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Application of Learning Styles Adaptivity in Mobile Learning Environments
Application of Learning Styles Adaptivity in Mobile
Learning Environments
Oleh : Kinshuk, Taiyu Lin
ABSTRACT
Availability of advanced mobile technologies, such as high bandwidth
infrastructure, wireless technologies, and handheld devices, have started to extend e
learning towards mobile learning !mlearning" !Sharples, #$$$"% &his phenomenon
fits well with the new paradigm 'anytime, anywhere computing( !Lehner and
)*se+abel, #$$#"% owever, the development of mlearning is still at rather early
stage and many issues have yet to be resolved% One of these issues is the potential of
individuali-ation of learning process for the learners%
&his paper explores how to improve learning process by adapting course
content presentation to student learning styles in multiplatform environments such
as ./ and .0A% A framewor+ has been developed to comprehensively model
student1s learning styles and present the appropriate sub2ect matter, including thecontent, format, media type, and so on, to suit individual student% &he wor+ is based
on the 3elderSilverman Learning Style &heory% &he framewor+ uses traditional
webbased intelligent tutorial architecture, with two additional components:
4learning style analysis module1 and 4access device analysis module1% &he learning
style analysis module ta+es care of modeling student learning style and
communicates with student model, whereas the access device analysis module
identifies the access device profile and provides the information to tutorial module%
&he tutorial module creates the suitable content, based on the student model
!including individual learning styles" and access device profile, and presents to the
student%
5ased on the framewor+, a prototype for the domain of .. programmingcourse has been developed% 6ith this system, students are able to learning ..
programming with course content that matches their own learning style and the
device used to access the content% A formative evaluation is planned to assess the
student satisfaction, learning efficiency, and effectiveness of the system while
providing various presentations of the same content to different users on different
devices%
INTRO!CTION
Availability of advanced mobile
technologies, such as high bandwidth
infrastructure, wireless technologies, and
handheld devices, have started to extend e
learning towards mobile learning !m
learning" !Sharples, #$$$"% &his
phenomenon fits well with the new paradigm
'anytime, anywhere computing( !Lehner
and )*se+abel, #$$#"% owever, the
development of mlearning is still at rather
early stage and many issues have yet to beresolved% One of these issues is the potential
of individuali-ation of learning process for
the learners%
7n past decades, researchers from
different disciplines have intended to define
and classify learning styles that help teacher
1
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)ama 8urnal% 9olume $ nomor $ &ahun #$;, $#;
to improve their individuali-ed teaching%
&here are many learning style theories used
today and the learning style theories have
been applied in educational environment
widely% 3or example, the &heory into
.ractice 0atabase !&7., #$$
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Application of Learning Styles Adaptivity in Mobile Learning Environments
practical approach to establish the
dominant learning style of each
student%
o &he results of 7LS can be lin+ed
easily to adaptive environments
(.aredes and odrigue-, #$$#"%
o
7t is most appropriate and feasible to be implemented for hypermedia
courseware !/arver and oward,
???"%
&his theory categori-es an
individual1s preferred learning style by a
sliding scale of five dimensions: sensing-
intuitive, visual-verbal, inductive-deductive,
activereflective and sequential-global
!3elder and Silverman, ?BB 3elder, ??
