Int
ern
at
i
onal
Journ
al of E
valua
tio
n
an
d
Rese
arch
in
Educati
on (I
JE
RE)
Vo
l.
8
, No
.
4
,
Decem
ber
201
9
, p
p.
659
~
665
IS
S
N: 22
52
-
8822
,
DOI: 10
.11
591/
ije
re
.
v8
i
4
.
20292
659
Journ
al h
om
e
page
:
http:
//
ia
es
core
.c
om/
journa
ls
/i
ndex.
ph
p/IJE
R
E
The
R
asch
-
ratin
g scale m
odel to
identify
l
earning
difficul
ties of
ph
ysic
s s
tud
ents b
ase
d
on
se
lf
-
regu
lation sk
ills
Ha
bibi
Habi
b
i
1
,
Ju
m
ad
i
Jumadi
2
,
Mundi
lart
o Mun
dil
ar
to
3
1
Do
ct
or
Ca
ndidate
,
Un
i
ver
sit
as N
e
ger
i
Y
ogya
kar
ta
, In
done
sia
1
Dep
a
rtm
ent o
f
Ph
ysi
cs
Educat
ion,
I
K
IP
Mat
a
ram
(
Undikm
a
),
Ind
on
e
sia
2,3
Depa
rt
m
ent of
Ph
y
sics
Educat
i
on,
Univer
si
ta
s
Nege
ri
Yog
y
a
ka
rta
,
Indone
si
a
Art
ic
le
In
f
o
ABSTR
A
CT
Art
ic
le
history:
Re
cei
ved
A
ug
16
, 201
9
Re
vised
Oct
2
4
, 2
01
9
Accepte
d
Nov 12
, 201
9
Thi
s
stud
y
ap
pli
es
the
unid
i
m
ensiona
l
Rasc
h
Model
assum
pti
ons
in
ide
nti
f
y
i
ng
ph
y
s
ic
s
l
ea
rning
dif
fic
ulties
b
ase
d
on
student
s
'
se
l
f
-
reg
ulation
abi
litie
s
.
A
tot
a
l
of
126
ph
y
sics
te
a
che
r
c
andi
d
a
te
s
have
b
ee
n
o
bserve
d
for
one
sem
este
r.
S
el
f
-
proj
ec
t
as
a
le
arn
ing
strateg
y
has
b
ee
n
used
.
Data
w
ere
col
l
ec
t
ed
using
20
it
ems
in
ra
ti
n
g
sca
le
s
and
th
e
n
ana
l
y
z
ed
qu
a
n
ti
tativel
y
to
get
f
ea
sibi
li
t
y
in
m
ea
suring
self
-
r
egul
a
ti
on
ski
ll
s.
The
r
esult
s
h
ave shown
tha
t
the
profi
le
it
ems
ana
l
y
z
ed
b
y
the
Rasch
Model
a
re
fe
asibl
e
to
m
ea
sure
sel
f
-
reg
ulation
skill
s
through
a
self
-
sus
ta
ini
ng
proj
ec
t
stra
te
g
y
.
M
ost
ph
y
sics
te
a
che
r
c
andi
da
t
es
have
a
m
edium
abi
li
t
y
o
f
51%
in
the
proc
ess
of
self
-
reg
ulation,
h
igh
=
33%,
and
low
=
16%.
Th
e
implicati
ons
of
ap
pl
y
ing
self
-
proje
c
ts t
o
the pr
oc
esses of
se
lf
-
r
egul
a
ti
on
are di
s
cussed
in this
article
.
Ke
yw
or
d
s
:
Ra
sch
-
m
od
el
Ra
ti
ng
scale
s
Self
-
regulat
ion
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
:
Hab
i
bi H
a
bib
i,
Dep
a
rtm
ent o
f Physi
cs
Edu
cati
on
,
IKIP
Ma
ta
ram
(
U
nd
i
km
a),
I
ndonesi
a
Pem
ud
a
Street
No
. 59A,
Mat
aram
, P
os
tc
ode:
8312
5,
I
ndone
sia
Em
a
il
: hab
ibi2
702198
3@
ya
hoo.com
1.
INTROD
U
CTION
Ph
e
no
m
ena
in
ever
y
day
li
fe
that
ha
ve
been
integrate
d
in
physi
cal
con
ce
pt
s
can
m
otivate
le
arn
e
rs
to
easi
ly
un
de
rsta
nd.
Co
ns
ci
ou
s
or
not,
that
al
l
li
fe
has
been
su
r
r
ounded
by
ph
ysi
cal
phe
no
m
ena
with
al
l
it
s
com
plexity
[1
]
.
Alth
ough
physi
cs
is
ver
y
i
m
po
rtant
to
s
ol
ve
va
rio
us
prob
le
m
s
in
daily
li
fe,
unf
or
t
unat
el
y
m
any
stud
e
nts
consi
der
it
ve
ry
com
plica
t
ed
the
n
disru
pts
their
m
otivati
on
an
d
int
erest
to
le
ar
n
it
[2
]
.
