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I
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I
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9
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No
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3
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Sep
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2020
:
6
6
5
-
674
666
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2.
RE
S
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M
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O
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n
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k
[
1
5
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.
A
d
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ased
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w
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t
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a
m
s
[
1
6
]
ap
p
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B
esid
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ltid
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ts
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lti
n
o
m
ial
L
o
g
it
Mo
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el
(
MRC
M
L
M)
[
1
7
]
w
as
ap
p
lied
to
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te
th
e
q
u
alit
y
o
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ic
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la
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T
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p
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p
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latio
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lled
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ch
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ip
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ar
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ad
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tu
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en
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[
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A
to
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1
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5
0
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tu
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it
h
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iv
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els
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m
1
1
9
s
ch
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ati
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ied
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1
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ase
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2
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2
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u
r
e
1
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n
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ir
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ase,
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at
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at
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s
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Fig
u
r
e
1
.
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o
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r
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ig
ital to
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l d
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I
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v
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&
R
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s
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d
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I
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N:
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8822
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8822
I
n
t
.
J
.
E
v
al
.
&
R
es
.
E
d
u
c
.
Vo
l.
9
,
No
.
3
,
Sep
tem
b
er
2020
:
6
6
5
-
674
668
T
h
e
f
in
al
co
m
p
o
n
e
n
t
o
f
I
T
AR
is
a
d
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n
o
s
tic
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ep
o
r
t.
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h
is
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p
o
n
en
t
ca
n
s
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o
w
th
e
i
n
d
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v
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al
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ep
o
r
t
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d
th
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s
d
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n
o
s
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s
r
ep
o
r
t.
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h
e
in
d
iv
id
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al
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ep
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r
t
is
a
d
at
a
-
d
r
iv
e
n
est
i
m
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tio
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o
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d
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s
c
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r
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e
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etails
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th
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m
e
[
2
2
]
r
eg
ar
d
in
g
t
h
e
f
o
u
r
k
e
y
asp
ec
ts
:
(
i)
w
h
at
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d
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(
ii)
w
h
at
s
t
u
d
en
t
s
n
ee
d
to
lear
n
; (
iii)
h
o
w
to
lear
n
it,
a
n
d
(
iv
)
h
o
w
w
e
ll st
u
d
en
t
s
u
n
d
er
s
ta
n
d
it.
I
n
th
e
th
ir
d
p
h
ase,
r
esear
ch
er
s
i
m
p
le
m
e
n
ted
an
d
test
ed
th
e
p
r
o
to
ty
p
e
w
it
h
1
,
5
0
4
T
h
ai
s
ev
en
th
-
g
r
ad
e
s
tu
d
e
n
ts
.
I
f
t
h
e
d
i
g
ital
to
o
l
w
a
s
in
a
p
p
r
o
p
r
iate
f
o
r
teac
h
er
s
an
d
s
tu
d
en
ts
,
r
esear
c
h
er
s
co
n
d
u
c
ted
iter
ativ
e
c
y
cle
s
o
f
test
i
n
g
a
n
d
r
ef
i
n
e
m
en
t
o
f
s
o
lu
tio
n
s
to
f
i
n
d
s
u
itab
le
w
a
y
s
to
i
m
p
le
m
e
n
t
i
n
t
h
e
ac
t
u
al
class
r
o
o
m
co
n
tex
t.
I
n
th
e
f
i
n
al
p
h
ase,
r
esear
c
h
e
r
s
g
e
n
er
ated
th
e
q
u
alit
y
ev
id
en
ce
o
f
t
h
e
p
r
o
to
t
y
p
e
b
y
v
a
lid
atin
g
it
t
h
r
o
u
g
h
a
m
ea
s
u
r
e
m
e
n
t
m
o
d
el.
R
ese
ar
ch
er
s
e
m
p
lo
y
ed
MR
C
M
L
M
to
ex
a
m
in
e
t
h
e
in
ter
n
al
s
tr
u
ctu
r
e
ev
id
en
ce
o
f
v
alid
it
y
b
ased
o
n
t
h
e
co
m
p
ar
is
o
n
o
f
m
o
d
el
f
it
to
en
s
u
r
e
t
h
at
t
h
e
s
tr
u
ctu
r
e
o
f
th
e
d
ia
g
n
o
s
tic
to
o
l
i
n
t
w
o
-
d
i
m
en
s
io
n
s
(
i.e
.
M
A
P
an
d
SLO)
f
it
s
b
etter
t
h
an
o
n
e
d
i
m
en
s
io
n
.
T
h
e
r
eq
u
ir
ed
s
a
m
p
le
s
iz
e
f
o
r
esti
m
atio
n
o
f
ite
m
p
ar
a
m
eter
s
i
n
th
e
m
u
l
tid
i
m
en
s
io
n
a
l
m
o
d
el
o
f
th
e
R
a
s
ch
-
f
a
m
i
l
y
m
o
d
el
s
is
4
0
0
to
5
0
0
t
o
p
r
o
v
id
e
ac
cu
r
ate
p
ar
am
eter
esti
m
ate
s
[
1
9
,
2
3
]
.
I
n
ad
d
itio
n
,
a
W
r
ig
h
t
m
ap
was
u
s
ed
to
s
u
p
p
o
r
t
th
e
v
alid
at
io
n
to
o
l
b
ec
au
s
e
it
co
m
b
i
n
ed
th
e
co
n
s
tr
u
c
t
f
o
r
m
ea
s
u
r
i
n
g
M
A
P
an
d
SL
O
id
ea
w
it
h
th
e
MR
C
M
L
M
m
o
d
el,
a
p
o
w
er
f
u
l
m
ea
n
s
to
in
ter
p
r
et
th
e
s
tu
d
e
n
ts
’
MP
in
ea
ch
d
i
m
e
n
s
io
n
[
1
5
]
.
Mo
r
e
o
v
er
,
th
e
lo
w
s
tan
d
ar
d
er
r
o
r
o
f
m
ea
s
u
r
e
m
e
n
t
(
SEM
(
)
)
an
d
th
e
ac
ce
p
tab
le
v
al
u
es
o
f
in
f
it
an
d
o
u
t
f
it
m
ea
n
s
w
o
u
ld
d
eter
m
in
e
w
h
et
h
er
t
h
e
d
ig
ital
to
o
l
h
a
s
ac
cu
r
ac
y
,
co
n
s
is
te
n
c
y
,
a
n
d
s
ta
b
ilit
y
to
d
iag
n
o
s
e
in
m
u
ltip
le
p
r
o
f
icien
cies.
2
.
3
.
M
P
ins
t
ru
m
ent
A
ll
MP
task
s
ar
e
cr
ea
ted
ac
co
r
d
in
g
to
t
h
e
co
r
e
cu
r
r
icu
l
u
m
.
T
h
e
task
s
w
er
e
re
-
d
esi
g
n
ed
ac
co
r
d
in
g
to
teac
h
er
s
an
d
co
n
ten
t
ex
p
er
ts
’
f
ee
d
b
ac
k
as
w
ell
as
an
i
n
iti
al
em
p
ir
ical
an
a
l
y
s
is
o
f
t
h
e
p
ilo
t
test
in
g
,
u
s
i
n
g
th
e
W
r
ig
h
t
m
ap
,
ite
m
f
it,
an
d
s
tep
f
it
f
o
r
v
alid
atio
n
p
u
r
p
o
s
e
an
d
th
e
s
tr
u
ctu
r
al
m
o
d
el
o
f
m
ea
s
u
r
e
m
en
t,
th
e
i
n
ter
n
a
l
co
n
s
i
s
te
n
c
y
an
d
s
p
lit
-
h
alf
r
eliab
ilit
y
co
ef
f
icie
n
t
s
f
o
r
r
eliab
ilit
y
i
n
f
o
r
m
atio
n
.
