I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
. 14, No. 5, O
c
to
be
r
2025
, pp.
3647
~
3655
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
14
.i
5
.pp
3647
-
3655
3647
Jou
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n
al
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e
page
:
ht
tp
:
//
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bn T
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a
s
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t
a
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e
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d, M
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f
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B
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T
R
A
C
T
A
r
ti
c
le
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y
:
R
e
c
e
iv
e
d
N
ov 27
,
2024
R
e
vi
s
e
d
J
ul
26
,
2025
A
c
c
e
pt
e
d
A
ug 06
,
2025
Machine
learning
has
found
extensive
application
and
improvement
in
the
field
of
education.
Nevertheless,
there
remains
a
lack
of
research
studies
focusing
on
unsupervis
ed
learning
within
this
domain.
To
address
th
is
gap,
our
study
aims
to
investigate
the
relationship
between
teacher
attributes
and
student
achievement
in
Morocco
while
identifying
regions
re
quiring
attenti
on
and
intervent
ion,
using
a
novel
clusteri
ng
approach
bas
ed
on
unsupervised
competitive
learning,
specifically
the
'
Centroid
neural
network'
,
to
cluster
Moroccan
teachers
based
on
their
qualitie
s
and
qualifications.
Teacher
qualities
and
qualifications
are
operationali
zed
as
initial
teaching
qualifica
tions,
completion
of
training
programs
,
and
employm
ent
status.
To
achieve
our
objectiv
e,
we
utili
ze
the
progr
am
for
internationa
l
student
assessment
(PISA)
dataset,
which
pr
ovides
comprehens
ive
responses
from
indivi
dual
student
s,
includi
ng
informat
ion
on
parental
backgrounds,
socio
-
economic
positi
ons,
and
school
cond
itions
.
Additionally
,
we
incorporate
data
from
the
t
eacher
questionnaire,
which
encompass
es
background
informat
ion,
initi
al
education
,
profe
ssional
development,
teaching
practi
ce,
and
teacher
beliefs
and
attitudes.
Con
sistent
with
previous
research,
our
findings
suggest
that
teachers'
qualiti
es
and
qualifications
significantly
influence
student
performanc
e.
Furthermo
re,
our
clusteri
ng
approach
identif
ies
regions
where
there
is
a
pron
ounced
prevalence
of
attributes
negatively
impacting
student
achievement.
U
rging
academicians
to
incorporat
e
resilien
ce
-
building
measures
into
the
de
sign
of
policies in these regions to improve students'
educational outcomes.
K
e
y
w
o
r
d
s
:
C
lu
s
te
r
in
g
C
om
pe
ti
ti
ve
l
e
a
r
ni
ng
E
duc
a
ti
on r
e
f
or
m
M
a
c
hi
ne
l
e
a
r
ni
ng
T
e
a
c
h
e
r
c
om
pe
te
nc
e
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
I
ly
a
s
T
a
m
m
ouc
h
L
a
bor
a
to
r
y of
T
e
le
c
om
m
uni
c
a
ti
ons
S
ys
te
m
s
a
nd
D
e
c
is
io
n E
ngi
ne
e
r
in
g, F
a
c
ul
ty
of
S
c
ie
nc
e
I
bn T
of
a
il
U
ni
ve
r
s
it
y
K
e
ni
tr
a
-
14000, M
or
oc
c
o
E
m
a
il
:
il
ya
s
.t
a
m
m
ouc
h@
ui
t.
a
c
.m
a
1.
I
N
T
R
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D
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C
T
I
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N
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e
ve
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a
l
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m
pi
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tu
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r
a
ti
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on
te
a
c
he
r
qua
li
ty
ha
v
e
f
oc
us
e
d
on
s
tu
de
nt
a
c
hi
e
ve
m
e
nt
a
s
a
f
oc
a
l
poi
nt
.
