Indonesi
an
Journa
l
of El
ect
ri
cal Engineer
ing
an
d
Comp
ut
er
Sc
ie
nce
Vo
l.
1
4
,
No.
1
,
A
pr
il
201
9
, p
p.
413
~
420
IS
S
N: 25
02
-
4752, DO
I: 10
.11
591/ijeecs
.v1
4
.i
1
.pp
413
-
420
413
Journ
al h
om
e
page
:
http:
//
ia
es
core.c
om/j
ourn
als/i
ndex.
ph
p/ij
eecs
Emotion
al augm
ented r
eality
-
bas
ed mobil
e learnin
g design
elements
: a kansei
engin
eering app
roach
Fau
z
iah
Redz
ua
n
, An
-
Nu
r
At
iq
ah
Kh
air
uddin,
Nor
Az
iah
Daud
Facul
t
y
of
Com
pute
r and
Ma
them
at
ic
a
l
Sci
ences,
Univer
si
ti Te
kn
ologi
MA
RA (UiT
M),
Ma
lay
si
a
Art
ic
le
In
f
o
ABSTR
A
CT
Art
ic
le
history:
Re
cei
ved
Oct
2
1,
2018
Re
vised
Dec
1, 2018
Accepte
d
Dec
14
, 201
8
In
re
ce
n
t
ti
m
es,
var
ious
studie
s
have
show
n
tha
t
Augm
ent
ed
Rea
lit
y
(AR)
will
be
th
e
next
wave
of
onli
ne
le
arn
ing
.
Th
is
is
bec
ause
of
th
e
adve
nt
o
f
powerful
sm
art
p
hones
tha
t
has
cha
nged
user
e
xper
ie
n
ce
s,
the
r
eb
y
able
to
inc
re
ase
th
e
c
a
pabi
lit
y
of
AR.
The
r
e
has
b
e
en
m
uch
con
centra
ti
on
in
pre
vious
studie
s
on
cogni
ti
on
to
wards
the
use
of
AR
in
educ
atio
n,
in
which
li
ttle
considerat
i
on
has
bee
n
give
n
to
emotions
which
is
al
so
an
important
aspe
ct
in
learni
n
g.
Based
on
thi
s,
the
pre
sen
t
r
ese
arc
h
ai
m
s
to
identif
y
sa
li
en
t
conne
c
ti
ons
be
t
wee
n
emotions
and
design
el
e
m
ent
s
of
AR
-
base
d
m
obil
e
le
arn
i
ng
m
at
eri
a
l
through
the
a
ppli
c
at
ion
of
th
e
Kansei
Engi
n
ee
ring
(KE)
appr
oac
h
.
In
ord
er
to
a
chi
ev
e
th
is
stud
y
obj
ective,
the
use
of
a
hu
m
an
hea
rt
i
n
re
lation
to
th
e
m
obil
e
AR
application
of
the
KE
a
pproa
ch
was
ado
pte
d
in
th
is
re
sea
rc
h
as
a
c
ase
stud
y
,
in
w
hi
ch
sev
en
spe
ci
m
ens
of
the
m
obil
e
AR
appl
i
ca
t
ion
wer
e
evalua
t
ed
inc
lu
ding
55
emotio
ns
of
Kansei
W
ords
(KW
).
Additi
onall
y
,
th
e
kanse
i
evalua
t
i
on
expe
riment
o
f
thi
s
stud
y
was
ca
rri
ed
out
b
y
28
studen
ts
from
one
of
the
publi
c
univ
ersit
i
e
s,
aft
er
whi
ch
th
e
data
wer
e
ana
l
y
sed
using
Fact
or
and
Princ
i
pal
Com
ponent
Anal
y
sis
.
The
re
sults
of
thi
s
stud
y
show
the
important
pillars
of
emotions
or
Kansei
sem
ant
i
c
spac
e
of
emotions
for
AR
-
base
d
m
obil
e
learni
ng
m
at
erials.
Based
on
Fact
or
Anal
y
sis
,
it
re
ve
al
ed
four
m
ai
n
pil
l
ars;
pr
ofe
ss
iona
l
-
m
oti
v
at
ed
,
conf
used,
wande
ring
-
thri
lled,
challe
n
ging
and
one
a
ddit
ional
pi
ll
ar
;
trusta
bl
e.
B
esi
des
tha
t
,
th
is
re
sea
rc
h
al
so
d
esc
ribe
d
design
el
emen
ts
of
AR
-
base
d
m
obil
e
l
ea
rn
ing
m
at
eri
a
l
that
m
ight
evoke
spe
ci
f
ic
emotions
bas
ed
on
the
id
e
ntified
pillars
.
Final
l
y
,
the
find
i
ngs
of
thi
s
re
sea
rc
h
are
hoped
to
be
appl
i
ca
bl
e
as
a
guide
in
design
during
pre
par
a
ti
on
of
AR
-
base
d
m
obi
le
le
a
rning
m
at
eri
a
ls
with
aff
ective elemen
ts
in the
future.
Ke
yw
or
d
s
:
Augm
ented
Re
al
it
y
Desig
n
el
em
ents
Em
otion
Kan
s
ei
Engine
erin
g
Mob
il
e Lea
rn
i
ng
Copyright
©
201
9
Instit
ut
e
o
f Ad
vanc
ed
Engi
n
ee
r
ing
and
S
cienc
e
.
Al
l
rights re
serv
ed
.
Corres
pond
in
g
Aut
h
or
:
Fauziah
Redz
ua
n,
Faculty
of Com
pu
te
r
an
d
Ma
them
a
ti
cal
Scie
nces,
Un
i
ver
sit
i Te
knol
og
i M
ARA
(U
iTM
)
,
40450
S
hah A
l
a
m
, S
el
ango
r,
Ma
la
ysi
a.
Em
a
il
: fau
zi
ah
r@
tm
sk
.u
it
m
.e
du.m
y
1.
INTROD
U
CTION
This
pa
per
pr
esents
the
res
earch
on
em
otion
al
A
ug
m
ented
Re
al
it
y
(A
R)
-
based
m
obil
e
le
arn
ing
desig
n
el
em
ents
base
d
on
Ka
ns
ei
E
ng
i
neer
i
ng
(
KE)
ap
pro
ach.
C
urre
ntly
,
the
I
nter
net
pe
netrati
on
in
Ma
la
ysi
a
sta
nd
s
at
67%,
thereb
y
m
aking
it
reli
able
to
harness
the
power
of
e
-
le
a
rn
i
ng
for
onli
ne
gl
ob
al
ed
ucati
on
[
1].
Eve
n
with
t
he
per
sist
e
nt
incre
ase
of
onli
ne
c
ourse
e
nrolm
e
nt,
the
re
sti
ll
exist
serio
us
iss
ues
c
on
t
rib
utin
g
to
it
s
low
retenti
on
rates
[
2].
Among
su
c
h
fact
or
s
are;
ed
uca
ti
on
al
le
vel,
f
ai
lure
in
unde
rstan
ding
t
he
con
te
nt,
stud
e
nts’
sat
isf
act
ion
,
a
nd
stu
den
ts
’
m
otivatio
n
[2
-
4].
T
he
pr
el
im
inary
st
ud
y
s
hows
tha
t
m
os
t
stud
ent
s
hav
e
diff
ic
ulty
in
lear
ni
ng
c
o
m
ple
x
co
ur
ses
with
abstract
con
c
epts,
an
d
are
le
ss
sat
isfie
d
w
it
h
the
cur
re
nt
on
li
ne
le
arn
in
g
m
at
erial
s.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2502
-
4752
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci,
Vo
l.
1
4
, N
o.