B
ased
o
n
th
e
p
ilo
t
test
i
n
g
r
es
u
lt
s
,
r
esear
ch
er
s
d
elete
d
th
o
s
e
tas
k
s
th
at
o
v
er
lap
p
ed
th
e
co
n
te
n
t
k
n
o
w
led
g
e
b
etw
ee
n
ite
m
s
a
n
d
als
o
s
o
m
e
ta
s
k
s
th
a
t
w
er
e
f
o
u
n
d
in
ap
p
r
o
p
r
iate
f
o
r
s
ev
en
t
h
-
g
r
a
d
e
s
tu
d
en
t
s
.
S
u
b
s
eq
u
e
n
tl
y
,
a
m
i
x
ed
f
o
r
m
at
w
as
d
ev
elo
p
ed
in
clu
d
in
g
o
p
en
-
en
d
ed
q
u
esti
o
n
s
a
n
d
s
elec
ted
-
r
e
s
p
o
n
s
e
test
ite
m
s
.
A
s
p
ec
if
ic
s
co
r
i
n
g
g
u
id
e
w
a
s
u
s
ed
to
ass
e
s
s
s
t
u
d
en
t
s
’
M
AP
an
d
S
L
O
as
t
h
e
t
w
o
MP
d
i
m
en
s
io
n
s
.
T
h
e
s
co
r
es
o
f
t
h
is
s
co
r
in
g
g
u
i
d
e
r
an
g
ed
f
r
o
m
0
to
4
a
n
d
0
to
3
f
o
r
M
A
P
an
d
S
L
O,
r
e
s
p
ec
tiv
el
y
,
i
n
d
icatin
g
in
ap
p
r
o
p
r
iate,
p
ar
tly
ap
p
r
o
p
r
i
ate,
m
o
s
t
ap
p
r
o
p
r
iate,
an
d
b
e
y
o
n
d
p
r
o
f
icien
c
y
le
v
els.
T
h
e
s
co
r
in
g
g
u
id
e
w
as
u
s
ed
f
o
r
th
e
a
s
s
e
s
s
m
en
t to
o
l f
o
r
th
e
en
tire
class
.
3.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
I
O
N
Af
ter
r
esear
ch
er
s
i
m
p
le
m
e
n
te
d
th
e
I
T
A
R
in
t
h
e
m
at
h
e
m
a
tic
s
class
r
o
o
m
f
o
r
a
y
ea
r
in
t
h
e
I
s
an
r
eg
io
n
,
T
h
ailan
d
,
r
esear
ch
er
s
ai
m
ed
to
v
alid
ate
th
e
cr
ea
ted
d
ig
ital
to
o
l
f
o
r
d
iag
n
o
s
in
g
m
at
h
e
m
at
ical
p
r
o
f
icien
c
y
i
n
th
e
Nu
m
b
er
an
d
Alg
eb
r
a
Stra
n
d
o
f
T
h
ai
s
ev
en
t
h
-
g
r
ad
e
s
t
u
d
en
ts
i
n
ter
m
s
o
f
its
ac
cu
r
ac
y
,
co
n
s
is
ten
c
y
,
s
tab
ilit
y
u
s
i
n
g
v
alid
atio
n
b
ased
o
n
in
t
er
n
al
s
tr
u
c
tu
r
e,
r
eliab
ilit
y
,
a
n
d
ite
m
f
i
t.
T
h
e
r
esu
lts
o
f
t
h
i
s
s
tu
d
y
ar
e
p
r
esen
ted
b
y
f
o
llo
w
in
g
t
h
e
th
r
ee
m
e
th
o
d
s
o
f
th
e
v
a
lid
atio
n
an
a
l
y
s
is
,
n
a
m
el
y
v
alid
it
y
ev
id
e
n
c
e,
r
eliab
ilit
y
ev
id
en
ce
,
an
d
ite
m
f
it.
3
.
1
.
Va
lid
it
y
ev
idence
Af
ter
r
esear
ch
er
s
tr
ied
o
u
t
th
e
cr
ea
ted
d
ig
ital
d
iag
n
o
s
tic
to
o
l,
r
esear
ch
er
s
in
ter
v
ie
w
ed
th
o
s
e
s
tu
d
en
ts
r
eg
ar
d
in
g
t
h
eir
u
n
d
er
s
ta
n
d
i
n
g
o
f
th
e
co
n
ten
ts
a
n
d
th
e
r
ele
v
an
c
y
o
f
th
e
ta
s
k
s
i
n
t
h
e
d
ig
ital
d
i
ag
n
o
s
tic
to
o
l.
T
h
e
r
esu
lts
r
e
v
ea
led
th
a
t
s
t
u
d
en
ts
u
n
d
er
s
ta
n
d
w
ell
ab
o
u
t
th
e
ite
m
s
as
e
x
p
ec
ted
b
y
r
es
ea
r
ch
er
s
.
B
esid
es,
r
esear
ch
er
s
also
u
tili
ze
d
t
h
eir
f
ee
d
b
ac
k
to
i
m
p
r
o
v
e
t
h
e
tas
k
s
a
n
d
s
co
r
in
g
b
ef
o
r
e
co
n
d
u
ctin
g
in
th
e
ac
t
u
a
l
class
r
o
o
m
co
n
te
x
t.
T
h
e
s
ec
o
n
d
v
alid
atio
n
o
n
th
e
in
ter
n
a
l
s
tr
u
ct
u
r
e
o
f
t
h
e
d
i
g
ita
l
to
o
l
in
ter
m
s
o
f
its
ac
cu
r
ac
y
o
f
t
h
e
MP
co
n
s
tr
u
ct
w
a
s
co
n
d
u
c
ted
b
y
c
o
m
p
ar
i
n
g
t
h
e
m
o
d
el
f
it,
f
o
r
t
h
e
u
n
id
i
m
en
s
io
n
al
a
n
d
m
u
ltid
i
m
e
n
s
io
n
al
m
o
d
els.
T
h
e
u
n
id
i
m
e
n
s
io
n
al
m
o
d
el
m
ea
n
s
a
co
m
p
o
s
it
io
n
o
f
a
ll
th
e
task
s
in
to
o
n
e
d
im
e
n
s
io
n
w
h
ile
th
e
m
u
ltid
i
m
e
n
s
io
n
a
l
m
o
d
el
m
ea
n
s
s
ep
ar
atio
n
o
f
th
e
tas
k
s
in
to
1
1
task
s
an
d
7
task
s
f
o
r
th
e
r
esp
ec
tiv
e
M
A
P
an
d
S
L
O
d
i
m
e
n
s
io
n
s
a
s
s
h
o
wn
in
Fi
g
u
r
e
4
an
d
Fi
g
u
r
e
5
.
T
h
e
r
esu
lts
r
e
v
ea
led
th
at
m
u
lti
d
i
m
en
s
io
n
al
m
o
d
el
h
ad
a
s
t
a
tis
tica
l
f
it
s
ig
n
i
f
ica
n
tl
y
b
etter
t
h
an
u
n
id
i
m
en
s
io
n
al
m
o
d
el
th
r
o
u
g
h
t
h
e
L
i
k
eli
h
o
o
d
R
atio
C
h
i
-
Sq
u
ar
ed
G
2
(
2
=5
8
9
.