Howe
ver,
the
com
plexity
of
cases
in
ph
ysi
cs
beco
m
es
a
chall
e
ng
e
for
eac
h
le
ar
ne
r
to
m
ake
it
easy
,
interest
ing, a
nd
fun [3
]
.
Accor
ding
to
En
glish
[
4]
that
m
otivati
on
is
cl
os
el
y
relat
ed
to
the
abili
ty
of
sel
f
-
regulat
io
n
w
hich
has
a
po
sit
ive
im
pact
on
res
ponsi
bili
ti
es
and
le
arn
i
ng
diff
ic
ulti
es.
This
m
ea
ns
that
le
arn
i
ng
dif
ficult
ie
s
can
be
detect
ed
th
r
ough
sel
f
-
re
gu
la
ti
on
sk
il
ls
in
dicat
or
s.
I
n
a
dd
it
io
n,
sel
f
-
regulat
ion
is
a
psy
ch
o
log
ic
al
c
onditi
on
tha
t
can
be
de
te
ct
ed
us
i
ng
a
ps
yc
ho
m
et
ric
scal
e
te
st.
Psycho
lo
gical
conditi
ons
include
per
ce
ptions,
opini
ons,
a
nd
at
ti
tud
es of i
ndividu
al
s
or
gro
up
s
of
pe
op
le
a
bout
var
i
ou
s
s
oc
ia
l ph
e
no
m
en
a [
5
-
6].
A
te
st
m
us
t
be
cat
ego
rize
d
as
valid
to
b
e
us
ed
in
inter
pr
et
in
g
the
va
riables
that
ar
e
m
easur
e
d
correct
ly
[7
]
.
Pr
e
vious
stu
di
es
relat
ed
t
o
t
he
ps
yc
holo
gy
of
co
gnit
ive
processes
ha
ve
pro
ven
that
sel
f
-
regulat
ion
s
kill
s
hav
e
been
use
d
to
predict
le
arn
i
ng
ac
hiev
e
m
ent
[8
-
12]
.
Howe
ver,
the
inabili
ty
to
regulat
e
le
arn
in
g
beh
a
vi
or
is
cl
os
el
y
r
el
at
ed
to
dif
fere
nces
in
le
vels
of
le
ar
ni
ng
di
ff
ic
ulti
es
[
13
-
14]
.
This
al
lo
w
s
that
m
easur
em
ent ite
m
s that h
a
ve been
used
are no
t
fit (in
valid
)
w
it
h va
riables
that ha
ve bee
n m
easur
ed
.
This
arti
cl
e
de
scribes
the
f
easi
b
il
it
y
of
t
he
Ra
ti
ng
Sca
le
Mod
el
instr
um
ent
to
identify
le
arn
ing
diff
ic
ulti
es
thr
ough
the
f
our
strat
egies
of
sel
f
-
regulat
io
n
processes
a
s
f
ollows:
pla
nn
i
ng,
c
on
t
ro
l
li
ng
,
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2252
-
8822
In
t.
J
.
Eval
.
&
Re
s
.
E
du
c
.
Vo
l
.
8
, No
.
4
,
Dec
e
m
ber
2019
: 6
5
9
-
66
5
660
evaluati
ng,
a
nd
rei
nfor
ci
ng.
Self
-
regulat
ion
is
a
dim
ension
of
m
et
acog
ni
ti
ve
know
le
dge
use
d
t
o
re
gu
la
te
cogniti
ve pr
oc
esses as a
b
a
sis f
or
plan
ning le
arn
i
ng strategi
es [15
]
.
Each
per
s
on
ha
s
the
c
har
act
e
risti
cs
to
m
anag
e
his
co
gnit
ive
processe
s
w
hich
ha
ve
im
pl
ic
at
ion
s
f
or
the
ap
plica
ti
on
of
le
ar
ning
s
trat
egies.
I
f
e
ver
y
on
e
has
a
div
e
rsity
of
s
el
f
-
re
gula
ti
on
processes
,
the
n
it
is
i
m
po
ssible
to
i
nter
pr
et
[
16
-
17]
.
This
has
c
on
trad
ic
te
d
the
a
s
su
m
ption
of
a
un
i
dim
ension
a
l
Ra
sch
Mode
l,
[18].
Fischer
[
19
]
ha
s
asserte
d
tha
t
the
existe
nce
of
di
ff
e
ren
t
processes
on
the
sam
e
con
ti
nuum
of
abili
ti
es
cause
s
m
easur
em
ents
no
t
to
be
li
near
a
nd
im
po
ssible
to
co
m
par
e.
The
im
pl
ic
at
ion
requires
the
sam
e
init
ia
l
assum
ption
s
a
bout
sel
f
-
re
gula
ti
on
strat
egi
es
to
easi
ly
gen
e
rali
ze
the
div
e
rsity
of
ind
ivi
du
al
le
arn
i
ng
diff
ic
ulti
es found
.
Ra
sch
Ra
ti
ng
Scal
e
m
od
el
s
(Rasch
-
RSM
)
a
re
orde
re
d
cat
egories
that
ar
e
us
e
d
to
view
it
e
m
s
as
a
represe
ntati
on
of
m
easur
e
d
s
a
m
ple
be
hav
i
or.
