However, it is worth noting that maintaining this ontology is still time-consuming.
3.2. A Visualization Learning Support System Providing a Knowledge Comparison Environment
3.2. A Visualization Learning Support System Providing a Knowledge Comparison Environment
To automatically manipulate the course-centered ontology which describes all the relations
To automatically manipulate the course-centered ontology which describes all the relations
between KPs, a system which is intended to provide visualization learning supports for the
between KPs, a system which is intended to provide visualization learning supports for the construction
construction of learner knowledge frameworks is developed. Figure 5 shows a learner’s view of the
of learner knowledge frameworks is developed. Figure 5 shows a learner’s view of the computer
computer science course in the VSSE.
science course in the VSSE.
In the left part of this view, all the concepts of COCS are shown by a tree structure. Users can
In the left part of this view, all the concepts of COCS are shown by a tree structure. Users can
open all the concepts level by level until reaching the KP they are seeking. Further, a search function
open all the concepts level by level until reaching the KP they are seeking. Further, a search function is
is also provided to the right, above the tree structure. The learner can set the time period (for
also provided to the right, above the tree structure. The learner can set the time period (for example,
example, from 3 January 2016 to 4 January 2016) and push the search button; then, the KPs which are
from 3 January 2016 to 4 January 2016) and push the search button; then, the KPs which are involved
involved in the pages that the learner reads during that time period will be highlighted. In addition,
in the pages that the learner reads during that time period will be highlighted. In addition, the learner
the learner can search items by keywords, and items which contain the keywords in the tree
can search items by keywords, and items which contain the keywords in the tree structure will also be
structure will also be highlighted to enable further checking.
highlighted to enable further checking.
When the user double-clicks one leaf which represents one KP, the right relation panel will
When the user double-clicks one leaf which represents one KP, the right relation panel will display
display this KP and all its related KPs lined by relations defined in COCS. For example, in Figure 5
this KP and all its related KPs lined by relations defined in COCS. For example, in Figure 5 the
the individual which represents the KP “shift_JIS” is chosen. Then users can get a visual
individual which represents the KP “shift_JIS” is chosen. Then users can get a visual representation of
representation of relevant information as shown in the relation panel.
relevant information as shown in the relation panel.
Also, when the user moves the mouse on every node shown in the relation panel, the essential
Also, when the user moves the mouse on every node shown in the relation panel, the essential
properties of that KP (represented by data properties of one individual in COCS) will be listed, while
properties of that KP (represented by data properties of one individual in COCS) will be listed, while
for every arc shown in the relations panel, the relation statement will be displayed (for example, the
for every arc shown in the relations panel, the relation statement will be displayed (for example, the
displayed relation axiom between “shift_JIS” and “JIS_X_0201” in Figure 5). Therefore, users can get
displayed relation axiom between “shift_JIS” and “JIS_X_0201” in Figure 5). Therefore, users can
the essential properties of every KP and all its related KPs from the relations panel conveniently. All
get the essential properties of every KP and all its related KPs from the relations panel conveniently.
this information is extracted automatically from the OWL (Web Ontology Language for Semantic
All this information is extracted automatically from the OWL (Web Ontology Language for Semantic
Web, designed by W3C) [15] file of COCS. Furthermore, if there are too many relations shown in the
Web, designed by W3C) [13] file of COCS. Furthermore, if there are too many relations shown in the
relation panel, the user can select the relations of interest with the Arc Types panel.
relation panel, the user can select the relations of interest with the Arc Types panel.
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Figure 5. The main interface of VSSE for learners.
Figure 5. The main interface of VSSE for learners.
This system’s function is expected to provide the visualization support for the construction of
This system’s
function
is expected
provide
the visualization
support
theinstructors
construction
learner
knowledge
frameworks.
The to
other
function,
which is intended
tofor
help
to of
learner
knowledge
frameworks.
The other
function,
intended
todistribution
help instructors
to understand
understand
the learners’
knowledge
structures
andwhich
easily is
decide
group
for collaborative
thelearning
learners’
knowledge
and easily decide group distribution for collaborative learning
activities,
is stillstructures
under development.
activities, is still under development.