1
4
2
,
d
f
=2
)
[
2
4
]
as w
ell
as
t
h
e
Ak
ai
k
e
I
n
f
o
r
m
atio
n
C
r
iter
io
n
(
A
I
C
)
[
2
5
]
an
d
B
a
y
esia
n
I
n
f
o
r
m
atio
n
C
r
iter
io
n
(
B
I
C
)
[
2
6
]
h
ad
lo
w
e
r
v
alu
e
i
n
m
u
lt
id
i
m
e
n
s
io
n
al
co
n
s
tr
u
cts
f
o
r
d
iag
n
o
s
in
g
MP
,
as sh
o
w
n
i
n
T
ab
le
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
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&
R
e
s
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c
.
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SS
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Fig
u
r
e
4
.
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A
P
an
d
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O
d
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m
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n
s
io
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u
ltid
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m
o
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el
f
o
r
d
iag
n
o
s
in
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T
h
e
th
ir
d
v
alid
it
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ce
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m
in
ed
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s
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h
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ig
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t
m
ap
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m
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ig
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m
ap
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g
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ic
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d
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en
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ate
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m
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o
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h
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ap
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atc
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ates to
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ap
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ates.
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lies
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t
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lt
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g
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th
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d
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tio
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O
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th
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ep
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h
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e
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n
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ld
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tiv
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ite
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d
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is
tr
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te
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m
ilar
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h
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ig
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l
ev
el
o
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h
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r
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h
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m
ap
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itio
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f
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s
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e
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ar
d
er
th
an
f
o
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th
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M
A
P
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lie
s
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MP
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v
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t
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[
1
1
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2
7
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th
at
th
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tr
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ap
s
a
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th
at
s
k
il
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ad
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r
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ly
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a
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f
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t
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r
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m
ap
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Gen
er
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h
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ill
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ated
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h
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m
s
tar
g
eted
at
h
ig
h
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g
r
ad
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lev
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ls
.
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n
th
i
s
lin
e
o
f
r
ea
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o
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g
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alid
it
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ev
id
e
n
ce
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s
u
p
p
o
r
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th
e
i
n
ter
n
al
s
tr
u
ct
u
r
e
in
t
h
is
s
tu
d
y
is
p
r
o
v
id
ed
as
th
e
ite
m
ca
lib
r
atio
n
s
s
u
p
p
o
r
ted
th
e
d
i
m
e
n
s
io
n
s
f
o
r
d
iag
n
o
s
i
n
g
MP
an
d
ite
m
s
d
esi
g
n
.
T
h
is
ar
g
u
m
en
t
w
a
s
also
s
u
p
p
o
r
ted
th
e
p
r
ev
io
u
s
v
alid
it
y
ev
id
en
ce
f
o
r
th
is
s
tu
d
y
b
ased
o
n
a
d
ig
ital
to
o
l’
s
co
n
te
n
t.
As
i
n
d
icate
d
in
[
28
]
,
th
er
e
is
a
r
elatio
n
s
h
ip
b
et
w
ee
n
a
n
MP
ass
e
s
s
m
e
n
t
to
o
l’
s
co
n
te
n
t
an
d
t
h
e
co
n
s
tr
u
ct
th
a
t
is
in
ten
d
ed
to
m
ea
s
u
r
e,
a
n
d
ite
m
s
ca
n
b
e
i
n
ter
p
r
et
ed
as
th
e
as
s
es
s
m
en
t
to
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l
i
s
v
al
id
to
u
s
e
[
15
]
,
w
h
ic
h
h
as
b
ee
n
ex
p
lo
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th
r
o
u
g
h
th
e
W
r
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t
m
ap
o
n
th
e
co
m
m
o
n
s
ca
le,
as
s
h
o
w
n
i
n
Fig
u
r
e
5
.
T
h
e
ite
m
lo
ca
tio
n
s
o
n
t
h
e
r
ig
h
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th
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en
t
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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8822
I
n
t
.
J
.
E
v
al
.
&
R
es
.
E
d
u
c
.
Vo
l.
9
,
No
.
3
,
Sep
tem
b
er
2020
:
6
6
5
-
674
670
Fig
u
r
e
5
.
W
r
ig
h
t
m
ap
o
f
u
n
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i
m
en
s
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n
a
l a
n
d
m
u
lt
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i
m
e
n
s
io
n
al
m
o
d
els
f
o
r
d
iag
n
o
s
in
g
MP
3
.
2
.
Relia
bil
it
y
ev
idence
R
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ch
er
s
s
tar
ted
to
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n
al
y
ze
th
e
r
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y
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e
f
f
ic
ien
t
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s
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I
te
m
R
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o
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T
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ti
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n
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E
x
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ted
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P
o
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te
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an
d
Sep
ar
atio
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E
A
P
/PV)
v
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e
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s
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o
w
n
in
Fi
g
u
r
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6
.
T
h
e
E
A
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V
r
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ies
o
f
M
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n
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er
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4
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d
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esp
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le
cr
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8
5
w
as a
l
s
o
ac
ce
p
tab
le
[
1
6
]
.
B
esid
es,
r
eliab
ilit
y
ev
id
en
ce
o
f
M
A
P
an
d
S
L
O
’
s
s
ta
n
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ar
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er
r
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r
o
f
m
ea
s
u
r
e
m
e
n
t
(
SEM
θ
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h
o
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ed
th
at
SEM
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θ
M
A
P
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a
n
d
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ar
e
r
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g
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r
o
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d
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h
is
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m
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ies
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t
h
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alu
e
s
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o
r
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o
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i
m
en
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io
n
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ac
ce
p
tab
le
w
it
h
a
s
m
al
l
er
r
o
r
f
o
r
esti
m
ati
n
g
MP
,
p
ar
ticu
lar
l
y
f
o
r
in
ter
m
ed
iate
to
th
e
h
i
g
h
le
v
el
o
f
MP
.
T
h
is
is
b
ec
au
s
e
b
o
th
SEM
v
a
lu
e
s
h
ad
th
e
lo
w
est
er
r
o
r
if
t
h
e
s
t
u
d
en
t
ab
ilit
y
(
θ)
w
er
e
in
th
e
r
an
g
e
f
r
o
m
0
.
0
to
0
.
5
lo
g
its
.
Ho
w
ev
e
r
,
th
e
er
r
o
r
s
s
ee
m
ed
to
in
cr
ea
s
e
w
h
e
n
esti
m
at
in
g
th
e
lo
w
lev
e
l
o
f
MP
.
T
h
e
r
eliab
ilit
y
ev
id
e
n
ce
s
u
g
g
e
s
ted
th
at
th
e
d
ig
ital
d
iag
n
o
s
tic
to
o
l
h
as
h
i
g
h
p
r
ec
is
io
n
,
s
tab
ilit
y
,
a
n
d
co
n
s
is
te
n
c
y
to
d
iag
n
o
s
e
MP
i
n
ea
ch
d
i
m
e
n
s
io
n
.
He
n
ce
,
th
is
to
o
l
is
f
o
u
n
d
ap
p
r
o
p
r
iate
f
o
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
v
al
&
R
e
s
E
d
u
c
.
I
SS
N:
2252
-
8822
V
a
lid
a
tio
n
o
f a
d
i
g
ita
l to
o
l fo
r
d
ia
g
n
o
s
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th
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Ju
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671
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tu
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t
in
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iate
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o
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el
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o
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e
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t
h
e
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el.
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h
is
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ec
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s
e
t
h
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est
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lev
el
o
f
s
t
u
d
en
t
s
s
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o
w
ed
th
e
h
ig
h
e
s
t
er
r
o
r
o
f
SEM
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alu
e.