T
his
m
eans
that
the
data
c
ollec
te
d
is
in
t
he
form
of
opinion
scal
e
or
a
la
te
nt
at
ti
tud
e.
T
he
m
ai
n
assum
ption
of
R
SM
is
t
hat
the
assess
m
ent
of
res
ponse
cat
eg
or
ie
s
of
it
em
s
m
us
t
be
the
sam
e
wh
ere
the
scor
e
m
us
t
increase
co
ns
ta
ntl
y
accord
i
n
g
t
o
the
le
vel
of
diff
ic
ulty
[20
-
21
]
.
The
Ra
sch
m
od
el
has
bette
r
m
eas
ur
em
ent
accur
acy
than
cl
assic
al
te
st
theor
y
(CTT)
.
The
Ra
sch
m
od
el
is
a
ble
to
process
the
e
va
luati
on
of
te
st
res
ults
it
erati
ve
ly
throu
gh
cal
ibrati
on
to
fi
nd
the
optim
al
com
po
sit
ion
an
d
m
e
et
the
m
od
el
criter
ia
[22
-
24]
.
T
his
ad
van
ta
ge
is
no
t
com
plem
ented
by
CT
T;
therefore
it
has
the
li
m
it
a
ti
on
of
requirin
g
m
or
e
test
it
e
m
s to
ge
t qu
al
it
y m
eas
ur
em
ents.
The
Ra
sc
h
M
odel
pr
ov
i
des
a
naly
sis
to
ver
i
fy
the
as
su
m
ption
s
of
the
it
em
s
us
ed.
Ra
sc
h
Mo
deli
ng
al
so
p
r
ov
i
des
e
stim
at
es
of
the
sp
eci
fic
c
har
ac
te
risti
cs
of
the d
iffic
ulty
le
vel
of
it
em
s
in
cer
ta
in
sta
ges
bas
ed
on
pro
bab
il
it
y
[
25
-
28
]
.
T
he
pro
ba
bili
ty
appro
ac
h
acc
omm
od
at
ed
ai
m
s
to
kee
p
t
he
Ra
sch
Mo
de
l
not
determ
inist
ic
,
therefo
re
m
ea
su
ri
ng
obj
e
ct
s
ca
n
be
ide
nt
ifie
d
m
or
e
caref
ully
.
The
pro
bab
il
it
y
of
t
he
Ra
sch
M
od
el
f
or
RSM
has
be
en
de
velo
ped
base
d
on
t
he
Partia
l
Credit
Mod
el
(P
CM
)
pro
bab
il
it
y
eq
uatio
n
as foll
ows:
=
(
−
)
1
+
(
−
)
(1)
Wh
e
re
β
is a
c
om
po
ne
nt o
f
th
e abili
ty
level, and
is t
he
sp
ec
ific
ch
aracte
risti
c o
f
the
d
if
fic
ulty
level of it
em
i
at
eac
h
ste
p
j,
w
her
eas
in
RSM
pro
ba
bili
ty
,
the
value
is
desc
ribe
d
as
−
he
nce
the
eq
uati
on
beco
m
es as fol
lows
:
=
(
−
−
)
1
+
(
−
−
)
;
f
or
x
= 1
, 2, 3,…,
m
i
(2)
The
pro
ba
bili
t
y
eq
uation
of
t
he
Ra
sc
h
M
od
el
shows
that
is
the
dif
ficult
y
le
vels
of
res
pons
e
it
e
m
s,
and
is
the
sp
eci
fic
char
act
e
risti
cs
of
the
diff
ic
ulty
le
vels
of
res
pons
e
it
em
s
i
i
n
each
ste
p
of
j
.
Wh
il
e
it
e
m
s
that
hav
e
bee
n
te
ste
d
are
declare
d
fit t
o
be
us
e
d
if
the I
NFIT T
va
lue is in the
ra
ng
e
of
±
2 wh
il
e the stand
a
rd
error
(α)
is
5% [
17
]
.
The
ap
plica
ti
on
of
rati
ng
sca
le
has
been
ca
rr
ie
d
ou
t
in
va
rio
us
so
ci
al
stud
ie
s
to
detect
a
per
son'
s
beh
a
vior
su
c
h
as:
identify
ing
ben
c
hm
ark
s
from
the
po
li
to
m
us
scal
e
[1
8],
interpr
et
in
g
th
e
strat
egies
of
stud
e
nt
le
arn
in
g
m
otivati
on
[
29
]
,
ex
plainin
g
unde
r
sta
nd
i
ng
c
on
c
epts
from
cand
idate
s’
el
em
e
ntary
sch
oo
l
te
acher
,
[
30
]
,
an
d
strea
m
li
nin
g
m
easur
em
ents
to
i
m
pro
ve
te
acher
so
ci
al
sk
il
ls
[31
]
.
This
has
pr
ov
e
n
that
the
Ra
ti
ng
scal
e m
od
el
is flexible t
o be a
pp
li
ed
in va
rio
us
c
onti
nuum
i
nvolv
i
ng the
af
fecti
ve do
m
ai
n.