1
,
A
pr
il
201
9
:
413
–
420
414
Fu
rt
her
m
or
e,
it
has
been
pro
ven
that
Aug
m
ented
Re
al
it
y
(A
R)
is
the
fu
t
ur
e
of
e
-
le
a
rn
i
ng
[
5,6].
A
com
pr
ehe
ns
ive
survey
by
[
7]
prese
nted
t
ha
t
AR
ca
n
be
use
d
for
onli
ne
le
arn
i
ng,
t
hereby
a
ble
to
pr
ov
i
de
eff
ect
ive
le
ar
ni
ng.
Curre
ntly
,
there
has
bee
n
a
rap
id
tre
nd
de
velo
pm
ent
fo
r
AR
app
li
cat
io
ns
an
d
the
ad
opti
on
of
m
ob
il
e
com
pu
ti
ng
de
vices,
su
ch
as
sm
art
phones
an
d
ta
bl
et
s
[8
,9
]
.
T
his
is
becau
se
the
ai
ds
of
m
ultim
edia
el
e
m
ents,
and
interact
ion
i
n
the
desi
gn
of
AR
vis
a
vis
the
abst
ract
co
ncep
t
ca
n
facil
it
at
e
le
arn
ing
[
10,11]
.
Howe
ver,
ed
uc
at
ion
ori
ented
of
AR
a
pp
li
c
at
ion
s
has
not
been
s
o
dee
pl
y
exp
lo
red
[12
]
.
Re
ason
bei
ng
that,
pr
e
vious
resea
rch
es
relat
ed
t
o
A
R
on
ly
focuse
d
on
co
gn
it
ion
wh
il
e
ne
glect
ing
the
e
m
ot
ion
al
or
af
fecti
ve
value
i
n
le
ar
ni
ng [1
3].
Con
se
quently
,
the
delivery
of
the
rig
ht
co
ntent
to
le
ar
ne
rs
thr
ough
e
-
le
arn
i
ng
c
ouple
d
with
go
od
desig
n
will
ultim
a
te
ly
resu
lt
into
an
ef
fec
ti
ve
le
arn
in
g
[
14,1
5].
Mo
reover
,
resea
rch
e
r
s
belie
ved
th
a
t
the
e
m
otion
s
of
st
ud
e
nt
durin
g
c
ourse
e
ngagem
ent
play
a
vital
ro
le
i
n
a
ny
le
arn
i
ng
en
vir
onm
ent,
inclu
ding
in
e
-
le
arn
in
g
[
16,17
]
.
Re
centl
y,
so
m
e
research
er
s
hav
e
ex
plored
pr
og
ressively
on
stu
de
nt’s
e
m
ot
ion
in
e
-
le
a
rn
i
ng,
especial
ly
in
hig
he
r
ed
ucati
on,
but
unf
or
t
unat
el
y
stud
ie
s
that
captur
e
stu
de
nt’s
em
otion
in
e
-
le
ar
ning
ar
e
sti
l
l
la
cking,
e
ve
n
though
the
un
der
sta
nd
i
ng
of
le
arn
e
r’s
em
otion
is
im
po
rtant
in
or
der
to
desi
gn
the
le
arn
i
ng
m
at
erial
[
18
]
.
Gen
e
rall
y,
AR
can
be
descr
i
bed
as
a
m
ultid
isc
ipli
nar
y
fi
el
d
that
e
nco
m
passes
c
om
pu
te
r
gra
ph
ic
s
,
visio
n
an
d
m
ultim
edia,
wh
ic
h
deals
with
t
he
real
-
tim
e
com
bin
at
io
n
of
digi
ta
l
(co
m
pu
te
r
-
gen
e
rated
data
)
an
d
ph
ysi
cal
inf
or
m
at
ion
(r
eal
w
or
l
d)
th
r
ough
diff
e
re
nt
te
chnolo
gical
dev
ic
es
[19].
Mo
bile
AR
(MAR
),
on
t
he
oth
e
r
ha
nd
ext
ends
the
sc
op
e
and
pros
pecti
ve
f
unct
ion
al
it
y
of
AR
,
the
re
by
presenti
ng
a
dynam
ic
pr
oc
edur
e
for peo
ple to
in
te
ract wit
h
c
om
pu
te
rs
an
d digit
al
infor
m
at
i
on [2
0].
Find
i
ngs
in
m
os
t
stu
dies
ha
ve
in
dicat
ed
t
he
ef
fec
ti
ve
ne
ss
of
AR
in
le
arn
i
ng,
suc
h
as
en
han
ci
ng
le
arn
in
g
pe
rform
ance
thr
ough
abstra
ct
co
nc
ept
as
m
entione
d
in
the
stu
dy
of
[
21]
,
an
d
prom
oting
le
a
rn
i
ng
m
ot
ivati
on
as
well
as
increa
sing
le
ar
ning
eng
a
gem
ent
[22].
Des
pite
the
ben
e
fits
highli
gh
te
d
in
pr
evio
us
st
ud
ie
s,
e
ducat
ion
or
ie
nted
A
R
app
li
cat
ions
hav
e
not
ye
t
been
dee
ply
exp
l
or
e
d
[
23]
.
As
m
entioned
earli
er,
AR ca
n be inte
gr
at
e
d wit
h
e
-
l
earn
i
ng, thus
m
aking
it
t
he n
ext w
a
ve
i
n on
l
ine lea
r
ning
[24,25
]
.
On
e
of
t
he
cha
racteri
sti
cs o
f AR is t
he
integ
rati
on
a
nd
i
nter
act
ion
b
et
ween t
he
real and
vi
rtual worl
d,
wh
ic
h
al
lows
huge
ve
rsati
li
ty
and
creati
vit
y
in
le
arn
ing
[
26
]
.
No
t
only
the
le
arn
e
rs
abl
e
to
see
and
li
ste
n
the
virtu
al
in
form
a
ti
on
,
they
ca
n
al
so
m
anipu
la
te
and
al
s
o
a
ble
to
re
peat
sp
ec
ific
par
t
of
th
e
aug
m
en
ta
ti
on
wh
ic
h
can b
e
nef
it
in
their
le
a
rn
i
ng
e
xp
e
rience
[
27]
.
Acc
ordin
g
t
o
[
28
]
t
her
e
are
two
ty
pes
of
c
onte
nts d
e
plo
ye
d
in
a
n
AR
ap
plica
ti
on;
(i)
sta
ti
c
(texts,
3D
m
od
el
)
a
nd
(ii)
dy
nam
i
c
(an
im
at
ion
).
Gen
e
rall
y,
the
interface
inte
ra
ct
ion
betwee
n
the
stud
e
nt
s
an
d
the
dev
ic
e
use
d
in
on
li
ne
le
ar
ning
is
m
ade
by
the
m
ean
bu
tt
ons
or
gest
ur
es
be
cause
stud
e
nts
ca
n
m
ov
e
the
3D
ob
j
ect
with
butt
ons,
sc
al
e
the
si
ze
of
the
vid
e
o,
im
age
or
ob
j
ect
,
or
play
a
vid
e
o
[29,
30
]
.
A
st
udy
by
[31]
cl
assifi
ed
five
us
a
bili
ty
pr
incip
le
s
fo
r
sm
artpho
ne
AR
ap
plica
ti
on
s
,
wh
ic
h
are
;
us
er
-
inf
or
m
at
ion
, c
ogniti
ve,
sup
port,
interact
io
n, an
d usa
ge
re
spe
ct
ively
.
As
sta
te
d
be
f
or
e
,
the
re
is
s
ti
ll
ver
y
few
r
esearch
that
look
i
nto
the
r
ole
of
em
otion
s
with
AR
te
chnolo
gy
to
reinfo
rce
le
ar
ni
ng
.