F
ig
u
r
e
6
s
h
o
w
s
t
h
e
r
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lt
s
o
f
th
e
s
tan
d
ar
d
er
r
o
r
o
f
m
ea
s
u
r
e
m
e
n
t
f
o
r
b
o
th
MA
P
a
n
d
SLO
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i
m
e
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s
io
n
s
.
(a
)
SEM
(
MA
P
)
(
b
)
SEM
(
SL
O
)
Fig
u
r
e
6
.
Stan
d
ar
d
er
r
o
r
o
f
m
e
asu
r
e
m
en
t o
f
M
A
P
an
d
SLO
d
i
m
e
n
s
io
n
s
3
.
3
.
I
t
e
m
f
it
A
ll
t
h
e
1
8
item
d
if
f
icu
lties
i
n
th
e
MP
d
ig
ital
d
iag
n
o
s
tic
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l
co
n
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is
ts
o
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I
te
m
1
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1
1
o
f
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P
ite
m
s
an
d
I
te
m
1
2
to
1
8
o
f
SL
O
ite
m
s
w
er
e
e
x
a
m
in
ed
as
el
u
cid
at
ed
in
T
ab
le
2
.
Fo
r
th
e
MA
P
d
i
m
e
n
s
io
n
,
th
e
ite
m
d
if
f
ic
u
lt
ies
r
an
g
ed
f
r
o
m
–
1
.
1
6
lo
g
its
(
SE=
0
.
0
6
)
to
+
1
.
2
7
l
o
g
its
(
SE=
0
.
0
7
)
.
Ho
w
e
v
er
,
f
i
v
e
o
u
t
o
f
th
e
ite
m
s
ar
e
p
o
ly
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m
o
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s
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co
r
ed
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d
th
e
d
if
f
ic
u
lt
y
s
h
o
u
ld
b
e
f
o
cu
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ed
o
n
th
e
ite
m
s
co
r
e
th
r
esh
o
ld
.
I
n
th
i
s
ca
s
e,
th
e
r
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g
e
o
f
ite
m
d
if
f
ic
u
lt
y
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n
M
A
P
r
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g
e
d
f
r
o
m
-
1
.
1
6
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+
1
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8
0
,
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d
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e
s
t
u
d
en
t
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ea
s
u
r
es
r
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g
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f
r
o
m
–
2
.
3
1
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g
its
to
+2
.
4
7
lo
g
its
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h
er
e
ar
e
s
o
m
e
s
tu
d
en
t
s
(
1
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5
0
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w
h
o
s
e
m
ath
e
m
atics
ab
ilit
ie
s
ar
e
m
o
r
e
th
an
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8
0
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g
its
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8
0
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d
less
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h
an
–
1
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1
6
l
o
g
its
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1
2
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0
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an
d
h
e
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ce
n
o
t
'm
atc
h
ed
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ag
a
in
s
t
a
n
ite
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lo
c
atio
n
o
n
th
e
s
ca
le.
I
n
Fig
u
r
e
6
(
b
)
,
th
er
e
a
r
e
n
o
ite
m
s
m
atc
h
i
n
g
s
tu
d
e
n
ts
at
eith
er
th
e
lo
w
e
s
t
en
d
(
-
1
.
1
7
to
–
2
.
3
1
lo
g
its
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o
r
th
e
h
i
g
h
est
e
n
d
(
+1
.
2
8
to
+2
.
4
7
lo
g
its
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o
f
th
e
s
ca
le,
i
n
d
ica
tin
g
s
o
m
e
i
m
p
r
o
v
e
m
en
ts
ar
e
n
ee
d
ed
f
o
r
th
e
test
.
T
h
at
is
,
b
o
th
ea
s
y
an
d
h
ar
d
ite
m
s
n
e
ed
to
b
e
ad
d
e
d
to
im
p
r
o
v
e
th
e
tar
g
eti
n
g
o
f
t
h
e
ite
m
s
f
o
r
d
iag
n
o
s
i
n
g
MP
,
p
ar
ticu
lar
l
y
ea
s
y
ite
m
s
.
T
h
er
e
ar
e
ap
p
r
o
x
i
m
atel
y
1
9
1
s
tu
d
en
t
s
w
h
o
f
o
u
n
d
th
ese
tes
t
ite
m
s
ea
s
y
a
n
d
ap
p
r
o
x
im
a
tel
y
5
7
o
f
th
e
m
w
h
o
f
o
u
n
d
th
e
ite
m
s
w
er
e
h
ar
d
.
T
h
e
ite
m
d
i
f
f
ic
u
lt
ies
w
er
e
ap
p
r
o
p
r
iate
f
o
r
th
e
r
est
o
f
th
e
s
tu
d
e
n
ts
,
ap
p
r
o
x
i
m
atel
y
1
,
2
5
6
s
tu
d
en
ts
.
T
h
e
ev
id
en
ce
s
u
p
p
o
r
ts
th
e
v
alid
it
y
ar
g
u
m
e
n
t
b
ased
o
n
in
ter
n
al
s
tr
u
ct
u
r
e,
as s
h
o
w
n
i
n
Fi
g
u
r
e
5
.
On
th
e
o
th
er
h
an
d
,
f
o
r
th
e
SL
O
d
i
m
e
n
s
io
n
,
th
e
ite
m
d
if
f
i
cu
ltie
s
w
er
e
r
an
g
ed
f
r
o
m
–
2
.
2
7
lo
g
it
s
(
SE=
0
.
0
5
)
t
o
+
1
.
9
0
lo
g
its
(
S
E
=0
.
0
5
)
,
an
d
th
e
s
t
u
d
en
t
ab
ili
t
y
w
as
r
a
n
g
ed
f
r
o
m
–
2
.
5
2
lo
g
its
to
+2
.
4
8
lo
g
its
.
R
es
u
lts
r
e
v
ea
led
th
at
t
h
er
e
ar
e
s
o
m
e
s
t
u
d
en
t
s
(
5
.
9
0
%)
w
h
o
s
e
MP
is
m
o
r
e
th
an
+1
.
9
0
lo
g
it
s
(
5
.
6
0
%)
an
d
less
th
an
–
-
2
.
2
7
lo
g
its
(
0
.
3
0
%)
an
d
h
en
ce
n
o
t
'm
atc
h
ed
'
a
g
ai
n
s
t
an
ite
m
d
i
s
tr
ib
u
tio
n
o
n
t
h
e
s
c
ale.
I
n
Fig
u
r
e
6
(
b
)
,
th
er
e
ar
e
n
o
ite
m
s
m
atc
h
i
n
g
s
t
u
d
en
t
s
at
eith
er
t
h
e
lo
w
e
s
t
en
d
(
-
2
.
2
7
t
o
-
2
.
5
2
lo
g
its
)
o
r
th
e
h
ig
h
est
e
n
d
(
+1
.
9
1
to
+2
.
4
8
lo
g
its
)
o
f
th
e
s
ca
le,
in
d
icatin
g
s
o
m
e
i
m
p
r
o
v
e
m
en
ts
ar
e
n
ee
d
ed
f
o
r
th
e
test
.
Nev
er
th
e
less
,
th
e
p
er
ce
n
tag
e
o
f
ite
m
d
if
f
ic
u
ltie
s
w
er
e
q
u
ite
s
m
al
l,
esp
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iall
y
ea
s
y
ite
m
s
.
T
h
e
ea
s
y
ite
m
d
i
f
f
ic
u
lties
w
er
e
in
ap
p
r
o
p
r
iate
f
o
r
m
atc
h
i
n
g
to
o
n
l
y
f
i
v
e
s
t
u
d
en
t
s
.