2.
RESEA
R
CH MET
HO
D
2.1
Ca
se
s
an
d
i
nstr
uments
The
sta
bili
ty
of
the
est
i
m
at
ed
resu
lt
s
on
the
exp
ect
e
d
log
it
scal
e
is
need
ed
.
In
sp
eci
fic
cases
us
in
g
a
scal
e
of
±
1
l
ogit
(lo
ga
rithm
odds
unit
)
t
o
obta
in
t
he
sta
bili
ty
of
t
he
est
i
m
at
ed
data
for
a
c
onfide
nce
l
evel
of
99%
is
req
uir
e
d
that
the
nu
m
ber
of
case
s
(s
a
m
ple
siz
e)
is
fe
asi
ble
at
le
as
t
fo
r
50
pe
op
le
[
32
]
.
T
her
e
for
e
the
sam
ple
of
this
stud
y
is
126
pe
op
le
w
ho
a
re
pros
pecti
ve
physi
cs
te
achers
i
n
the
early
sem
e
ste
r
of
2018
in
one
of private
un
i
ve
rsity
, Indo
nes
ia
.
Teacher
can
di
dates
w
ho
ha
ve
bee
n
ta
ke
n
w
it
h
pur
po
si
ve
s
a
m
pling
are
co
nd
it
io
ned
by
pro
j
ect
-
base
d
le
arn
in
g
for
one
sem
est
er
to
facil
it
at
e
sel
f
-
re
gu
la
ti
on
str
at
egies.
T
hrough
t
he
a
pp
li
ca
ti
on
of
t
his
le
arn
i
ng
m
od
el
,
the
a
bili
ty
of
sel
f
-
re
gu
la
ti
on
of
pros
pe
ct
ive
te
ache
rs
can
be
ass
ume
d
to
be
ho
m
og
ene
ou
s
i
n
eac
h
ste
p
of
the
pro
j
ect
.
The
ste
ps
of
the
sel
f
-
regul
at
ion
strat
egy
identifie
d
a
re
as
fo
ll
ows:
pl
ann
i
ng,
m
on
it
or
i
ng,
evaluati
ng,
a
nd
rein
f
or
ci
ng.
Id
e
ntific
at
ion
i
s
done
th
rou
gh
the
pr
ov
isi
on
of
quest
io
nnai
res
that
have
been
adap
te
d
to
s
el
f
-
re
gu
la
ti
on
str
at
egies.
T
he
quest
io
nn
ai
re
c
on
sist
e
d
of
20
it
e
m
s
that
us
ed
a
f
our
-
poin
t
rati
ng
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
E
val & R
es Educ
.
IS
S
N:
22
52
-
8822
Th
e r
as
c
h
-
ra
ti
ng sc
ale mo
del to id
e
ntif
y lear
ning
diff
ic
ulti
e
s o
f
physi
cs stu
den
ts
base
d on ...
(
Habi
bi Ha
bib
i
)
661
scal
e
nam
e
ly
:
1
=
Com
plete
l
y
Un
true
,
2
=
Un
t
ru
e
,
3
=
Tr
ue,
4
=
Com
plete
ly
Tru
e.
Th
e
qu
est
io
nnai
r
e
was
giv
e
n
at
th
e e
nd
of the sem
est
er to fin
d o
ut t
he
le
ar
ning st
r
at
egies u
se
d w
hile usi
ng proj
ect
-
base
d
le
ar
ni
ng
.
2.2
Data anal
ys
is
The
prob
a
bili
ty
of
a
Ra
sc
h
Mod
el
is
ge
ne
rated
f
r
om
the
raw
data
ana
ly
zed
us
in
g
th
e
QUES
T
app
li
cat
io
n
to
find
a
fit
m
od
el
a
nd
est
i
m
at
e
the
te
ac
her
'
s
sel
f
-
regu
la
ti
on
abili
ty
le
vels.
The
Q
UES
T
app
li
cat
io
n
p
r
ov
i
des
a
wide
r
range
of
com
m
un
it
y
m
easur
e
m
ent
and
res
earch
f
aci
li
ti
es
based
on
the
la
te
st
ps
yc
hom
et
ric
m
et
ho
ds
from
heter
og
e
ne
ous
it
e
m
resp
on
se
m
od
el
s,
m
ulti
di
m
ension
a
l
responses,
l
at
ent
regressio
n
m
od
el
s,
a
nd
in
for
m
s
log
ic
al
val
ues.
The
Q
UE
ST
pr
ogram
is
fr
ee
war
e
that
can
be
do
wn
l
oa
ded
at
https:/
/ww
w.ra
sch.org/s
of
twa
re.
htm
.
The
ce
ntral
el
e
m
ents
in
the
QUEST
pro
gram
are
it
e
m
respo
ns
e
the
ori
es
(I
RTs
)
tha
t
hav
e
bee
n
adjuste
d
to
t
he
Ra
sch
Mo
del
(RM).