A
fe
w
e
xam
ples
can
be
fou
nd
i
n
[
30
]
and
[
32
]
,
i
n
wh
ic
h
both
stud
ie
s
rev
eal
e
d
that
m
os
t
of
the
stud
ents
e
njo
ye
d
l
earn
i
ng
diff
ic
ul
t
cou
rse
(a
bs
tr
act
or
te
chn
ic
a
l
cou
rse
)
th
rou
gh
th
e
us
e
of
AR.
I
n
ref
e
re
nce
to
[33],
s
uggeste
d
that
desig
ne
rs
of
a
pro
gra
m
app
li
cat
i
on
m
ay
m
anipu
la
te
the
pro
per
ti
es
of
the
artefact
to
trigg
e
r
a
de
sir
ed
em
otion
al
sta
te
,
and
s
ho
uld
no
t
neg
le
c
t
the
i
m
po
rtan
ce
of
reinfo
rcin
g po
s
it
ive em
otion
s.
Ov
e
r
t
he
ye
ar,
ther
e h
a
ve
bee
n
var
i
ou
s
stu
di
es
car
ried
out on
the
e
ff
ect
i
ve
ness
of
AR
in
le
arn
i
n
g, bu
t
there
is
sti
ll
la
ck
of
em
pirical
wor
k
that
e
xplores
th
e
r
ole
of
em
otion
s
in
s
upportin
g
le
ar
ning
th
rou
gh
t
he
use
of
AR
te
ch
no
l
og
y.
A
few
e
xam
ples
of
AR
researc
hes
th
a
t
hav
e
em
ph
as
iz
ed
on
em
oti
on
s
can
be
f
ound
in
histor
ic
al
ed
uc
at
ion
[
34
]
,
to
ur
is
m
ind
us
try
[
33
]
an
d
c
om
pu
te
r
sci
ence
sub
j
ect
[32].
Re
ce
ntly
,
a
stud
y
by
[3
4]
exam
ined
the
r
ole
of
aca
dem
i
c
achievem
ent
e
m
otion
s
by
c
om
par
ing
virt
ua
l
and
locat
io
n
-
base
d
AR
us
i
ng
AR
histor
ic
al
t
our m
ob
il
e app
li
cat
ion
s.
Kan
s
ei
En
gine
erin
g
(
KE)
i
s
def
i
ned
as
a
te
c
hn
iq
ue
of
an
er
gonom
ic
and
co
nsu
m
er
-
ori
ente
d
te
chnolo
gy
to
translat
e
us
er
’s
Ka
ns
ei
(em
oti
on
s
,
feeli
ngs,
and
dem
and
s
)
into
a
desi
gn
e
lem
ents
[35,3
6]
.
In
oth
e
r
w
ords
,
KE
is
a
te
ch
nolo
gy
w
hich
unit
es
Ka
ns
ei
into
e
ngineeri
ng
realm
s
by
pro
vid
i
ng
a
syst
e
m
at
ic
pr
oce
dure
of
unde
rstan
ding
t
he
insi
gh
t
of
use
r
per
ce
ptio
ns
ab
ou
t
t
he
arte
facts
in
orde
r
t
o
produce
c
on
crete
desig
n
c
har
act
erist
ic
s
that
m
at
ch
co
nsum
er
’s
nee
d
a
nd
de
sire
via
se
veral
ph
ysi
ol
og
ic
al
and
ps
yc
hol
og
ic
al
m
easur
em
ent m
et
ho
ds [
37
]
.
KE
m
et
ho
d
ha
s
bee
n
a
ppli
ed
in
va
rio
us
f
ie
lds,
wh
e
re
it
m
a
inly
aim
s
at
assist
ing
de
sign
e
rs
t
o
unde
rstan
d
co
ns
um
ers’
em
otion
s
a
nd
im
pr
essions
t
o
c
on
t
rib
ute
so
luti
on
s
and
hel
p
pro
vid
e
c
on
c
rete
desig
n
par
am
et
ers
[38].
Am
on
g
th
e
i
m
ple
m
entation
s
of
K
E
wh
ic
h
f
ocu
s
on
the
desig
n
of
physi
cal
pro
du
ct
,
par
ti
cula
rly
IT
artefact
a
re
onli
ne
cl
ot
hing
web
sit
e
[39],
vi
rtual
reali
ty
[40],
on
li
ne
le
ar
ning
[41],
a
nd
vid
e
o
-
base
d
le
ar
ning
[
42
]
.
T
her
e
for
e,
in
this
stu
dy,
K
E
m
et
ho
d
is
em
plo
ye
d,
as
it
is
the
m
os
t
su
it
able
a
ppr
oa
ch
i
n
order t
o
m
eet
t
he object
ive
of
this r
e
searc
h.
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
Em
otional
augmente
d
re
alit
y
-
ba
s
ed
mob
il
e l
ea
r
ning
desig
n el
emen
ts
: a k
ansei
…
(
Fa
uz
ia
h
Re
dzuan
)
415
Ther
e
f
or
e,
the
fo
c
us
of
this
researc
h
is
t
o
eng
i
neer
em
oti
on
al
e
xperie
nc
e
in
order
to
achieve
a
n
eff
ect
ive
form
ula
duri
ng
pr
e
par
at
io
n
of
A
R
m
ob
il
e
le
arn
in
g
m
at
erial
s.
The
inte
ntion
of
t
his
resea
r
ch
is
t
o
pro
vid
e
a
ns
we
rs
to
tw
o
ob
j
ec
ti
ves;
(i)
To
i
de
ntify
the
em
ot
ion
s
of
st
ud
e
nt
s
durin
g
their
exp
e
rience
with
AR
-
base
d
m
ob
il
e
le
arn
in
g
m
at
erial
s
and
(ii)
T
o
ide
ntify
the
sp
eci
fic
em
oti
on
s
ass
ociat
ed
with
the
AR
-
base
d
m
ob
il
e
le
arn
ing
de
sig
n
el
em
ents.
Howe
ver
this
resea
rch
on
ly
disc
u
sses
the
seco
nd
ob
j
ect
ive
as
for
t
he
fir
st
obj
ect
ive
h
as
a
lready
been dis
cusse
d
in
[4
3].
2.
RESEA
R
CH MET
HO
D
In
this
resea
r
ch,
KE
m
et
ho
dolo
gy
has
been
em
plo
ye
d
with
the
quantit
at
ive
ap
proac
h
us
in
g
ps
yc
holo
gical
m
easur
em
ent.
Ov
e
rall
,
this
re
search
f
ollo
ws
KE
Ty
pe
I
I
procedu
re
w
hic
h
is
ad
op
te
d
fro
m
[4
4]
and
div
ide
d
in
to
two
phases;
1
and
2
as
in
Figu
re
1.
Als
o,
li
t
eratur
e
re
views
we
re
do
ne
in
this
rese
arch
i
n
order
to
i
den
ti
fy
issues
an
d
pro
blem
that
occu
rs
in
e
-
le
ar
ning
en
vir
on
m
ent.
I
n
orde
r
to
sup
port
issu
es
and
ob
ta
in
i
nfor
m
at
ion
as
well
as
op
i
nions
re
ga
r
ding
e
-
le
ar
ning
,
a
pr
el
im
inary
inv
est
igati
on
has
be
en
ca
rr
i
e
d
out
with
32
stude
nt
s
fr
om
a
local
public
un
i
ver
si
ty
us
ing
onli
ne
su
r
vey.
Af
te
r
wards,
prob
le
m
sta
teme
nt,
research
qu
e
sti
on
s
a
nd
researc
h
obje
ct
ives
we
re
def
i
ned
base
d
on
fin
dings
from
bo
th
li
te
ratur
e
re
view
s
an
d
pr
el
im
inary
stud
y.