T
h
is
m
ea
n
s
th
at
th
e
ite
m
d
if
f
ic
u
lt
y
i
s
ad
eq
u
ate
f
o
r
th
e
d
ig
ital
d
iag
n
o
s
t
ic
to
o
l.
Ho
w
e
v
er
,
w
h
en
r
esear
ch
er
s
co
n
s
id
er
ed
th
e
ite
m
s
co
r
e
t
h
r
esh
o
ld
as
s
h
o
w
n
in
T
ab
le
2
,
I
tem
16
s
ee
m
ed
to
b
e
a
v
er
y
h
ar
d
ite
m
.
T
h
e
s
tu
d
en
t
h
ad
a
5
0
p
e
r
ce
n
t
ch
an
ce
o
f
o
b
tain
in
g
a
s
co
r
e
o
f
3
lo
w
er
th
a
n
th
e
o
th
er
ite
m
s
at
th
e
s
a
m
e
s
co
r
e.
I
n
ad
d
itio
n
,
th
i
s
ite
m
w
a
s
th
e
h
ar
d
est
ite
m
.
I
t
s
h
o
u
ld
b
e
re
-
o
r
ien
ted
f
o
r
d
iag
n
o
s
i
n
g
MP
in
th
e
f
u
tu
r
e.
T
ab
le
2
s
h
o
w
s
t
h
e
r
es
u
lt
s
o
f
th
e
ite
m
f
it st
at
is
tic
a
n
al
y
s
i
s
.
T
h
e
r
esu
lts
o
f
t
h
e
d
ig
ita
l
to
o
l
in
s
p
ec
tio
n
ar
e
f
o
u
n
d
i
n
ac
co
r
d
an
ce
w
i
th
ac
ce
p
tab
le
cr
iter
i
a.
T
h
e
test
s
tat
is
t
ics
co
n
s
i
s
t
o
f
Un
w
e
ig
h
t
ed
Fit
MN
SQ
(
o
u
tf
it)
b
et
w
ee
n
0
.
7
6
-
1
.
2
6
an
d
W
eig
h
t
Fit
MN
SQ
(
in
f
it)
b
et
w
ee
n
0
.
8
2
-
1
.
1
0
w
h
ich
w
er
e
w
it
h
i
n
t
h
e
ac
ce
p
tab
le
r
an
g
e
t
h
at
is
b
et
w
ee
n
0
.
7
5
an
d
1
.
3
3
[
28
-
30
]
as
s
h
o
w
n
i
n
T
ab
le
1
.
T
h
er
ef
o
r
e,
all
1
8
task
s
ar
e
f
o
u
n
d
in
co
m
p
lia
n
ce
w
it
h
t
h
e
f
it.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8822
I
n
t
.
J
.
E
v
al
.
&
R
es
.
E
d
u
c
.
Vo
l.
9
,
No
.
3
,
Sep
tem
b
er
2020
:
6
6
5
-
674
672
T
ab
le
2
.
R
esu
lts
o
f
ite
m
f
it
s
ta
tis
tic
a
n
al
y
s
is
I
t
e
m
D
i
f
f
i
c
u
l
t
y
SE
O
u
t
f
i
t
I
n
f
i
t
I
t
e
m Sco
r
e
T
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Stu
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lev
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w
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Stu
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Stu
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l
y
p
r
o
v
id
ed
f
o
r
m
ati
v
e
f
ee
d
b
ac
k
f
o
r
b
o
th
teac
h
er
s
an
d
s
tu
d
en
ts
to
en
h
an
ce
th
eir
MP
s
o
th
e
y
ca
n
k
n
o
w
w
h
at
s
t
u
d
en
t
s
lear
n
,
w
h
at
s
t
u
d
en
t
s
n
ee
d
to
lear
n
,
an
d
h
o
w
w
ell
th
e
y
u
n
d
er
s
tan
d
it.
As
a
r
e
s
u
l
t,
I
T
A
R
ca
n
b
e
u
t
ilized
to
g
u
id
e
th
e
lear
n
i
n
g
a
n
d
in
s
tr
u
ctio
n
b
ased
o
n
m
u
l
tip
le
p
r
o
f
icien
cies.
A
lt
h
o
u
g
h
I
T
A
R
ca
n
b
e
ac
ce
s
s
ed
f
r
ee
l
y
as
w
e
b
-
b
ased
r
eso
u
r
ce
s
an
d
co
m
p
atib
ilit
y
w
it
h
m
u
lti
p
le
s
eq
u
en
ce
s
an
d
ap
p
r
o
ac
h
e
s
,
th
e
r
es
u
lt
s
r
ev
ea
led
t
h
at
t
h
e
p
r
o
ce
s
s
s
y
s
te
m
co
m
p
o
n
e
n
t
s
t
ill
h
as
p
r
o
b
le
m
s
esti
m
ati
n
g
s
t
u
d
en
t
s
’
MP
b
ec
a
u
s
e
o
f
th
e
co
m
p
le
x
it
y
o
f
th
e
alg
o
r
ith
m
w
h
ich
is
a
p
s
y
ch
o
m
etr
ic
m
o
d
el.
Fu
t
u
r
e
r
esear
ch
er
s
s
h
o
u
ld
b
e
co
n
ce
r
n
ed
ab
o
u
t
th
e
n
e
g
ati
v
e
a
n
d
p
o
s
iti
v
e
co
n
s
eq
u
en
ce
s
o
f
u
s
in
g
I
T
A
R
i
n
th
e
ac
t
u
a
l
m
at
h
e
m
a
tics
cla
s
s
r
o
o
m
s
.
T
h
e
f
o
llo
w
i
n
g
co
n
s
eq
u
en
ce
s
o
f
u
s
in
g
a
d
ig
ita
l
d
iag
n
o
s
t
ic
to
o
l
s
h
o
u
ld
b
e
tak
e
n
i
n
to
co
n
s
id
er
atio
n
,
f
o
r
ex
a
m
p
le,
(
i)
h
o
w
s
t
u
d
en
ts
i
m
p
r
o
v
e
th
e
MP
w
h
e
n
u
s
i
n
g
I
T
A
R
;
(
ii)
h
o
w
m
u
c
h
is
t
h
e
s
tu
d
e
n
ts
’
g
r
o
w
t
h
r
ate
b
et
w
ee
n
b
ef
o
r
e
an
d
af
ter
u
s
in
g
I
T
A
R
;
(
iii)
h
o
w
to
u
s
e
I
T
A
R
s
i
m
p
l
y
f
o
r
b
o
th
s
tu
d
en
t
s
an
d
teac
h
er
s
,
an
d
(
iv
)
h
o
w
t
o
in
teg
r
ate
cu
r
r
ic
u
lu
m
,
i
n
s
tr
u
c
tio
n
,
an
d
d
iag
n
o
s
tic
th
r
o
u
g
h
I
T
AR
a
n
d
li
n
k
th
e
s
t
u
d
en
t
s
’
p
r
o
g
r
ess
io
n
lear
n
i
n
g
w
h
e
n
ev
er
t
h
e
y
u
p
g
r
ad
e
in
th
e
h
i
g
h
er
le
v
el
as
w
el
l
as in
a
co
h
o
r
t st
u
d
y
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
r
esear
ch
an
d
d
ev
elo
p
m
e
n
t
ac
ti
v
itie
s
th
at
d
i
s
cu
s
s
ed
in
th
is
ar
ticle
w
er
e
s
u
p
p
o
r
ted
b
y
th
e
g
r
an
t
f
r
o
m
T
h
aila
n
d
R
e
s
ea
r
ch
Fu
n
d
(
T
R
F)
A
d
v
a
n
ce
d
R
e
s
ea
r
ch
Sch
o
lar
a
n
d
Kh
o
n
Kae
n
U
n
iv
er
s
it
y
,
T
h
ailan
d
(
Gr
an
t
No
:
R
S
A
6
0
8
0
0
7
4
)
.