T
he
Q
UES
T
pro
gr
a
m
us
es
unc
ondi
ti
on
al
(U
C
O
N
)
or
a
j
oi
nt
m
a
xim
u
m
li
kelihood
to
e
stim
at
e
par
am
e
te
r
it
e
m
s
on
raw
sco
res
[
33
]
.
The
ra
w
sco
re
on
a
scal
e
r
will
be
con
ve
rted
to
a
log
it
scale
that
sh
ows
a
per
s
on
'
s ab
il
it
y (b
)
as
(3)
:
b
= l
og [(r/
(L
-
r)]
(3)
Fo
r
L
is
the
nu
m
ber
of
act
ivit
ie
s
(ite
m
s).
Wh
ereas
to
fin
d
ou
t
t
he
le
vel
of
diff
ic
ulty
of
it
e
m
(d
)
,
howe
ver
the
value o
f
r
ca
n be c
onve
rted
i
nto
a
lo
git scal
e as in
the e
qu
at
ion
as
(
4)
:
d
= l
og [(N
-
S
)/S)]
(4)
Wh
e
re
N
is
the
nu
m
ber
of
te
s
ts
(c
ase
/
per
son)
an
d
S
is
the
scor
e
of
the
it
e
m
[3
4
]
.
The
s
iz
e
of
S
for
po
li
tom
us
'
scaled
m
easur
em
e
nt
data
i
n
th
e
QU
E
ST
pro
gr
a
m
beco
m
es
w
ij
an
d
the
di
ff
ic
ulty
le
vel
of
d
will
be
change
d
to
δ
ij
.
The
e
quat
ion f
or RSM
in
the
QU
E
ST
pro
gr
a
m
is as
(
5)
:
P (
=
)
∑
(
−
−
=
0
)
∑
=
0
∑
(
−
−
)
=
0
(
5
)
wh
e
re
β
n
is
a
com
po
ne
nt
of
th
e
abili
ty
le
vel
of
t
he
te
st
(cas
e
/
per
s
on)
n
,
w
ij
is
the
scor
e for
ste
p
j
on
a
n
it
e
m
i
,
δ
i
in
form
s
us
of
the
dif
ficult
y
le
vels
of
the
it
e
m
s,
and
τ
ij
is
t
he
s
pecific
c
ha
racteri
sti
c
of
th
e
dif
ficult
y
le
ve
ls
of
the it
em
s in
i
cat
egory at
each
step
j
.
Wh
ere
a
s the e
quat
ion
scan
ned in
dic
ho
t
om
ou
s is
re
du
ce
d
t
o
(6)
P (
=
)
(
(
−
)
)
1
+
(
(
−
)
)
(
6
)
The
diff
ic
ulty
le
vels
in
t
he
Q
UES
T
Pro
gr
a
m
are
cl
early
e
xpresse
d
in
the
thres
hold
valu
e.
The
th
reshol
d
value
is
cal
culat
ed
ba
sed
on
t
he
va
lue
of
τ
wh
ic
h
re
pr
ese
nts
t
he
abili
ty
le
vels
require
d
by
t
he
te
st
(case
/
pe
rson
)
with the
h
i
ghes
t chan
ce
of
0.5
0.
3.
RESU
LT
S
A
ND
DI
SCUS
S
ION
3.1.
Fittin
g of
R
asc
h m
od
el
(
Par
amet
er
lo
gisti
c m
odel
)
The
L
og
ist
ic
par
am
et
er
m
od
el
disp
la
ys
t
he
outp
ut
of
su
it
abili
ty
of
it
e
m
s
in
detect
ing
le
ar
ning
diff
ic
ulti
es
thr
ough
sel
f
-
re
gu
la
ti
on
strat
egie
s.
The
com
patibil
it
y
of
it
e
m
s
de
velo
ped
both
base
d
on
IN
F
IT
MNSQ
or
INF
IT
T
is
in
acco
rd
a
nce
with
t
he
requirem
ents
(Tab
le
1).
T
hi
s
is
in
accor
da
nce
with
the
re
su
lt
s
of
the
IRT
a
naly
s
is
that
the
distr
ibu
ti
on
of
al
l
i
tem
s
has
bee
n
fit
accor
ding
t
o
the
Ra
sc
h
M
od
el
w
hich
is
i
n
the
range
of
sc
or
e
s
f
ro
m
0.77
to
1.3
0.
This
m
eans
t
hat
the
c
on
st
ru
ct
of
it
em
s
is
app
r
opri
at
e
an
d
ef
fecti
ve
for
m
easur
in
g
sel
f
-
re
gu
la
ti
on i
ndic
at
or
s.
The
ou
t
pu
t
f
r
om
the
QU
ES
T
program
pr
ov
ides
inf
orm
ati
on
a
bout
the
f
easi
bili
ty
of
ite
m
s
analy
zed
accor
ding
to
th
e
Ra
sch
m
od
el
.
Table
1
il
lustr
at
es
the
ou
tp
ut
that
inform
s
abo
ut
the
distri
buti
on
of
it
e
m
s
b
as
e
d
on their
com
patibil
it
y wit
h
the Rasch m
od
el
.