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ExpertModule
StudentModel
Tutoring Module
Communication Mechanism
Server
Client
User Interface Module (PC or
InternetInternet
Learning styleanalysis
Access deiceanalysis
)ama 8urnal% 9olume $ nomor $ &ahun #$;, $#;
these styles, and there is respective course
material for each style% 6hen student first
time enter the system, the course content is
delivered to student randomly% &hen the
system monitors student1s learning process
and base on student1s evaluation to update
student1s learning styles !auditory, visual,tactile or a combination of them"% According
to student1s latest learning styles, the system
provides the suitable course content% &he
learning styles supported by the system are
not based on any educational learning style
theory, so its learning styles are more or less
li+e preference%
Adaptive /ourseware Environment
!A/E" !Specht and Oppermann, ??B"
provides certain mechanism to adapt to
student1s learning styles% 6hen a studentstarts to use a new courseware, the student
are as+ed for their learning strategies, such
as learning by example, reading texts, or
learning by doing% 5ased on the learning
model, the domain model and the
pedagogical model, the presentation
component selects appropriate learning units
and generates individual hypermedia
documents for student% 7n strict learning
style theory in education, its supporting
learning styles may be classified into student
preference%
&he 6ebbased system created by
.aredes and odrJgue- !#$$#" uses
3elderSilverman Learning Style &heory and
7ndex of Learning Styles to assess student1s
learning styles% &hen the assessment result is
used to automatically adapt 6ebbased
educational systems1 content seFuencing for
student% owever, the system only supports
two dimensions of four dimensions in the
3elderSilverman Learning Style &heory%
0ifferent systems have various ways
to collect student1s learning styles, such as
interview, Fuestionnaire, and monitor
student1s behaviour% owever, an important
point that has to be +ept in mind is to get a
useful student1s learning style actually is a
psychological test process that specially
designed, and not by a simple interview
!5rusilovs+y, #$$"%
All of these systems are ./ based systems%
&hey do not support the mobile
environments, which are emerging very fastand need to be further explored% &he aim of
the paper is to explore how to support
individual study in mobile environments%
T'E 'I"'%LE&EL ARC'ITECT!RE
O$ T'E S#STEM
&he highlevel architecture uses traditional
webbased intelligent tutorial architecture,
with two additional components: 4learning
style analysis module1 and 4access device
analysis module1% !figure"%
3igure : the high level architecture of the
prototype
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Application of Learning Styles Adaptivity in Mobile Learning Environments
&he learning style analysis module ta+es
care of modeling student learning style and
communicates with student model, whereas
the access device analysis module identifies
the access device profile and provides the
information to tutorial module% &he tutorial
module creates the suitable content, based onthe student model !including individual
learning styles" and access device profile,
and presents to the student%
TO+AR T'E *ROTOT#*E
&his section discusses how toimplement the prototype based on the above
architecture%
Adaptation in mobile learning
environments
&he adaptation in mobile learning
environments implicates that it has to
consider the individual learner and theaccess device% 7n the prototype, the access
devices are ./ or .0A%
Adaptation to student1 learning styles:
&he framewor+ is able to adapt to individual
student learning style by providing a default
learning style adaptation and individual
learning style adaptation%
&he default learning style adaptation:
the system populates student learning style
with a default learning style if student selects
not to ta+e the learning style assessment
Table : default !references for mild
!references of learning st"le dimensions
process% 5ased on validation of
3elderSoloman1s 7ndex of Leaning Styles
that more students are active, sensing,
seFuential, and visual than reflective,
intuitive, verbal and global!9anKwanenberg, et al%, #$$ Kywno, #$$
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)ama 8urnal% 9olume $ nomor $ &ahun #$;, $#;
tutorial module% 5ased on the student1s
learning style and hisGher access device, the
system will present the course content
accordingly%
T,e control flo-s in t,e system
3rom the figure , the control flows
in the initial accessing the system are: a"
after a student logs into the system
successfully, it provides two options !default
learning style and ta+e the learning style
assessment" for the student% b" 7f the student
selects the default style, the system
populates the student learning style in
student model with the default style!activeGsensingGvisualGseFuential"%
Otherwise, the system provides the 7ndex of
Learning Style Fuestionnaire for the student%
After the student finishes the Fuestions and
submits the answers, the system analyses the
student1s learning style and populates hisGher
student model%
3rom then, the tutoring module will
combine the student1s leaning style
information and the information of access
device provided by access device analysis
module, and serve the student with course
content according to hisGher learning style
and access device%
Classification of st.dent learning styles
and t,eir implementation r.les
5ased on 3elderSilverman Learning
Style &heory, classification of student
learning styles and their implementation
rules are defined%
5ecause the system signs
corresponding default preferences for those