T
h
e
r
esear
ch
er
s
w
o
u
ld
li
k
e
to
th
a
n
k
t
h
e
C
e
n
ter
f
o
r
R
e
s
ea
r
ch
i
n
Ma
t
h
e
m
atics
E
d
u
ca
tio
n
(
C
R
ME
)
,
Kh
o
n
Ka
en
U
n
iv
er
s
it
y
f
o
r
p
r
o
v
id
in
g
t
h
e
s
u
p
p
o
r
ts
to
m
a
k
e
t
h
e
r
esear
c
h
a
s
u
cc
es
s
.
Sp
ec
ial
th
an
k
s
to
Facu
lt
y
o
f
E
d
u
ca
t
io
n
,
Kh
o
n
Kae
n
U
n
i
v
er
s
it
y
,
T
h
ailan
d
f
o
r
p
r
o
v
id
in
g
f
i
n
a
n
cial
s
u
p
p
o
r
t
to
p
r
esen
t
o
u
r
r
e
s
ea
r
ch
r
esu
lt
s
at
t
h
e
3
r
d
W
o
r
ld
C
o
n
f
er
en
ce
o
n
E
d
u
ca
ti
o
n
2
0
1
9
(
W
C
E
DU)
at
Ku
alar
L
u
m
p
u
r
,
Ma
la
y
s
ia.
RE
F
E
R
E
NC
E
S
[1
]
P
.
Ju
n
p
e
n
g
,
M
.
I
n
p
ra
sit
h
a
a
n
d
M
.
W
il
so
n
,
“
M
o
d
e
li
n
g
o
f
th
e
o
p
e
n
-
e
n
d
e
d
it
e
m
s
f
o
r
a
ss
e
ss
in
g
m
u
lt
ip
le
p
ro
f
icie
n
c
ies
in
m
a
th
e
m
a
ti
c
a
l
p
ro
b
lem
so
lv
in
g
,
”
T
h
e
T
u
rk
ish
O
n
li
n
e
J
o
u
r
n
a
l
o
f
Ed
u
c
a
ti
o
n
a
l
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
2
,
S
p
e
c
ial
Iss
u
e
f
o
r
INT
E
-
I
T
IC
A
M
-
IDEC,
p
p
.
1
4
2
-
1
4
9
,
2
0
1
8
.
[2
]
J.
M
e
n
sa
h
a
n
d
D.
Da
k
e
,
“
T
e
st,
m
e
a
su
re
m
e
n
t,
a
n
d
e
v
a
lu
a
ti
o
n
:
u
n
d
e
rsta
n
d
in
g
a
n
d
u
se
o
f
th
e
c
o
n
c
e
p
ts
in
e
d
u
c
a
ti
o
n
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Ev
a
lu
a
ti
o
n
a
n
d
Res
e
a
rc
h
i
n
E
d
u
c
a
ti
o
n
(
IJ
ER
E)
,
v
o
l.
9
,
n
o
.
1
,
p
p
.
1
0
9
-
1
1
9
,
2
0
2
0
.
[3
]
U.
Zain
iy
a
h
a
n
d
M
a
rsig
it
,
“
I
m
p
ro
v
in
g
m
a
th
e
m
a
ti
c
a
l
li
tera
c
y
o
f
p
ro
b
lem
so
lv
in
g
a
t
th
e
5
th
g
ra
d
e
o
f
p
ri
m
a
r
y
stu
d
e
n
ts
,”
J
o
u
rn
a
l
o
f
E
d
u
c
a
ti
o
n
a
n
d
L
e
a
rn
i
n
g
(
Ed
u
L
e
a
rn
)
,
v
o
l.
1
3
,
n
o
.
1
,
p
p
.
9
8
-
1
0
3
,
2
0
1
7
.
[4
]
Y
-
M
.
Hu
a
n
g
,
S
-
H.
H
u
a
n
g
a
n
d
T
-
T
.
W
u
,
“
E
m
b
e
d
d
in
g
d
iag
n
o
st
ic
m
e
c
h
a
n
is
m
s
in
a
d
ig
it
a
l
g
a
m
e
f
o
r
lea
rn
in
g
m
a
th
e
m
a
ti
c
s
,
”
Ed
u
c
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
Res
e
a
rc
h
a
n
d
De
v
e
lo
p
me
n
t
,
v
o
l.
6
2
,
n
o
.
2
,
p
p
.
1
8
7
-
2
0
7
,
2
0
1
4
.
[5
]
B.
M
.
N.
B.
Ba
k
a
r,
“
T
h
e
p
ro
c
e
ss
o
f
th
in
k
in
g
a
m
o
n
g
ju
n
io
r
h
ig
h
sc
h
o
o
l
stu
d
e
n
ts
in
so
lv
in
g
H
OT
S
q
u
e
stio
n
,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Ev
a
lu
a
ti
o
n
a
n
d
Res
e
a
rc
h
i
n
E
d
u
c
a
ti
o
n
(
IJ
ER
E)
,
v
o
l.
4
,
n
o
.
3
,
p
p
.
1
3
8
-
1
4
5
,
2
0
1
5
.
[6
]
M
.
M
a
rw
ian
g
,
P
.
Ju
n
p
e
n
g
,
a
n
d
N.
Na
k
o
rn
,
“
T
h
e
d
e
v
e
lo
p
m
e
n
t
o
f
a
m
o
d
e
l
f
o
r
m
a
th
e
m
a
ti
c
s
c
las
sro
o
m
a
ss
e
ss
m
e
n
t
:
Co
ll
a
b
o
ra
ti
v
e
a
ss
e
ss
m
e
n
t
p
y
ra
m
i
d
,
”
Pro
c
e
d
i
a
S
o
c
ia
l
a
n
d
Beh
a
v
i
o
r
a
l
S
c
ien
c
e
s,
v
o
l
.
1
4
3
,
p
p
.
7
6
4
-
7
6
8
,
2
0
1
4
.
[7
]
P
.
Ju
n
p
e
n
g
,
“
T
h
e
d
e
v
e
lo
p
m
e
n
t
o
f
c
las
sro
o
m
a
ss
e
s
s
m
e
n
t
s
y
ste
m
i
n
m
a
th
e
m
a
ti
c
s
f
o
r
b
a
sic
e
d
u
c
a
ti
o
n
o
f
T
h
a
il
a
n
d
,
”
Pro
c
e
d
ia
S
o
c
i
a
l
a
n
d
Beh
a
v
io
ra
l
S
c
ien
c
e
s
,
v
o
l.
6
9
,
p
p
.
1
9
6
5
–
1
9
7
2
,
2
0
1
2
.
[8
]
D.
F
o
u
ry
z
a
,
S
.
M
.
Am
in
,
a
n
d
R.
Ek
a
w
a
ti
,
“
De
sig
n
in
g
les
so
n
p
lan
o
f
in
teg
e
r
n
u
m
b
e
r
o
p
e
ra
ti
o
n
b
a
se
d
o
n
f
u
n
a
n
d
e
a
sy
m
a
th
(F
EM
)
a
p
p
r
o
a
c
h
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Eva
l
u
a
t
io
n
a
n
d
Res
e
a
rc
h
i
n
Ed
u
c
a
ti
o
n
(
IJ
ER
E)
,
v
o
l.
8
,
n
o
.
1
,
p
p
.
1
0
3
-
1
0
9
,
2
0
1
9
.