S
tu
de
nt
s
'
a
bi
li
ti
e
s
a
c
qui
r
e
d
th
r
oughout
th
e
ir
a
c
a
de
m
ic
e
xpe
r
ie
nc
e
a
r
e
vi
ta
l
f
or
th
e
ir
s
uc
c
e
s
s
in
th
e
la
bor
m
a
r
ke
t,
a
nd
unde
r
s
ta
ndi
ng
w
hi
c
h
s
ty
le
of
te
a
c
he
r
is
m
or
e
li
ke
ly
to
f
a
vor
a
bl
y
e
f
f
e
c
t
th
e
ir
hum
a
n
c
a
pi
ta
l
a
c
c
um
ul
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pr
oc
e
s
s
is
c
r
it
ic
a
l
in
a
ny
e
nde
a
vor
to
boos
t
t
he
ir
pe
r
f
or
m
a
nc
e
.
P
o
li
c
ym
a
ke
r
s
,
e
duc
a
ti
ona
l
in
s
ti
tu
ti
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pa
r
e
nt
s
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a
nd
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e
duc
a
ti
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ta
ke
hol
de
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s
a
r
e
a
ll
in
vol
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d
th
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s
e
da
ys
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M
a
ny
s
tu
di
e
s
a
r
e
be
in
g
c
onduc
te
d
to
id
e
nt
if
y
th
e
de
te
r
m
in
in
g
f
a
c
to
r
s
in
f
lu
e
nc
in
g
s
tu
de
nt
s
uc
c
e
s
s
in
or
de
r
to
im
pr
ove
s
tu
de
nt
a
c
hi
e
ve
m
e
nt
.
A
ll
of
th
e
m
s
ha
r
e
th
e
s
a
m
e
f
in
di
ngs
,
s
how
i
ng
a
s
tr
ong
c
or
r
e
la
ti
on
be
twe
e
n
in
s
tr
uc
to
r
c
ha
r
a
c
te
r
is
ti
c
s
a
nd
s
tu
de
nt
p
e
r
f
or
m
a
nc
e
.
T
he
f
a
c
to
r
s
th
a
t
th
e
r
e
s
e
a
r
c
he
r
pa
y
s
c
lo
s
e
a
tt
e
nt
io
n
to
in
r
e
ga
r
d
to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 14, No. 5, O
c
to
be
r
2025
:
3647
-
3655
3648
te
a
c
he
r
c
ha
r
a
c
te
r
is
ti
c
s
a
r
e
e
duc
a
ti
on
ba
c
kgr
ound,
e
xpe
r
ie
n
c
e
,
c
e
r
ti
f
ic
a
te
s
ta
tu
s
,
le
a
de
r
s
hi
p
e
xp
e
r
ie
nc
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,
pe
r
s
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ve
r
a
nc
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, t
e
a
c
he
r
e
va
lu
a
ti
on
s
c
or
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, a
nd pr
e
pa
r
e
dne
s
s
f
or
c
l
a
s
s
w
or
k
[
1]
–
[
7]
,
[
8]
–
[
16
]
.
C
he
tt
y
e
t
al
.
[
17]
f
ound
th
a
t
s
tu
de
nt
s
in
s
tr
uc
te
d
by
hi
ghl
y
e
f
f
e
c
ti
ve
te
a
c
he
r
s
,
a
s
in
di
c
a
te
d
by
s
tu
de
nt
gr
ow
th
pe
r
c
e
nt
il
e
s
(
S
G
P
s
)
a
nd
va
lu
e
-
a
dde
d
m
e
a
s
ur
e
s
(
V
A
M
s
)
,
e
xhi
bi
te
d
a
g
r
e
a
te
r
li
ke
li
hood
of
a
tt
e
ndi
n
g
c
ol
le
ge
,
a
c
hi
e
vi
ng
hi
gh
e
r
e
a
r
ni
ngs
,
li
vi
ng
in
a
f
f
lu
e
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c
om
m
u
ni
ti
e
s
,
a
c
c
um
ul
a
ti
ng
r
e
ti
r
e
m
e
nt
s
a
vi
ngs
,
a
nd
ha
vi
ng
f
e
w
e
r
c
hi
ld
r
e
n
dur
in
g
th
e
i
r
te
e
na
ge
ye
a
r
s
.