T
his
wa
s
f
ollow
e
d
by
th
e
phase
1
of
t
his
stu
dy,
w
hich
is
to
ide
ntify
im
po
rtant
em
ot
ion
s
i
n
e
-
le
arn
i
ng
e
nv
i
ronm
ent.
This
resu
lt
ed
in
the
sel
ect
ion
of
55
e
m
otion
s
adopted
f
ro
m
a
re
search
ca
rr
ie
d
ou
t
by
[41].
Ap
a
rt
fro
m
that,
these
e
m
ot
ion
s
we
re
al
so
us
e
d
f
or
phase
2
of
th
e
present
resea
rch,
in
w
hich
the
aim
of
the phase
2 is t
o
ide
ntify t
he
desi
gn elem
ents
for
s
pecific e
m
ot
ion
s
w
hich
are
t
he
m
ai
n
f
ocus
of
t
his r
e
s
earch
.
Figure
1.
A
n o
verview
of s
um
m
arize resear
ch
m
et
ho
dolo
gy
steps usin
g K
E Type
II m
et
ho
d
adap
te
d from
[
44
]
Ther
e
f
or
e,
pha
se
2
be
gan
w
it
h
the
sel
ect
i
on
of
a
s
urve
y
ta
rg
et
.
The
first
ste
p
is
pur
posel
y
to
determ
ine
the
fo
c
us
dom
ai
n
that
was
der
i
ve
d
from
the
init
ia
l
wo
r
k
in
phase
1.
T
he
f
ol
lowing
ste
p
w
as
the
pr
e
par
at
io
n
of
the
eval
uation
ta
rg
et
w
hich
c
on
sist
s
of
t
hr
ee
i
m
po
rtant
as
pe
ct
s.
Firstl
y,
th
e
sp
eci
m
en
or
AR
of
m
ob
il
e lea
rn
in
g
m
at
erial
w
as exam
ined.
Bas
ed on t
his,
se
ve
n
s
pecim
ens
wer
e
prepa
re
d t
o
f
ocus
on
t
he
hum
an
anatom
y
cou
rs
e,
par
ti
cula
rly
the
hum
an
hear
t
top
ic
.
T
he
de
ci
sion
wa
s
ba
sed
on
an
ea
rlie
r
prel
i
m
inary
stud
y
and
t
he
obser
vation
act
ivit
y
of
ex
-
ist
in
g
app
li
cat
io
ns
in
the
curre
nt
m
a
rk
et
.
Re
s
ults
f
ro
m
the
pr
el
im
inary
stud
y
re
vealed
that
the
m
ajo
r
it
y
of
t
he
st
udents
hav
e
And
ro
i
d
sm
artpho
ne.
The
c
ho
se
n
t
op
ic
(the
he
art)
f
or
this
stud
y
was
fo
un
d
to
be
a
m
o
ng
the
high
est
top
ic
that
needs
suppo
rt
for
m
ob
il
e
le
a
rn
i
ng
a
nd
has
m
or
e
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2502
-
4752
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci,
Vo
l.
1
4
, N
o.
1
,
A
pr
il
201
9
:
413
–
420
416
op
ti
ons
of
A
R
m
ob
il
e
le
ar
ning
m
at
erial
s
.
The
refo
re,
f
our
s
pecim
ens
of
m
ob
il
e
-
ba
sed
a
pp
li
cat
io
n
wer
e
ob
ta
ine
d
f
ro
m
Goo
gle
Play
St
or
e
,
w
hile
the
rem
ai
nin
g
thre
e
wer
e
ge
ner
at
ed
thr
ough
t
he
dev
el
op
m
ent
of
The
Hu
m
an
Hea
rt
Mob
il
e
AR
a
ppli
cat
ion
(H
e
MAR)
wh
ic
h
was
dev
el
op
e
d
in
an
an
droi
d
platfo
rm
that
us
e
d
ca
m
era
-
base
d
trackin
g
m
et
h
od
in
visu
al
iz
ing
the
AR
sc
ene
via
m
ark
er.
The
functi
onal
requirem
ent
was
identifie
d
f
ro
m
the
obser
vatio
nal
act
iv
it
y,
w
hile
us
e
r
requi
rem
ent
was
ob
ta
ined
from
the
prel
i
m
inary
stud
y.
Also
,
U
nity
and
V
ufo
ria
pl
at
fo
rm
wer
e
use
d
to
de
velo
p
HeMARs,
wh
il
e
us
er
ex
per
ie
nce
(
U
X)
w
hic
h
enco
m
passes
hum
an
factor
a
nd
us
a
bili
ty
as
m
entioned
in
[8
]
was
ta
ke
n
as
a
ba
sis.
Ba
s
ed
on
th
ese,
al
l
the
chosen
se
ve
n
sp
eci
m
ens
have
diff
e
rence
s
a
nd
sim
il
arities
in
desig
n
w
hi
ch
we
re
f
ound
to
fit
the
sel
ect
ion
crit
eria acc
ordi
ng to
t
he rules
of K
E
m
et
ho
d.
Seco
nd
ly
,
t
he
su
bject
s
t
o
part
ic
ipate
in
the
evaluati
on
e
xperim
ent
wer
e
s
el
ect
ed
thr
ough
the
us
e
of
pur
po
si
ve
sam
pling
in
one
of
the
public
unive
rsiti
es
in
Ma
la
ysi
a.
The
s
ubj
e
ct
s
or
pa
rtic
ipants
we
re
unde
rgraduate
stud
e
nts
who
curre
ntly
enroll
ed
or
ha
d
ta
ken
the
hu
m
an
a
natom
y
c
ourse.
M
or
e
ov
er,
the
crit
eria
f
or
t
he
par
ti
ci
pa
nts
a
re
that
th
ey
m
us
t
be
fam
il
iar
with
e
-
le
ar
ni
ng
an
d
a
ble
t
o
e
xpress
em
otion
s
accor
dingly
.
A
s
sta
te
d
by
Na
gam
achi
in
[4
4]
,
a
nu
m
ber
of
20
or
30
peop
le
are
su
f
fici
en
t
to
be
e
m
plo
ye
d
as
su
bject
s
in
KE
research.
Ba
sed
on
this,
35
st
ud
e
nts
we
re
inv
it
ed
for
the
evaluati
on
in
th
is
s
tud
y,
bu
t
only
32
of
t
hem
j
oin
ed
.
Thir
dly,
Sem
antic
Diff
e
re
ntial
(S
D)
scal
e
of
t
he
ex
per
im
ent
was
c
onfig
ur
e
d
a
nd
55
e
m
ot
ions
as
m
entioned
i
n
phase
1
we
r
e
us
ed
i
n
5
-
le
ve
l
SD
scal
e
for
evaluati
on.
F
ourt
hly,
an
extr
act
ing
it
e
m
/c
ateg
ory
to
inv
e
sti
gate
the
de
si
gn
of
t
he
s
pecim
en
(e.g
.
c
ol
our,
siz
e
and
la
yout)
was
car
ried
ou
t.
In
KE
,
the
s
pecific
char
act
e
risti
cs
in
pr
oduct
desi
gn
are
the
it
em
and
the
sm
a
ll
groupin
gs
in
each
it
e
m
are
know
n
as
cat
egory.
The
eval
uation
exp
erim
ent
was
condu
ct
e
d
in
a
li
br
ary
roo
m
to
p
ro
vi
de
a
con
tr
olled
en
vi
ronm
ent.