mild preferences of leaning style dimensions
and treats the moderate and strong
preference as the same, there are ; types of
combination of leaning style dimensions%
&hey are:
• activeGsensingGvisualGseFuential
• activeGsensingGvisualGglobal
• activeGsensingGverbalGseFuential
• activeGsensingGverbalGglobal
• activeGintuitiveGvisualGseFuential
• activeGintuitiveGvisualGglobal
• activeGintuitiveGverbalGseFuential
• activeGintuitiveGverbalGglobal
• reflectiveGsensingGvisualGseFuential
• reflectiveGsensingGvisualGglobal
• reflectiveGsensingGverbalGseFuential
• reflectiveGsensingGverbalGglobal
• reflectiveGintuitiveGvisualGseFuential
• reflectiveGintuitiveGvisualGglobal
• reflectiveGintuitiveGverbalGseFuential
• reflectiveGintuitiveGverbalGglobal
&hese different types of learning
styles are respectively implemented by the
combination of the following B elements%
&he implementation rules for each element
are discussed as follows%
Active: study in groups to discussing,
guess possible Fuestion and answer
them, find ways to do something with
learning concepts :
• .roviding discussion area
• eminding student to guess several possible
Fuestions
eflective: thin+ about Fuietly before
going ahead stop periodically to review
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Application of Learning Styles Adaptivity in Mobile Learning Environments
what have been learning, writing
summaries :
• &hin+ before going ahead
• Stop periodically to review what
have been learning
• 6riting summaries
Sensing: facts, example following by
the exposition, handon wor+, practical
material :
• Example first and following by the
exposition
• andon wor+, such as practicing in
the applying environment
7ntuitive/ abstract, concept, theory,
exposition before example :
• Exposition first and following by the
example
• More concept and abstract
9isual: picture, graphs, diagram, flow
chart, schematics, demonstration,
concepts map, color notes, slides with
multimedia :
• More picture, graphs, diagram
• Animation demonstration
• /olor important concepts
9erbal: text, and audio
• text
• Audio
SeFuential: Step by step logically to
present material, outline the material in
order :
• Step by step to present material
• /onstrict lin+s
Dlobal: Large picture before detail, large
2ump, context of the sub2ect :
• Dive big picture of the course
• .rovide all the lin+s
; types of learning styles and their
corresponding implementation rules have
been finali-ed% 7n the next section, the design
and implementation of the prototype are
presented in details%
&he classification of student learning
styles and their implementation rules
reFuested for implementing the 3elder and
Silverman learning style theory have been
identified and discussed% 7n the following
section, the prototype that implements the
3elder and Silverman learning style theory is
discussed%
Implementation of t,e prototype
&he prototype uses the ..
programming tutorials material as course
content% Students1 profiles including the
learning style are stored in a MySL
database% &he prototype is developed by
using ML technologies, .., Apache web
server%
&o dynamically identify the access
device, a simple http header string
identification techniFue is used% Since the
.0A is limited at the si-e of screen and the
resource, it is not capable of displaying
multimedia as rich as ./% &herefore, two
sets of course content are developed for ./
based and .0A based%
&he figure # is the page that student
can select the default learning style or ta+ingthe leaning style assessment from ./s% 7f the
student selects the course area directly, the
system will populate the student model with
the default learning style and bring the
student to the course content according to
the learning style%
7
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)ama 8urnal% 9olume $ nomor $ &ahun #$;, $#;
Figure : a screens#ot t#at student can select
to go straig#t to course or ta$e t#e
assessment
&he figure < is a lesson for the
learning style
activeGsensingGvisualGseFuential% 7t provides
the example at the begging of the lesson !if the lesson has example"%
Student can enter the group discussion area
anytime through the menu H discussion area%
&he lesson is presented step by step with few
lin+s that student can 2ump% According to the
visual, the lesson is presented with picture
and highlighting the important concepts%
Figure %: a screens#ot of a lesson for leaning
st"le active&sensing&visual&sequential
CONCL!SION
7n this paper, we presented a
mechanism to fully model student1s learning
styles and fully adapt the course content to
individual learning style, based on the
3elderSilverman learning style theory, and
the access device% A prototype for ..
programming course was developed to
demonstrate the mechanism% Students can
perform study with course content that
nicely match their own learning style and
their access devices !./ or .0A"% 5yadapting the course content to the student1s
individual learning style and access device,
we expect students are able to learning more
efficiently and more effectively%
)ext step, we are going to conduct an
evaluation on the prototype to assess the
learning efficiency and effectiveness of this
system%
RE$ERENCES
5riggs, >% /% and Myers, 7% 5% !?@@"%
'"ers-riggs T"!e ndicator , .alo
Alto, /A: /onsulting .sychologist
.ress, 7nc%
5rusilovs+y, .% !#$$"% *da!tive#"!ermedia. Nser Modeling and Nser
Adapted 7nteraction, &en Iear
Anniversary 7ssue !Alfred >obsa,
ed%" !G#", B@$
/arver, /% A%, oward, % A% and Lane, 6% 0
!???"% +n#ancing tudent earning
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Application of Learning Styles Adaptivity in Mobile Learning Environments
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