4. Conclusions and Further Work
4. Conclusions and Further Work
In summary, we present a semi-automated method to construct a course-centered ontology,
and
based on we
thispresent
Course-centered
Ontology
we design
and develop
a system ontology,
to provide
In summary,
a semi-automated
method
to construct
a course-centered
and
visualization
support
for
BookLooper
e-book
users.
For
learners
of
BookLooper
users,
the
system
is
based on this Course-centered Ontology we design and develop a system to provide visualization
designed
the perspective
of meaningful
learning
to visually users,
supportthe
them
to effectively
support
for from
BookLooper
e-book users.
For learners
of BookLooper
system
is designed
construct
their knowledge
framework.
For instructors
of BookLooper
users,
when they construct
are planning
from
the perspective
of meaningful
learning
to visually
support them
to effectively
their
collaborative
learning
activities,
the
system
can
provide
visualization
support
to
help
them
decide
knowledge framework. For instructors of BookLooper users, when they are planning collaborative
the group
distribution
based oncan
knowledge
of learners.
learning
activities,
the system
providestructure
visualization
support to help them decide the group
After
the
development
of
these
two
mentioned
functions,
the visualization learning support
distribution based on knowledge structure of learners.
system based on the ontology technique will be evaluated from various perspectives. In our further
After the development of these two mentioned functions, the visualization learning support
work, to encourage active engagement, a timely guided discovery learning environment [13] called
system based on the ontology technique will be evaluated from various perspectives. In our further
“cache-cache comparison” [14] will be developed to encourage the learner to detect hidden relations
work, to encourage active engagement, a timely guided discovery learning environment [14] called
between relevant KPs or hidden acquired KPs from given relevant KPs and relations through
“cache-cache comparison” [15] will be developed to encourage the learner to detect hidden relations
reflecting on the attributes of acquired knowledge visually. If learners keep making mistakes during
between relevant KPs or hidden acquired KPs from given relevant KPs and relations through reflecting
the task the system will provide hints to help them rethink and get closer to the correct answer. The
onprocess
the attributes
of acquired
learners
keep
mistakes
during
the task
of seeking
hidden knowledge
relations orvisually.
conceptsIf is
intended
to making
encourage
learners
to locate
newthe
system
will
provide
hints
to
help
them
rethink
and
get
closer
to
the
correct
answer.
The
process
knowledge in their knowledge structure and restructure existing knowledge; meanwhile, the of
seeking
hidden
relations
or concepts and
is intended
to encourage
learners
to locate
new knowledge
iterative
procedure
of confirmation
modification
in their own
relation
map ensures
that they in
their
knowledge
structure
and
restructure
existing
knowledge;
meanwhile,
the
iterative
procedure
of
check the logical consistency of their ideas and clear up misunderstandings.
confirmation and modification in their own relation map ensures that they check the logical consistency
Acknowledgments: The research is supported by Kyushu University Interdisciplinary Programs in Education
of their ideas and clear up misunderstandings.
and Projects in Research Development, the Research and Development on Fundamental and Utilization
Technologies for Social
Big Data
(No. 178A03),
and the
Commissioned
Research Programs
of National
Institute ofand
Acknowledgments:
The research
is supported
by Kyushu
University
Interdisciplinary
in Education
Information
and
Communications
Technology,
Japan.
Projects in Research Development, the Research and Development on Fundamental and Utilization Technologies
for Social Big Data (No. 178A03), and the Commissioned Research of National Institute of Information and
Author Contributions:
Jingyun
Wang and Hiroaki Ogata conceived and designed the system function; Jingyun
Communications
Technology,
Japan.
Wang and Atsushi Shimada worked on the ontology design, and Atsushi Shimada provided the maintaining
Author Contributions: Jingyun Wang and Hiroaki Ogata conceived and designed the system function;
information for the ontology; Jingyun Wang performed the development of the ontology and the system and
Jingyun Wang and Atsushi Shimada worked on the ontology design, and Atsushi Shimada provided the
wrote the paper.
maintaining
information for the ontology; Jingyun Wang performed the development of the ontology and
the system and wrote the paper.
Conflicts of Interest: The authors declare no conflict of interest.
Conflicts of Interest: The authors declare no conflict of interest.
6
Systems 2016, 4, 21
7 of 7
Abbreviations
The following abbreviations are used in this manuscript:
KP
VSSE
Knowledge Point
visualization support system for E-book users
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© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).