An
al
ysi
s
of
the
data
colle
ct
ed
from
the
evaluati
on
ex
pe
rim
ent
wer
e
condu
ct
e
d
thr
ough
three
cat
egories
of
analy
sis,
wh
i
c
h
are;
(i)
desc
rip
ti
ve
analy
sis
(f
or
dem
ographi
c
pr
ofi
le
),
(ii)
com
bin
at
ion
of
Fact
or
A
naly
sis
(F
A)
a
nd
P
rincipal
Com
po
ne
nt
A
naly
sis
(P
CA
)
(for
Ka
ns
ei
s
e
m
antic
sp
ace)
,
an
d
(iii
)
P
art
ia
l
Least
Sq
ua
re
(PLS)
(
for
desig
n
el
e
m
ents o
r g
ui
delines).
3.
RESU
LT
S
A
ND AN
ALYSIS
Ther
e
are
t
wo
fin
dings
e
xp
ec
te
d
in
t
his
rese
arch,
w
hich
ar
e
(i)
Kansei
se
m
antic
sp
ace
of
AR
-
ba
se
d
m
ob
il
e
le
arn
in
g
s
pecifica
ll
y
for
Hu
m
an
A
natom
y
le
arn
ing
m
at
erial
s
fo
r
hi
gh
e
r
e
du
c
at
ion
,
(ii)
the
desig
n
el
e
m
ents
in
associat
ion
with
a
sp
eci
fic
em
o
ti
on
.
T
hese
fin
dings
we
re
obt
ai
ned
f
r
om
Fa
ct
or
A
naly
sis
(
FA
)
,
Pr
inci
pal
Com
pone
nt
A
naly
sis
(P
C
A)
a
nd
P
arti
al
Least
S
quare
(PLS)
m
e
thod
i
n
this
stu
dy
usi
ng
the
a
ver
a
ge
evaluati
on
data.
The
F
A
an
d
PCA
with
varim
ax
ro
ta
ti
on
wer
e
pe
rfor
m
ed
to
i
den
ti
fy
t
he
sem
antic
spa
ce
of
AR
-
base
d
m
obil
e
le
arn
in
g
m
at
erial
.
The
F
A
fin
dings
s
ho
w
that,
va
rianc
e
analy
sis
re
sul
t
can
be
ob
ta
i
ned
to
determ
ine
the
m
os
t
sign
ific
ant
facto
rs
of
e
m
ot
ion
.
In
s
ho
rt
base
d
on
the
first
obj
ect
i
ve
the
fin
ding
re
ve
al
s
the
identifie
d
sem
antic
sp
ace
f
or
AR
-
base
d
m
ob
il
e
le
ar
ning
m
at
erial
s
is
s
tructu
red
base
d
on
fi
ve
fact
or
s
or
pill
ars.
T
he
f
our
m
ai
n
pill
ars
are
the
pr
of
es
sion
al
-
m
otivate
d,
c
onf
us
ed
,
wande
rin
g
-
th
ri
ll
ed
and
c
halle
ng
i
ng.
The
a
ddit
ion
al
pill
ar is ide
ntif
ie
d
as tr
us
ta
ble.Th
is
fin
dings
hav
e
b
ee
n disc
us
se
d
in
[4
3].
In
view
of
the
fo
r
eg
oing
ana
ly
sis
as
pr
esen
te
d
ab
ov
e
,
the
desig
n
el
em
e
nts
for
these
pi
ll
ars
wer
e
exp
l
or
e
d
a
nd
s
upporte
d
by
th
e
res
ults
f
r
om
PLS
a
naly
sis.
These
res
ults
ha
ve
influ
e
ntial
relat
ion
to
each
othe
r
that ca
n be
u
se
d
as
a
gu
i
delin
e to
pro
vid
e
A
R
-
base
d
m
ob
il
e lea
rn
i
ng m
ater
ia
ls f
or
sp
eci
fic em
otion
s.
Ba
sed
on
pr
e
vi
ou
s
st
u
dies
i
n
KE,
Partia
l
L
east
Square
(PLS)
is
a
n
a
ppr
opriat
e
analy
sis
to
analy
se
the
relat
ion
s
hi
p
of
Ka
ns
ei
an
d
desi
gn.
T
herefo
re,
PL
S
wa
s
perform
ed
to
identify
the
de
sign
el
em
ents
of
AR
-
base
d
m
ob
il
e
le
arn
in
g
m
ater
ia
ls
for
the
Hu
m
an
A
natom
y
cou
rse
r
el
evan
t
to
t
he
sp
eci
fic
em
otion
s.
Conver
sel
y,
it
is
us
ed
to
dete
rm
ine
the
influ
entia
l
desi
gn
el
e
m
ents
in
each
em
otion
th
at
is
evo
ke
d
by
each
sp
eci
m
en.
As
ex
per
ie
nce
d
duri
ng
the
ea
rly
ph
ase
of
thi
s
researc
h,
the inv
est
igati
on
of
the
desig
n
el
e
m
ents
fo
r
7
sp
eci
m
ens
pro
du
ce
d
19
desi
gn
s
el
em
ents
and
41
valu
es.
This
m
et
ho
d
was
us
e
t
o
a
na
ly
sed
the
relat
ion
s
hi
p
within
m
ulti
var
ia
te
cat
egoric
al
data
th
rou
gh
the
it
em
fo
r
a
va
riable
an
d
cat
e
gory
f
or
t
he
var
ia
ti
ons
of
a
var
ia
ble
.
W
it
h
reg
a
rd
to
PLS
analy
sis,
al
l
th
ese
el
em
ents
(i
tem
/c
at
ego
ry)
wer
e
first
c
onve
rted
i
nto
var
i
ables.
Ther
ea
fter
,
the
y
wer
e
c
om
bin
ed
with
t
he
av
erag
e
value
of
each
re
pr
e
sent
at
ive
of
fou
r
m
ai
n
gro
up
facto
rs
tha
t
wer
e
der
ive
d
f
ro
m
the
pr
evio
us
analy
sis.
Th
is
was
fo
ll
owe
d
by
carryin
g
ou
t
a
PLS
a
naly
sis
to
dis
cov
e
r
th
e
relat
ion
s
hip
be
tween
em
otion
an
d
desig
n
el
e
m
ents.
The
cal
culat
ion
of
PL
S
Ra
nge
was
done
to
determ
i
ne
th
e
influ
e
nce
of th
e elem
ents of e
m
ot
ion
.
Fu
rt
her
m
or
e,
P
LS
Ra
nge
f
or
e
ver
y
em
otion
r
epr
ese
ntin
g
ea
ch
of
the
fact
or
gro
up
s
(
five
f
act
or
s
)
was
cal
c
ulate
d
in
orde
r
t
o
ide
ntif
y
the
in
flue
nc
e
of
desig
n
i
nc
lud
in
g
the
go
od
a
nd
ba
d
de
sign.
T
he
f
orm
ula
to
cal
culat
e the P
LS Ran
ge
is
as
b
el
ow:
PLSRan
ge
=
P
LSMax
–
PLS
Mi
n
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
Em
otional
augmente
d
re
alit
y
-
ba
s
ed
mob
il
e l
ea
r
ning
desig
n el
emen
ts
: a k
ansei
…
(
Fa
uz
ia
h
Re
dzuan
)
417
The
cal
culat
ed
PLS
range
of
each
desi
gn
el
e
m
ents
and
val
ue
in
res
pecti
ve
e
m
otion
was
pr
ese
nted
i
n
a
form
of
a
ta
bl
e.Th
e
ta
ble
co
ntains
tw
o
m
ain
c
olu
m
ns
an
d
fou
r
sub
c
olum
ns
.
The
fir
st
m
ai
n
colum
n
is
ti
tl
ed
as
Desig
n
Ele
m
ent,
wh
ic
h
c
on
ta
in
s
Item
a
nd
Ca
te
gory
of
each
s
pecim
e
n.