Th
e
Q
UES
T
outp
ut pr
ov
i
des refe
ren
ce
opti
ons
for
the
s
uitabil
it
y
of m
od
el
s d
e
ve
lop
e
d base
d on INFIT
MN
SQ crit
eria
or
INF
IT
T
.
Table
1
sho
ws
the
ou
tp
ut
dis
tribu
ti
on
pro
file
of
it
e
m
s
sp
eci
fical
ly
fo
r
fit
crit
eri
a
based
on
MNS
Q
IN
F
IT.
All
it
e
m
s
that
hav
e
be
en
us
e
d
a
re
w
it
hin
the
ra
ng
e
receive
d
(f
it
by
the
Ra
sc
h
m
od
el
)
acc
ordin
g
to
the
IN
F
IT
MNS
Q
an
d
INFIT
cr
it
eria.
The
re
a
re
no
it
e
m
s
that
sugg
est
to
be
im
pr
oved
be
cause
t
hey
m
eet
th
e
validit
y
crit
eria.
This
m
eans
t
hat
the
ou
tp
ut
produce
d
c
an
be
f
ully
us
ed
to
inter
pr
et
the
pr
oba
bili
ty
of
respo
nd
i
ng
sel
f
-
regulat
ion
be
hav
i
or.
In
ad
di
ti
on
,
it
can
be
sta
te
d
that
the
it
e
m
s
dev
el
ope
d
ha
ve
been
f
e
asi
ble
to
detect
le
arn
i
ng
di
ff
ic
ulti
es
thr
ough
in
dicat
or
s
of
sel
f
-
regulat
ion
a
bili
ty
.
This
fi
nd
i
ng
is
ve
ry
im
po
rta
nt
as
a
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2252
-
8822
In
t.
J
.
Eval
.
&
Re
s
.
E
du
c
.
Vo
l
.
8
, No
.
4
,
Dec
e
m
ber
2019
: 6
5
9
-
66
5
662
pr
e
requisi
te
for
detect
in
g
le
arn
i
ng
diff
ic
ul
ti
es
fo
r
pros
pe
ct
ive
ph
ysi
cs
te
achers
ba
se
d
on
sel
f
-
re
gula
ti
on
sk
il
ls.
A
naly
sis
of
the
Ra
sc
h
m
od
el
is
able
to
in
form
about
the
c
har
act
e
risti
cs
of
t
he
t
est
it
e
m
s
fo
rm
ed
int
o
the sam
e
m
et
ri
cs.
Table
1
.
Item
s
that are
fit wit
h t
he
R
asc
h
m
od
el
Ite
m
s
Inf
it M
NSQ
Inf
it t
Ou
tf
it t
Criterion
1
1
.14
1
.2
1
.0
Fit
2
1
.01
0
.1
0
.2
Fit
3
1
.10
0
.8
0
.9
Fit
4
1
.06
0
.6
0
.5
Fit
5
0
.86
-
1
.2
-
0
.9
Fit
6
1
.15
1
.2
1
.4
Fit
7
0
.86
-
1
.2
-
1
.0
Fit
8
0
.84
-
1
.3
-
0
.9
Fit
9
0
.86
-
1
.2
-
1
.0
Fit
10
1
.07
0
.6
0
.5
Fit
11
1
.01
0
.1
0
.0
Fit
12
1
.12
1
.0
0
.8
Fit
13
0
.90
-
0
.8
-
0
.7
Fit
14
1
.06
0
.6
0
.5
Fit
15
1
.04
0
.4
0
.4
Fit
16
0
.94
-
0
.5
-
0
.4
Fit
17
0
.82
-
1
.5
-
1
.2
Fit
18
0
.87
-
0
.9
-
0
.1
Fit
19
0
.92
-
0
.6
0
.1
Fit
20
0
.84
-
1
.3
-
1
.2
Fit
Criteria
f
o
r
f
it ite
m
s: 0
,
7
7
≤
IN
FIT
M
NSQ ≤
1, 33
[2
3]
o
r
-
2
≤
INF
IT
T≤
2
[17]
.
Accor
ding
to
Lo
wen
t
hal
[
35
]
,
te
st
it
e
m
s
in
qu
a
ntit
at
ive
an
al
ysi
s
m
us
t
ha
ve
t
he
reli
abili
t
y
to
m
easur
e
the
de
grees
of
consi
ste
ncy.
T
his
m
eans
that,
good
m
easur
e
m
ents
m
us
t
pr
esent
a
high
de
gr
ee
of
reli
a
bi
li
t
y
if
the
sc
or
es
a
re
consi
ste
nt
[
36
]
.
Th
e
Ra
sch
m
od
el
is
sti
ll
an
analy
ti
cal
t
oo
l
that
can
m
easu
re
the
accu
racy
of
the
validit
y
and
reli
abili
ty
of
researc
h
in
s
trum
ents,
eve
n
te
sti
ng
t
he
su
it
abili
ty
of
pe
ople
an
d
it
e
m
s
si
m
ultaneou
sly
.