I
n
a
s
im
il
a
r
c
ont
e
xt
,
B
e
tt
in
ge
r
a
nd
L
ong
[
18]
e
xa
m
in
e
d
a
s
ubs
ta
nt
ia
l
s
a
m
pl
e
of
publ
ic
in
s
ti
tu
ti
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in
O
hi
o
a
nd
di
s
c
ove
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e
d
th
a
t
a
dj
unc
t
f
a
c
ul
ty
in
c
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s
e
d
th
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li
ke
li
hood
of
s
tu
de
nt
a
tt
r
it
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dur
in
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th
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s
e
c
ond
ye
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r
.
T
he
ir
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s
e
a
r
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h
e
xa
m
in
e
d
th
e
im
pa
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t
of
a
dj
unc
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in
s
tr
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to
r
s
on
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nr
ol
lm
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nt
a
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s
uc
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s
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s
in
s
ub
s
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que
nt
c
our
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e
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,
in
di
c
a
ti
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t
ha
t
a
dj
unc
ts
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gr
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dua
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ubs
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t
in
a
s
ubj
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t
m
or
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th
a
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f
ul
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ti
m
e
,
te
nur
e
-
tr
a
c
k
f
a
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ty
,
a
lt
hough
th
e
e
f
f
e
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t
is
m
in
im
a
l
a
nd
va
r
ie
s
s
ig
ni
f
ic
a
nt
ly
a
c
r
os
s
di
s
c
ip
li
ne
s
.
H
of
f
m
a
nn a
nd
O
r
e
opoulos
[
5]
de
m
ons
tr
a
te
th
a
t,
a
lt
hough
s
tu
de
nt
s
'
pe
r
c
e
pt
io
n
s
of
th
e
ir
in
s
tr
uc
to
r
s
'
te
a
c
hi
ng
e
f
f
e
c
ti
ve
n
e
s
s
s
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r
ve
a
s
a
va
li
d
a
s
s
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s
s
m
e
nt
of
te
a
c
h
e
r
s
'
im
pa
c
t
on
s
tu
de
nt
pe
r
f
or
m
a
nc
e
,
obj
e
c
ti
ve
c
r
it
e
r
ia
s
e
e
m
to
b
e
ir
r
e
le
va
nt
.
O
ur
r
e
s
e
a
r
c
h
e
nha
nc
e
s
e
xi
s
ti
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li
te
r
a
tu
r
e
by
de
m
ons
tr
a
ti
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th
e
in
f
lu
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nc
e
of
in
s
tr
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to
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s
'
te
a
c
hi
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e
xpe
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ie
nc
e
a
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pr
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tr
a
in
in
g
on
M
or
oc
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a
n
s
tu
de
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s
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pe
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or
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a
nc
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in
th
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pr
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m
f
or
in
te
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na
ti
ona
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s
tu
de
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a
s
s
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s
s
m
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nt
(
P
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S
A
)
te
s
t,
w
hi
le
a
ls
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if
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W
e
a
s
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s
s
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tu
de
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s
'
pe
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f
or
m
a
nc
e
by
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a
lc
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a
ti
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th
e
a
ve
r
a
ge
of
pl
a
us
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le
va
lu
e
s
de
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iv
e
d
f
r
om
e
xa
m
in
a
ti
ons
on
a
c
e
r
ta
in
s
ubj
e
c
t.
T
he
s
e
m
e
tr
ic
s
of
s
tu
de
nt
pe
r
f
or
m
a
nc
e
a
ll
ow
us
to
a
s
s
e
s
s
th
e
im
pa
c
t
of
te
a
c
he
r
s
'
a
tt
r
ib
ut
e
s
a
nd
c
r
e
de
nt
ia
ls
o
n
s
tu
de
nt
s
uc
c
e
s
s
.