T
he
sec
ond
m
ai
n
colum
n
is
Value
and
c
on
ta
in
s
P
LS
value
of
e
m
ot
ion
,
in
w
hi
ch
PLS
Ra
nge
was
cal
culat
ed.
In
facil
it
at
ing
the
cal
culat
ion
of
the
range,
t
his P
L
S v
al
ue was s
ort
ed
in
desce
nding
order
f
or
e
ach categ
or
y.
The
la
r
gest pos
it
ive PLS
val
ue
shows
el
e
m
ents
le
ading
t
o
a
bette
r
desig
n
wh
il
e
t
he
la
r
gest
ne
ga
ti
ve
value
i
nd
i
cat
es
el
e
m
ents
le
adin
g
to
ba
d
desig
n
(as
in
Fig
ure
2
).
In
this
ta
ble,
t
he
a
rr
a
ngem
ent
of
the
m
axi
m
um
value
to
m
ini
m
u
m
value
dem
on
strat
es
t
he
de
gr
ee
of
adap
ta
ti
on
of
de
sign
el
em
ents
that
influ
ence
a
certai
n
e
m
oti
on.
The
m
axim
u
m
value
il
lu
strat
es
the
su
it
abili
ty
of
the
best
de
s
ign
el
em
ents,
wh
il
e
the
m
ini
m
u
m
value
il
l
us
trat
es
the
s
ui
ta
bili
ty
of
the
worst
desi
gn
e
lem
ents
that i
nf
l
uen
ce
the em
otion
.
Figure
2
sho
w
s
the
pa
rtia
l
resu
lt
of
t
he
in
fluen
ti
al
desi
gn
el
e
m
ents
fo
r
t
he
e
m
otion
of
pro
fessio
nal
-
m
ot
iv
at
ed.
In
order
to
vis
ualiz
e
the
se
quenc
e
of
dom
inant
desig
n
el
em
ents
f
or
a
s
pecific
em
otion
,
the
r
esults
are
arr
a
nged
i
n
a
descendin
g
order.
As
the
re
is
lim
i
te
d
sp
a
ce
in
this
pap
e
r,
ther
ef
or
e
on
l
y
the
detai
l
data
fo
r
e
m
otion
profes
sion
al
-
m
otivate
d wil
l be
pro
vi
ded
.
Figure
2
.
A
p
a
r
ti
al
v
ie
w
of t
he
inf
l
uen
ti
al
d
e
s
ign
elem
ents for
em
otion
of
Pr
ofessi
on
al
-
Moti
vated
In
orde
r
to
des
ign
AR
-
base
d
m
ob
il
e
le
arn
in
g
m
at
erial
s,
it
is
i
m
po
rtant
th
at
the
desi
gn
e
r
set
s
so
m
e
pr
i
or
it
ie
s
in
de
sign
i
ng
el
em
ents
accor
ding
to
the
hig
h
i
nf
l
uen
ce
it
em
that
fits
fo
r
a
par
ti
cular
e
m
ot
ion
.
Additi
on
al
ly
,
t
he
high
in
flue
nce
cat
eg
ori
es
for
eac
h
it
em
are
eq
ually
im
portant
a
nd
s
houl
d
be
incl
uded
as
a
gu
i
deline.
F
or
instance,
base
d
on
a
bove
res
ul
t,
the
e
m
otion
of
prof
es
sio
nal
-
m
otivate
d
can
be
ev
ok
e
d
by
it
e
m
s
su
c
h
as
3D
obje
ct
scale
, 3D o
bj
ect
c
at
eg
or
y
colo
ur,
bu
tt
on t
ype, a
nd o
t
her
s
.
Also
,
the
F
act
or
A
naly
sis res
ul
ts show
that t
he
re ar
e
f
our
m
a
in p
il
la
rs
a
nd one a
dd
it
io
nal pi
ll
ar o
f
t
he
Kan
s
ei
e
m
otion
s
in
AR
-
ba
se
d
m
ob
il
e
le
arn
in
g
m
at
erial
for
the
H
um
a
n
A
natom
y
cou
rse
,
pa
rtic
ularly
on
hu
m
an
hear
t
top
ic
.
T
he
f
our
pill
ars
are
prof
essi
on
al
l
y
-
m
otivate
d,
conf
us
ed
,
wa
nd
e
rin
g
-
t
hr
il
le
d
an
d
chall
eng
i
ng
w
hile
trustable
is
con
sid
ere
d
as
the
add
it
io
nal
pill
ar
as
di
scusse
d
in
[
43]
.
From
the
above
pr
ese
nted
a
nal
ysi
s,
the
m
os
t
su
it
able
AR
-
ba
sed
m
ob
il
e
le
arn
i
ng
m
at
erial
to
these
pill
ars
was
e
xp
l
or
e
d.
Thi
s
resu
lt
can
be
use
d
as
a
gu
idel
ine
to
prov
i
de
AR
-
base
d
m
obil
e
le
arn
ing
m
at
erial
s
fo
r
sp
e
ci
fic
pill
ar,
thus
an
enab
le
r
to
disc
ov
e
r
a
nd
vis
ua
li
ze
the
sp
eci
m
en
vis
a
vis
the
em
otion
th
at
hav
e
in
flue
ntial
relat
ion
t
o
eac
h
oth
e
r.
Ther
e
f
or
e
ba
se
d
on
the
res
ults,
the
desig
n
e
lem
ents
that
hav
e
the
hi
gh
es
t
in
fluen
ce
in
el
ic
it
ing
the
first
pill
ar (pro
fessio
nal
-
m
otivate
d)
has bee
n
inte
rpreted
a
s:
1.
3D ob
j
ect
s
houl
d
be
ab
le
t
o
sc
al
e in A
R
.
2.
3D ob
j
ect
s
houl
d
use
m
or
e tha
n
th
ree c
olour
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2502
-
4752
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci,
Vo
l.
1
4
, N
o.
1
,
A
pr
il
201
9
:
413
–
420
418
3.
3D ob
j
ect
co
l
our
sho
uld
be
i
n g
rad
ie
nt.
4.
Butt
on ty
pe
s
houl
d be ico
n.
5.
Inform
at
ion
prov
i
ded s
hould
be
sim
ple.
6.
Butt
on size s
houl
d be m
ediu
m
.
7.
Text fo
nt size s
hould be
m
edium
.
8.
Error ha
ndli
ng
sh
oul
d be
provi
ded
.
9.
In
st
ru
ct
io
n
s
ho
uld
be p
rovide
d.
10.
The b
utton sh
ould
be g
rou
ped.
Nev
e
rtheless
,
t
he
desig
n
el
e
m
ents
in
the
f
ifth
a
dd
it
io
nal
pill
ar
(t
ru
sta
bl
e)
can
al
so
be
ta
ken
int
o
consi
der
at
io
n
a
s
it
con
ta
ins
s
om
e
po
sit
ive
em
ot
ion
el
em
en
ts
that
are
si
m
i
la
r
to
the
m
ai
n
pill
ar.
Am
on
g
thes
e
are;
te
xt
f
ont
s
iz
e
that
sho
uld
be
m
edium
and
te
xt
font
c
olo
ur
that
sho
uld
be
one
c
olour.
Ba
sed
on
the
li
ste
d
desig
n
el
em
ents
for
the
m
ain
pill
ar,
the
m
os
tly
ty
pe
of
butt
on
s
houl
d
be
an
ic
on
and
butt
on
s
houl
d
be
gro
up
e
d.