Acc
ordi
ng
to
Christe
ns
en
[
3
7
]
,
Ra
sch
m
od
el
ing
ca
n
pro
du
ce
m
easur
e
m
ent
instr
um
e
nts
that
are
bette
r
a
n
d
m
or
e accur
at
e
than othe
rs.
3.2.
Ca
se
estim
ate
s for iden
tif
ying
sel
f
-
re
gu
l
ati
ng
ab
il
ity
The
qu
e
st
outp
ut
dis
play
s
det
ai
ls
of
s
kill
le
vels
f
ro
m
each
case
base
d
on
the
sc
or
e
est
i
m
at
es.
High
est
i
m
at
ed
scores can
be
i
nter
pr
et
e
d
that t
he
sel
f
-
re
gula
ti
on
sk
il
ls of
pro
sp
e
ct
ive tea
cher
s
are go
od. T
his
m
eans
that
sel
f
-
re
gu
la
ti
on
is
propo
rtion
al
to
an
inc
r
ease
in
le
arn
in
g
m
otivati
on
and
a
ff
ect
le
arni
ng
di
ff
ic
ulti
es
.
The
scor
e
est
i
m
at
e
s
in
the
IRT
r
equ
i
re
res
pond
ents
w
ho
ha
ve
high
a
bili
ty
(Esti
m
at
es
scor
es>
1.0
0),
m
e
diu
m
(
-
1.00 ≤
Esti
m
a
t
es scores
≤
1.00),
a
nd lo
w (Est
i
m
at
es sco
res<
-
1.0
0) as s
how
n
in
Ta
ble 2.
Table
2
.
P
hysi
cs tea
cher ca
ndidate
s b
ase
d o
n t
heir
le
vel of s
el
f
-
re
gula
ti
on
a
bili
ty
.
No
Cas
e/Perso
n
esti
m
ates
Levels
of
self
-
regu
latio
n
Sk
ills
Cas
e esti
m
a
tes
(perso
n
abilities
)
Su
m
of
cases
Persen
tag
es
(%)
1
Est >
1
.00
42
33
Hig
h
ability
2
-
1
.00
≤
Est
≤
1
.00
64
51
Mediu
m
abilit
y
3
Est <
-
1
.00
20
16
Low ab
ility
Table
2
s
hows
the
nu
m
ber
of
pe
rcen
ta
ge
of
ph
ysi
cs
te
ac
he
r
ca
nd
i
dates
w
ho
posses
s
sel
f
-
re
gu
la
ti
on
sk
il
ls
for
t
he
c
at
egories
of:
hi
gh
,
m
ediu
m
,
and
lo
w
a
bili
ties.
T
he
per
ce
nt
age
of
t
he
num
ber
of
pros
pe
ct
ive
te
achers
w
ho
hav
e
high
sel
f
-
regulat
ion
s
kill
s
is
33
%
,
an
d
then
m
os
t
of
them
are
in
the
m
ediu
m
cat
ego
ry
ie
51%,
w
hile
16
%
are
of
l
ow
a
bili
ty
.
It
was
f
ound
t
hat
m
os
t
of
t
he
ph
ysi
cs
te
acher
can
dida
te
s
ha
d
t
he
a
bi
li
t
y
to
cat
egorize
'm
e
diu
m
'
.
This
m
eans
t
hat,
the
us
e
of
al
te
r
na
ti
ve
le
arn
i
ng
strat
egies
ca
n
i
m
pr
ov
e
one'
s
sel
f
-
regulat
or
y
a
bili
ti
es
and
m
ini
m
iz
e
le
arn
ing
diff
ic
ulti
es.
Ac
cordin
g
to
Be
m
ben
utty
,
et
al,
[
38
]
that
le
ar
ner
s
w
ho
hav
e
hi
gh
sel
f
-
regulat
ion
ca
n
ove
rco
m
e
l
earn
i
ng
dif
ficult
ie
s
because
m
otivati
on
is
getti
ng
bette
r
.
Thi
s
m
ot
ivati
on
is a
p
syc
ho
l
og
ic
al
cause t
hat in
flu
ences lear
ni
ng
diff
ic
ulti
es [
39
-
41
].
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
E
val & R
es Educ
.
IS
S
N:
22
52
-
8822
Th
e r
as
c
h
-
ra
ti
ng sc
ale mo
del to id
e
ntif
y lear
ning
diff
ic
ulti
e
s o
f
physi
cs stu
den
ts
base
d on ...
(
Habi
bi Ha
bib
i
)
663
Fo
r
pr
os
pecti
ve
te
achers
,
sel
f
-
regulat
ion
is
ver
y
im
po
rtant
to
be
trai
ne
d.
G
ood
sel
f
-
r
eg
ulati
on
c
a
n
help
pros
pecti
ve
te
ache
rs
be
com
e
accustom
ed
to
com
pleti
ng
a
dm
in
ist
rati
ve,
te
achin
g,
a
nd
res
earc
h
assignm
ents.