[9
]
C.
Re
d
e
c
k
e
r
a
n
d
O.
Jo
h
a
n
n
e
ss
e
n
,
“
Ch
a
n
g
i
n
g
a
ss
e
ss
m
e
n
t
–
t
o
w
a
rd
s
a
n
e
w
a
ss
e
ss
m
e
n
t
p
a
ra
d
ig
m
u
sin
g
ICT
,
”
Eu
ro
p
e
a
n
J
o
u
rn
a
l
o
f
E
d
u
c
a
ti
o
n
,
v
o
l.
4
8
,
n
o
.
1
,
p
p
.
7
9
-
9
6
,
2
0
1
4
.
[1
0
]
P
.
P
a
tel
a
n
d
A
.
T
h
a
k
k
a
r
,
“
T
h
e
u
p
su
rg
e
o
f
d
e
e
p
lea
rn
in
g
f
o
r
c
o
m
p
u
ter
v
isio
n
a
p
p
li
c
a
ti
o
n
s
,
”
I
n
ter
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
1
,
p
p
.
5
3
8
-
5
4
8
,
2
0
2
0
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
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I
n
t
.
J
.
E
v
al
.
&
R
es
.
E
d
u
c
.
Vo
l.
9
,
No
.
3
,
Sep
tem
b
er
2020
:
6
6
5
-
674
674
[1
1
]
P
.
Ju
n
p
e
n
g
,
e
t
a
l.
,
“
Co
n
stru
c
ti
n
g
p
r
o
g
re
ss
m
a
p
s
o
f
d
ig
it
a
l
tec
h
n
o
lo
g
y
f
o
r
d
iag
n
o
si
n
g
m
a
th
e
m
a
ti
c
a
l
p
r
o
f
icie
n
c
y
,”
J
o
u
rn
a
l
o
f
E
d
u
c
a
t
io
n
a
n
d
L
e
a
rn
i
n
g
,
v
o
l.
8
,
no.
6
,
p
p
.
9
0
-
1
0
2
,
2
0
1
9
.
[1
2
]
M
.
M
a
rw
ian
g
,
J.
Kla
h
a
r
n
,
L
.
S
a
re
e
,
a
n
d
P
.
J
u
n
p
e
n
g
,
“
A
ss
e
ss
in
g
stu
d
e
n
ts’
m
a
th
e
m
a
ti
c
a
l
p
ro
b
le
m
so
lv
in
g
sk
il
l
th
ro
u
g
h
t
h
e
in
n
o
v
a
ti
v
e
les
so
n
st
u
d
y
a
n
d
o
p
e
n
a
p
p
ro
a
c
h
,”
T
u
rk
i
sh
On
li
n
e
J
o
u
r
n
a
l
o
f
Ed
u
c
a
ti
o
n
a
l
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
1
6,
S
p
e
c
ial
Iss
u
e
,
p
p
.
3
8
5
-
3
9
5
,
2
0
1
7
.
[1
3
]
M
.
M
a
rw
ian
g
,
e
t
a
l.
,
“
A
ss
e
s
s
m
e
n
t
o
f
lea
rn
in
g
p
r
o
g
re
ss
io
n
o
n
m
a
th
e
m
a
ti
c
a
l
p
ro
b
lem
so
lv
in
g
o
f
stu
d
e
n
ts
u
sin
g
o
p
e
n
a
p
p
ro
a
c
h
,
”
J
o
u
rn
a
l
o
f
P
h
y
sic
s: Co
n
fer
e
n
c
e
S
e
rie
s
,
v
o
l
.
1
3
4
0
,
n
o
.
1
,
p
p
1
-
7
,
2
0
1
9
[1
4
]
J.
B.
Brig
g
s
a
n
d
K.
C
o
ll
is,
Eva
lu
a
ti
n
g
th
e
q
u
a
li
ty
o
f
le
a
rn
i
n
g
:
T
h
e
S
OLO
ta
x
o
n
o
my
,
Ne
w
Yo
rk
:
A
c
a
d
e
m
ic
P
re
ss
,
1
9
8
2
.
[1
5
]
M
.
R.
W
il
so
n
,
Co
n
stru
c
ti
n
g
M
e
a
su
re
s:
An
Item
Res
p
o
n
se
M
o
d
e
li
n
g
Ap
p
ro
a
c
h
,
M
a
h
w
a
h
,
NJ
:
L
a
wre
n
c
e
Erl
b
a
u
m
A
s
so
c
.
,
2
0
0
5
.
[1
6
]
R.
J.
A
d
a
m
s,
“
Re
li
a
b
il
it
y
a
s
a
m
e
a
su
re
m
e
n
t
d
e
sig
n
e
f
f
e
c
t
,
”
S
tu
d
ies
In
Ed
u
c
a
ti
o
n
a
l
Ev
a
lu
a
ti
o
n
,
v
o
l.
3
1
,
n
o
.
2
-
3
,
p
p
.
1
6
2
-
1
7
2
,
2
0
0
5
.
[1
7
]
R.
J.
A
d
a
m
s,
M
.
R.
W
il
so
n
a
n
d
W
.
Wan
g
,
“
T
h
e
m
u
lt
id
im
e
n
sio
n
a
l
ra
n
d
o
m
c
o
e
ff
icie
n
t
m
u
lt
in
o
m
ial
lo
g
it
m
o
d
e
l
,
”
Ap
p
li
e
d
Psy
c
h
o
lo
g
ica
l
M
e
a
su
re
m
e
n
t,
v
o
l.
2
1
,
n
o
.
1
,
p
p
.
1
-
2
3
,
1
9
9
7
.
[1
8
]
T
h
a
il
a
n
d
M
i
n
istry
o
f
Ed
u
c
a
ti
o
n
,
L
e
a
rn
i
n
g
S
t
a
n
d
a
r
d
s
a
n
d
In
d
ica
to
rs
L
e
a
rn
in
g
o
f
M
a
t
h
e
ma
ti
c
s
(
re
v
ise
d
e
d
it
io
n
2
0
1
7
)
a
c
c
o
r
d
in
g
to
t
h
e
Co
re
Cu
rr
icu
lu
m
o
f
Ba
sic
E
d
u
c
a
t
io
n
,
B.
E.
2
5
5
1
.
Ba
n
g
k
o
k
:
P
ri
n
t
in
g
Ho
u
se
,
A
g
ricu
lt
u
ra
l
Co
o
p
e
ra
ti
v
e
o
f
T
h
a
il
a
n
d
,
2
0
1
7
.
[1
9
]
M
.
Cu
ste
r,
“
S
a
m
p
le
siz
e
a
n
d
it
e
m
p
a
ra
m
e
ter
e
sti
m
a
ti
o
n
p
re
c
isio
n
w
h
e
n
u
ti
li
z
in
g
th
e
o
n
e
-
p
a
ra
m
e
t
e
r
‘ra
sc
h
’
m
o
d
e
l
,
”
T
h
e
a
n
n
u
a
l
me
e
ti
n
g
o
f
th
e
mid
-
we
ste
rn
Ed
u
c
a
ti
o
n
a
l
Res
e
a
rc
h
Asso
c
ia
ti
o
n
,
Ev
a
n
st
o
n
,
Ill
i
n
o
is,
2
1
-
2
4
Oc
to
b
e
r,
2
0
1
5
.
[2
0
]
W
.
F
.
W
.
Ya
a
c
o
b
,
S
.
A
.
M
.
Na
sir,
W
.
F
.
W
.
Ya
a
c
o
b
,
a
n
d
N.
M
.