How
ever,
it
m
us
t
be
no
te
d
that
th
e
us
e
of
a
n
ic
on
is
f
or
easy
ac
cess
of
i
nfor
m
at
ion
an
d
gro
upin
g
the
inf
or
m
at
ion
accor
ding
to
it
s
im
po
rtance
[
8].
As
re
gards
to
the
3D
ob
j
ect
,
m
or
e
colours
in
gradie
nt
sho
uld
be
us
e
d.
T
he
ty
pe
of
3D
obj
ect
t
o
be
em
plo
ye
d
shou
l
d
hav
e
le
ss
infl
uen
ce
to
desig
n
el
em
ents,
but
m
us
t
be
us
e
d
to
ev
oke
wand
erin
g
-
th
rill
ed
e
m
ot
ion
,
w
her
e
the
ty
pe
of
3D
obj
ect
sho
uld
al
so
be
dynam
ic
(a
nim
at
ion
).
This
is i
n
li
ne wit
h
t
he
st
ud
y
of b
y
[28].
Additi
on
al
ly
,
t
he
Kan
sei
c
on
cept
of
AR
-
ba
sed
m
ob
il
e
le
arn
i
ng
m
at
erial
s
can
be
ge
ner
a
te
d
bas
ed
on
the
analy
sis
of
the
resu
lt
s.
T
hi
s
is
beca
us
e
t
he
pr
im
ary
e
m
otions
beco
m
e
pill
ars
of
the
structu
re
of
se
m
a
ntic
sp
ace,
wh
il
e
seconda
ry
e
m
otion
s
are
in
with
m
e
m
ber
gr
oup
f
or
each
pil
la
r.
O
ver
al
l,
re
su
lt
sh
ows
that
there
are
fi
ve
pill
ars
or
fact
or
s
(
fou
r
m
ai
n
pill
ars
and
one
a
ddit
ion
al
pill
ar)
t
ha
t
sh
ould
be
co
ns
ide
red
in
des
ign
i
ng
AR
-
base
d
m
o
bile
le
arn
in
g
m
at
eri
al
s.
In
orde
r
to
acq
uir
e
the
desire
d
resu
lt
s,
the
de
sign
e
r
is
adv
is
able
to
choose
t
he
be
s
t
com
bin
at
ion
po
s
sible
f
ro
m
a
concept
of
e
m
ot
ion
wh
ic
h
m
ay
con
sist
of
on
e
or
m
or
e
e
lem
ents
of em
otion
.
In
c
on
cl
us
io
n,
the
ou
tc
om
e
of
the
evaluati
on
exp
e
rim
ent
ha
s
been
descr
i
be
d
in
detai
l
in
this
pap
e
r.
Af
te
r
pe
rfo
rm
i
ng
sta
ti
sti
cal
analy
sis,
the
fin
al
resu
lt
s
ac
hie
ved
the
obje
ct
ive
for
this
res
earch
.
As
pr
es
ented
pr
e
viously
,
the
res
ult
of
sem
a
ntic
sp
ace
of
Kan
s
ei
em
otion
in
AR
-
base
d
m
ob
il
e
le
arn
ing
is
obta
ine
d
thr
ough
PCA
a
nd
F
A
m
et
ho
d,
w
herea
s
the
desig
n
el
e
m
ents
of
AR
-
base
d
m
ob
il
e
le
arn
i
ng
m
at
erial
s
fo
r
sp
eci
fi
c
e
m
otion
s a
re a
naly
sed by
us
ing t
he PLS
m
e
thod.
4.
CONCL
US
I
O
N
This
resea
rch
has
su
c
cessf
ully
achieved
th
e
obj
ect
ive
that
i
s
to
identify
th
e
sp
eci
fic
ka
nse
i
e
m
otion
s
associat
ed
wit
h
the
AR
-
base
d
m
ob
il
e
le
ar
ni
ng
desi
gn
el
e
m
ents.
I
n
orde
r
to
achie
ve
t
he
obj
ect
ive
,
Kan
s
ei
En
gin
eeri
ng
m
et
hodo
l
og
y
wa
s
us
e
d
beca
us
e
it
is
the
m
os
t
su
it
able
m
et
ho
d
that
sp
e
ci
fical
ly
m
easur
es
us
er
’
s
e
m
otion
s
e
voke
d
by
product
s
and
i
ncor
porates
them
int
o
a
pro
duct
de
sign,
in
w
hic
h
seve
ral
sta
ti
sti
cal
analy
sis we
re c
arr
ie
d o
ut to
ac
hieve
t
he desir
ed result
for
thi
s stu
dy.
Ba
sed
on
pre
vi
ou
s
fin
ding
of
the
five
fact
or
s
or
pill
ars
that
ref
e
r
t
o
t
he
em
otion
s
wh
ic
h
is
t
he
structu
re
of
ka
ns
ei
sem
antic
sp
ace
of
AR
-
ba
sed
m
ob
il
e
le
arn
i
ng
m
at
erial
fo
r
t
he
Hu
m
an
A
natom
y
course.
Am
on
g
the f
iv
e
factor
s or
pill
ars,
f
our
of
t
he
m
are
m
ai
n
pillars
w
hile
the
ot
her
one
is
an
a
dd
it
io
nal
pill
ar
.
The
m
ai
n
pill
ars
a
re
prof
e
ssio
na
ll
y
-
m
otivate
d,
conf
us
ed
,
wa
nderi
ng
-
th
rill
ed
and
chall
en
gi
ng.
Me
anwhil
e,
the
add
it
io
nal
pill
a
r
is
tr
us
ta
ble.
T
her
e
fore,
t
hese
em
otion
s
are
c
ru
ci
al
in
onli
ne
le
arn
i
ng
sp
eci
fical
ly
in
AR
-
ba
sed
m
ob
il
e
le
arn
in
g.
The
desi
gn
el
e
m
ents
of
AR
-
ba
sed
m
ob
il
e
lear
ni
ng
m
at
eria
l
wer
e
der
i
ved
fr
om
the
resu
lt
of
kan
s
ei
sem
antic
sp
ace of
AR
-
base
d m
ob
il
e
le
arn
in
g
m
at
erial
that
is t
he
fi
ve
fact
or
s
or
pill
ars.
The fin
dings
re
veal the
five
pill
ars
of
e
m
otion
s
with
the
corres
pond
ing
fi
ve
set
s
of
desig
n
el
e
m
ents
wh
ic
h
ha
ve
t
he
highest
infl
uen
ce
to evo
ke
em
otion
s
.
The
fin
dings
of
this
researc
h
hav
e
pr
ov
e
n
that
AR
-
ba
se
d
m
ob
il
e
le
arni
ng
ca
n
be
de
sign
e
d.
Thi
s
ind
ic
at
es
that
e
du
cat
io
nal
c
onte
nt,
inclu
ding
on
li
ne
or
virtua
l
le
arn
in
g
m
ater
ia
l
can
be
de
sign
e
d.
T
his
c
a
n
be
us
e
d
as
an
em
ph
asi
s
on
stu
dent
s
with
reg
a
rd
t
o
their
m
ai
n
fo
cus
(st
ud
e
nt
centric)
a
nd
al
ig
n
with
their
rele
van
t
course.
In
orde
r
to
gl
ob
al
iz
e
on
li
ne
le
a
rn
i
ng,
it
is
i
m
po
rtant
that
hig
he
r
e
du
cat
io
nal
inst
it
ution
s
(
HE
Is)
m
us
t
sta
y
abr
east
w
it
h
cur
ren
t
te
c
hnology
an
d
t
rends
with
res
pect
to
the
st
ud
e
nts’
ps
yc
holo
gical
needs
.