A
total
of
126
cases
that
hav
e
been
ide
ntifie
d
show
the
re
su
lt
s
of
the
est
i
m
ation
that
51%
of
pros
pecti
ve
ph
ysi
cs
te
achers
hav
e
m
edium
cat
egory
sel
f
-
r
egu
la
ti
on,
33
%
f
or
hi
gh,
a
nd
16%
with
lo
w
sel
f
-
regulat
ion
(
Ta
ble
2)
.
T
hese
r
esults
hav
e
pro
ve
n
that
sel
f
-
pro
j
ect
has
grea
tl
y
facilit
at
ed
t
he
a
s
su
m
ption
of
the
Ra
sch
Mod
el
[
42,
43
]
.
The
di
ff
ere
nce
in
sel
f
-
regulat
ion
a
bi
li
t
y
was
finall
y
identifie
d
thr
ough
the
resu
lt
s
of
the
Ra
sch
Mo
del
analy
sis
to
be
interp
reted
.
Jo
yc
e
[
44
]
sai
d
that
the
i
m
p
lem
entat
ion
of
le
arn
in
g
strat
egies
is
an
al
te
r
native s
olu
ti
on to
sti
m
ulate
learne
rs'
self
-
re
gula
ti
on
sk
il
ls.
4.
CONCL
US
I
O
N
Learn
i
ng
dif
ficult
ie
s
are
a
ps
yc
ho
lo
gical
cause
that
can
di
srupt
stu
den
t
s
el
f
-
re
gula
ti
on
in
le
arn
i
ng
.
T
he
Rasch
Model
is
fea
sibl
e
to
m
ea
sure
s
el
f
-
reg
u
la
t
ion
skill
s
through
a
self
-
sus
ta
ini
ng
proje
ct
stra
te
g
y
.
T
he
appr
opriat
enes
s
betwee
n
le
arn
i
ng
strat
egi
es
an
d
le
ar
ne
r
cha
racteri
sti
cs
has
co
ntri
bu
te
d
po
sit
ive
ly
for
sel
f
-
re
gula
ti
on.
ACKN
OWLE
DGE
MENTS
This
resea
rc
h
was
s
upported
by
the
Mi
nistry
of
Fina
nce
and
Re
sea
rc
h
and
Tech
nolo
gy
of
Hi
gh
e
r
Ed
ucati
on th
rough
t
he
B
UDI
DN sch
olar
sh
i
p pro
gr
am
.
REFERE
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odel
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adi
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id
ent
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ade
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t
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isio
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Shift
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ess
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om
e
self
-
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ulator
y
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m
erman,
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Atta
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sel
f
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reg
ulation:
A
social
cogn
it
iv
e
per
spec
t
ive,"
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M.
Boekaert
s,
P.
Pintri
ch,
&
M
.
Ze
idn
er
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andbook
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-
regulat
ion
,
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3
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39,
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m
m
erman,
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tsant
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"
The
hidden
dimension
of
per
sonal
compet
enc
e
:
Self
r
egula
te
d
le
arn
ing
and
pra
ctice,"
In
A
.
J.
El
l
iot
&
C.
S.
Dw
e
ck,
e
t
al
,
Handbook
of
co
mpete
nc
eand
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oti
vation
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ess
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m
m
erman,
B.
J.,
&
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D.
H.
"
Motivati
on:
An
essential
dimen
sion
of
self
-
reg
u
la
t
ed
l
earning,
"
In
D.
H
.
Schunk
&
B.
J.
Zi
m
m
erma,
et
al
,
Motivati
on
and
self
-
regulated
le
arning:The
ory,
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and
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icati
ons
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m
m
erman,
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Fro
m
cogni
ti
ve
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odeling
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o
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-
reg
ulation
:
A
socia
l
cogn
it
ive
ca
r
ee
r
path,"
Educ
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ional
Psyc
holog
ist
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vo
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,
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ht
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m
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erman,
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Se
lf
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regulat
ed
le
arni
ng
and
acade
mi
c
achiev
eme
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Appl
ic
a
ti
ons
of
self
-
regulated
le
arning
applie
dacross
div
erse
disci
pli
n
es:
A
tri
bute
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Pro
babi
li
st
ic m
odel
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som
e
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te
l
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nd
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in
m
ent
te
sts
,
"
200
5
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[17]
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i,
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"
Fitt
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th
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ixe
d
Rasch
Model
to
a
re
adi
n
g
comprehe
nsio
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te
st:
Id
ent
i
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i
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"
Mixed
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ent
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Fis
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r,
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H.
"
Unidimensional
li
nea
r
log
isti
c
Rasch
Models
,
"
In
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modern
it
em
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e
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y
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r, New York
,
NY
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B.
"
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le
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K
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E. B.
"
A goodne
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a
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"
Ps
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ida
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n
Voic
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f
e
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assic
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Rasch
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i
ng
Scal
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"
A
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v
ar
y
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IR
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ch to
a
cc
ou
nti
ng
for
response
st
y
l
es
,
"
Psy
cho
metric
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li
n
g
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rogen
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.
"
Pro
babi
li
st
ic m
odel
s for
som
e
in
te
l
ligence a
nd
atta
in
m
ent
te
sts
,
"
200
5
.
[26]
Dehqa
n,
A.,
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