S
o
b
ri
,
“
S
u
p
e
rv
ise
d
d
a
ta
m
in
in
g
a
p
p
ro
a
c
h
f
o
r
p
re
d
ictin
g
st
u
d
e
n
t
p
e
rf
o
rm
a
n
c
e
,
”
In
d
o
n
e
si
a
n
J
o
u
rn
a
l
o
f
E
lec
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
(
IJ
EE
CS
)
,
v
o
l.
1
6
,
n
o
.
3
,
p
p
.
1
5
4
8
-
1
5
9
2
,
2
0
1
9
.
[2
1
]
M
.
Ku
m
a
r
a
n
d
A
.
J.
S
in
g
h
,
“
Ev
a
lu
a
ti
o
n
o
f
d
a
ta
m
in
in
g
tec
h
n
i
q
u
e
s
f
o
r
p
re
d
ictin
g
stu
d
e
n
t’s
p
e
rf
o
r
m
a
n
c
e
,
”
M
o
d
e
rn
Ed
u
c
a
ti
o
n
a
n
d
Co
mp
u
ter
S
c
ien
c
e
,
v
o
l.
9
,
n
o
.
8
,
p
p
.
2
5
-
3
1
,
2
0
1
7
.
[2
2
]
K.
E.
M
e
rra
o
u
i,
A
.
F
e
rd
j
o
u
n
i,
a
n
d
M
.
Bo
u
n
e
k
h
la,
“
Re
a
l
tim
e
o
b
se
rv
e
r
-
b
a
se
d
sta
to
r
f
a
u
lt
d
ia
g
n
o
sis
f
o
r
IM
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
1
,
p
p
.
2
1
0
-
2
2
2
,
2
0
20
.
[2
3
]
S
.
Jia
n
g
,
C.
W
a
n
g
a
n
d
D.
J.
W
e
iss,
“
S
a
m
p
le
siz
e
re
q
u
ire
m
e
n
ts
f
o
r
e
stim
a
ti
o
n
o
f
it
e
m
p
a
r
a
m
e
ters
in
th
e
m
u
lt
id
im
e
n
sio
n
a
l
g
ra
d
e
d
re
sp
o
n
s
e
m
o
d
e
l
,
”
Fro
n
ti
e
rs
in
Psy
c
h
o
l
o
g
y
,
v
o
l.
7
,
A
rti
c
le
1
0
9
,
2
0
1
6
.
[2
4
]
M
.
R.
W
il
so
n
a
n
d
P
.
De
Bo
e
c
k
,
“
De
sc
rip
ti
v
e
a
n
d
e
x
p
lan
a
to
ry
it
e
m
re
sp
o
n
se
m
o
d
e
ls
,
”
In
P
.
De
Bo
e
c
k
&
M
.
W
il
so
n
(Ed
s.),
Ex
p
la
n
a
t
o
ry
Item
M
o
d
e
ls:
A
Ge
n
e
ra
li
ze
d
L
in
e
a
r
a
n
d
N
o
n
l
in
e
a
r
.
Ne
w
Yo
rk
:
S
p
rin
g
e
r
-
V
e
rlag
,
2
0
0
4
.
[2
5
]
L
.
Ya
o
a
n
d
R.
D.
S
c
h
w
a
rz
,
“
A
m
u
lt
id
im
e
n
sio
n
a
l
p
a
rti
a
l
c
re
d
it
m
o
d
e
l
w
it
h
a
ss
o
c
i
a
ted
it
e
m
a
n
d
t
e
st
sta
ti
stics
:
A
n
a
p
p
li
c
a
ti
o
n
t
o
m
ix
e
d
-
f
o
rm
a
t
tes
t
s,”
Ap
p
li
e
d
Psy
c
h
o
l
o
g
ica
l
M
e
a
su
re
me
n
t
,
v
o
l.
3
0
,
n
o
.
6
,
p
p
.
4
6
9
-
4
9
2
,
2
0
0
6
.
[2
6
]
G
.
S
c
h
wa
rz
,
“
Esti
m
a
ti
n
g
th
e
d
ime
n
sio
n
o
f
a
m
o
d
e
l
,
”
T
h
e
An
n
a
ls
o
f
S
ta
ti
stics
,
v
o
l.
6
,
n
o
.
2
,
p
p
.
4
6
1
-
4
6
4
,
1
9
7
8
.
[2
7
]
B.
Du
c
k
o
r,
K.
E
.
Ca
ste
ll
a
n
o
,
K.
T
ĕ
ll
e
z
,
D.
W
ih
a
rd
in
i
a
n
d
M
.
R.
W
il
so
n
,
“
Ex
a
m
in
in
g
th
e
i
n
tern
a
l
str
u
c
tu
re
e
v
id
e
n
c
e
f
o
r
th
e
p
e
rf
o
rm
a
n
c
e
a
ss
e
ss
m
e
n
t
fo
r
Ca
li
f
o
rn
ia
tea
c
h
e
rs:
A
v
a
li
d
a
ti
o
n
stu
d
y
o
f
th
e
e
lem
e
n
tar
y
li
tera
c
y
tea
c
h
in
g
e
v
e
n
t
f
o
r
ti
e
r
1
tea
c
h
e
r
li
c
e
n
su
re
,
”
J
o
u
r
n
a
l
o
f
T
e
a
c
h
e
r E
d
u
c
a
ti
o
n
,
v
o
l.
6
5
,
n
o
.
5
,
p
p
.
4
0
2
-
4
2
0
,
2
0
1
4
.
[2
8
]
Am
e
rica
n
Ed
u
c
a
ti
o
n
a
l
Re
se
a
rc
h
A
ss
o
c
iatio
n
,
A
m
e
ric
a
n
P
sy
c
h
o
l
o
g
ica
l
A
ss
o
c
iatio
n
a
n
d
Na
ti
o
n
a
l
Co
u
n
c
il
o
n
M
e
a
su
re
m
e
n
t
in
Ed
u
c
a
ti
o
n
,
S
ta
n
d
a
r
d
s
f
o
r
E
d
u
c
a
ti
o
n
a
l
a
n
d
Ps
y
c
h
o
lo
g
ica
l
T
e
stin
g
(6
th
e
d
.
).
W
a
sh
in
g
to
n
,
DC
:
Am
e
rica
n
E
d
u
c
a
ti
o
n
a
l
Re
se
a
rc
h
A
s
so
c
iatio
n
,
2
0
1
4
.
[2
9
]
R.
J.
A
d
a
m
s
a
n
d
S
.
T
.
Kh
o
o
,
Q
u
e
st:
T
h
e
In
ter
a
c
ti
v
e
T
e
st
A
n
a
lys
is
S
y
ste
m,
M
e
lb
o
u
r
n
e
,
V
ic:
A
u
stra
li
a
n
C
o
u
n
c
il
f
o
r
Ed
u
c
a
ti
o
n
a
l
Re
se
a
rc
h
,
1
9
9
6
.
[3
0
]
Y
-
J.
I.
C
h
e
n
,
M
.
R.
W
il
so
n
,
R.
C.
Ire
y
a
n
d
M
.
K.
Re
q
u
a
,
“
A
n
I
n
n
o
v
a
ti
v
e
m
e
a
su
re
o
f
o
rth
o
g
ra
p
h
ic
p
ro
c
e
ss
in
g
:
De
v
e
lo
p
m
e
n
t
a
n
d
i
n
it
ial
v
a
li
d
a
ti
o
n
,
”
L
a
n
g
u
a
g
e
T
e
sti
n
g
,
v
o
l.
3
7
,
n
o
.
3
,
p
p
.
1
-
1
8
,
2
0
2
0
.
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