This
researc
h
giv
e
s
an
insig
ht
t
o
HE
Is
i
n
a
ff
e
ct
ive
desi
gn
i
ng
on
li
ne
le
ar
ni
ng
sp
eci
fical
ly
AR
-
base
d
m
ob
ile
le
arn
in
g
m
at
eri
al
.
Thu
s
,
it
is
e
xp
ect
e
d
to
f
orm
a
basis
fo
r
m
or
e
resear
ch
on
on
li
ne
le
ar
ni
ng
,
par
ti
cularl
y
AR
-
base
d
m
ob
il
e lea
rn
i
ng m
at
eri
al
s.
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
Em
otional
augmente
d
re
alit
y
-
ba
s
ed
mob
il
e l
ea
r
ning
desig
n el
emen
ts
: a k
ansei
…
(
Fa
uz
ia
h
Re
dzuan
)
419
ACKN
OWLE
DGE
MENTS
We
woul
d
li
ke
to
t
hank
t
he
F
acult
y
of
Com
pu
te
r
a
nd
Ma
them
atical
Scie
nces,
U
niv
e
rsiti
Tek
no
l
og
i
MARA
(U
iTM
),
S
ha
h
Alam
, Mal
ay
sia
f
or
t
he
sup
port to
t
his r
esea
rch.
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nte
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l
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e
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s
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joie
SP
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par
ing
Virtua
l
a
nd
Loc
a
ti
on
-
B
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d
Augm
ent
e
d
Rea
lit
y
Mobil
e
Le
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ing
:
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nd
Learni
n
g
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uca
t
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l
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ec
hn
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y
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m
ac
hi
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anse
i
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ne
er
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-
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nt
ed
Tec
hnolog
y
for
Pro
duct
Deve
lopme
nt.
Inte
rna
ti
ona
l
Jou
rna
l
of
Industri
al E
rgonom
ic
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19
95;
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11.
[36]
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m
ac
hi
M.
Kansei
Engi
ne
e
ring
as
a
Pow
e
rful
Consum
er
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Orie
nte
d
T
ec
hn
olog
y
for
Product
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d
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AM
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n :
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e
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i
E
ngine
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y
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(1):
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Schütt
e
S.
Eng
i
nee
ring
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nal
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s
in
P
roduc
t
Design
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Engi
ne
eri
ng
in
Deve
lo
pm
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.
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nkopi
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T
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AM
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oor
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a
m
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hi
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c
ept
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Onlin
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h
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l
ity
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Based
Learni
ng
Envi
ronm
ent
with
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a
l
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ti
on
.
2
011;
6
(Jan)
:
25
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42.
[41]
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uan
F,
Lo
km
an
AM
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Oth
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an
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Kans
ei
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ant
ic
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ac
e
for
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n
in
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ng.
2014
3r
d
Inte
rna
ti
ona
l
Co
nfe
re
nc
e
on
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e
r
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n
ce a
nd
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gine
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US
Er
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uan
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lu
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ti
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s’
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l
Resp
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in
Video
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Based
Le
arn
ing
using
Kanse
i
Engi
ne
eri
ng.
20
16
4th
Inte
rna
ti
o
nal
Confer
en
ce
on
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er
Scie
nce
and
Engi
ne
eri
ng
(i
-
US
Er
2016).
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Redz
u
an
F,
Daud
NA
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Ev
aluati
ng
Stud
ent
s’
Emotional
Res
ponse
in
Augm
e
nte
d
R
ea
l
ity
-
Bas
ed
Mobi
le
Learni
n
g
using
Kansei
Engi
nee
r
ing.
Com
m
unic
at
ions
in
Com
pute
r
and
Inform
at
i
on
Scie
nce.
5t
h
Inte
rna
ti
ona
l
Co
nfe
re
nc
e
on
Us
e
r
Scie
n
ce a
nd
En
gine
er
ing
(i
-
US
Er
2018).
Spring
er.
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9
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89.
[44]
Lokman
AM
,
N
aga
m
ac
hi
M.
Ka
nsei
Engi
ne
eri
ng
Proce
dure
s:
K
a
nsei
Engi
ne
eri
ng
T
y
p
e
II.
Innov
a
ti
ons
of
Kansei
Engi
ne
eri
ng.
Ne
w York:
CRC Pr
ess.
Tay
l
or
&
Fr
anc
is
Group
.
20
11.
BIOGR
AP
HI
ES OF
A
UTH
ORS
Fauzi
ah
R
edz
ua
n
gra
duated
fro
m
Univer
sit
y
of
Nebra
ska
-
Li
n
coln,
US
A
in
1997
and
pursued
her
stud
y
in
Mast
er
of
Sci
ence
in
Inf
orm
at
ion
Techn
olog
y
from
Univ
ersit
i
Sains
Mal
a
y
sia
(US
M)
in
2000.
She
furth
ers
her
knowled
ge
expl
or
at
ion
a
nd
awa
rde
d
Do
ct
or
of
Philosop
h
y
in
S
y
stem
Scie
nc
es
and
Mana
gement
from
Univer
sit
i
Keb
angsa
an
Ma
lay
si
a
(UK
M)
in
201
7.
Her
cur
re
n
t
int
er
est
a
nd
r
ese
arc
h
ar
ea
s
inc
lud
e
Emotiona
l
/Affe
c
ti
ve
E
-
L
ea
rning
,
Kansei
/Affe
c
ti
v
e
Engi
ne
eri
ng
and
al
so Da
ta a
nd
Data
b
ase
Man
agem
ent
.
An
-
Nur
Atiqa
h
Khair
uddin
jo
in
ed
Sulta
n
Idr
is
Educ
a
ti
on
Univ
ersity
(UP
SI)
in
2010,
as
an
Inform
at
ion
Tec
hnolog
y
Off
ic
er
.
She
re
ce
iv
ed
his
B.
S.
and
M.
S.
in
Inform
at
io
n
Te
chno
lo
g
y
from
Univer
siti
Te
knologi
MA
RA
(UiTM).
Her
re
sea
rc
h
in
te
r
ests
inc
lude
m
obil
e
appl
icat
ion,
Kansei
Engi
n
eering
and
Mobil
e
Augm
ent
ed
Re
al
ity
.
She
w
on
the
bronz
e
m
ed
al
of
the
23th
Inte
rna
ti
ona
l
In
vent
ion
Innova
t
i
on
and
Technol
og
y
Exhi
bi
ti
on
(IT
EX)
in
2012
.
She
r
ecei
ved
Best
Paper
Aw
a
rd
at
Th
e
5th
I
nte
rna
ti
ona
l
Co
nfe
re
nc
e
on
Us
er
Scie
n
ce
and
Engi
ne
eri
ng
(i
-
US
Er
)
2018.
Cu
rre
ntly
,
she
is
re
spons
ibl
e
for
th
e
Univer
sit
y
In
tegrat
ed
Mana
gem
ent
S
y
s
te
m
in
UP
SI.
Nor
Azia
h
Daud
gra
duat
ed
from
Univer
siti
Utar
a
Malay
sia
(UU
M)
in
1997
and
cont
inue
d
he
r
stud
y
in
Master
of
Scie
n
ce
in
In
form
at
ion
T
ec
hn
olog
y
in
Univ
er
siti
T
eknol
ogi
MA
RA
(UiTM
)
in
2003.
She
r
ecen
tly
was
awa
rd
ed
Doctor
of
Philosoph
y
in
Infor
m
at
ion
Scie
n
ce
f
rom
Univer
siti
Keba
ngsaa
n
Ma
l
a
y
si
a
(UK
M) i
n
2018.
Her curre
n
t
intere
sts
are
D
a
ta
base
,
Persuasi
ve
Design
and
E
-
learni
ng
.
Evaluation Warning : The document was created with Spire.PDF for Python.