T
he
do
c
um
e
nt
is
s
tr
uc
tu
r
e
d
a
s
f
ol
lo
w
s
:
s
e
c
ti
on
2
de
li
ne
a
te
s
da
ta
r
e
ga
r
di
ng
s
tu
d
e
nt
a
nd
te
a
c
he
r
a
tt
r
ib
ut
e
s
a
nd
th
e
m
e
th
odol
ogy
e
m
pl
oye
d
in
t
he
s
tu
dy;
s
e
c
ti
on 3 a
r
ti
c
ul
a
te
s
t
he
f
in
di
ngs
;
a
nd
s
e
c
ti
on 4 pr
ovi
de
s
a
c
onc
lu
s
io
n.
2.
D
A
T
A
A
N
D
M
E
T
H
O
D
S
2.1.
D
at
a
T
he
P
I
S
A
a
s
s
e
s
s
e
s
15
-
ye
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r
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d
s
tu
de
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a
bi
li
ty
to
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ppl
y
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e
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m
a
th
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a
ti
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s
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a
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ie
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dge
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nd
a
bi
li
ti
e
s
to
r
e
a
l
-
w
or
ld
pr
obl
e
m
s
[
19]
,
[
20]
.
W
e
c
hos
e
P
I
S
A
s
in
c
e
it
is
one
of
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e
onl
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-
s
our
c
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pi
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ic
a
l
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ta
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or
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ond,
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r
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S
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ip
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om
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ot
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h.
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hi
s
s
tu
dy uti
li
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e
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t
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o di
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ti
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t
da
ta
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ts
:
th
e
P
I
S
A
2018 s
tu
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nt
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ur
ve
y da
ta
c
ove
r
in
g 6814 M
or
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a
n
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tu
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nd
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c
or
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e
s
ponding
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h
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ge
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twe
e
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he
s
ha
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d i
de
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ie
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um
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S
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D
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om
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or
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of
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on "
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ng c
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s
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une
qua
l
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r
io
us
r
e
gi
ons
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or
oc
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o,
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e
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e
d
a
r
e
pr
e
s
e
nt
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ti
ve
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a
ndom
s
a
m
pl
in
g
m
e
th
od
w
he
r
e
by
65
te
a
c
h
e
r
s
f
r
om
e
a
c
h
r
e
s
pe
c
ti
ve
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e
gi
on
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e
r
e
c
hos
e
n.
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hi
s
s
a
m
pl
in
g
te
c
hni
que
e
na
bl
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th
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a
c
hi
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m
e
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of
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ul
ts
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lo
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ly
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li
gne
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e
popula
ti
on
m
e
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n
a
nd
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nc
e
m
a
de
pos
s
ib
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e
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ni
ngf
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om
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r
is
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ons
.
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or
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s
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a
r
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r
s
w
ho
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s
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M
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a
n
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ta
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or
th
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ow
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e
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c
c
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th
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ta
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nt
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th
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ic
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E
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s
it
e
'
s
M
or
oc
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o
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di
vi
dua
l
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t
ht
tp
s
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w
.o
e
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d.or
g/
pi
s
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ta
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018da
ta
ba
s
e
/.
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he
s
it
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of
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e
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s
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us
e
r
-
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y
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ovi
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he
da
ta
s
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t
s
tr
uc
tu
r
e
a
nd va
r
ia
bl
e
s
.
2.2.
S
t
u
d
yi
n
g
t
h
e
i
m
p
ac
t
of
t
e
ac
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s
q
u
al
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s
on
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t
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s
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as
s
e
s
s
m
e
n
t
T
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in
it
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ta
ge
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our
s
tu
dy
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to
c
a
r
r
y
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t
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pr
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r
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f
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r
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hi
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e
nt
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w
it
h
th
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P
I
S
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da
ta
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n
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ly
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oye
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(
A
N
O
V
A
)
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om
m
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a
m
ong
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in
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r
it
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r
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r
ly
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r
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nt
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in
s
ta
ti
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ti
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r
s
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om
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c
a
n
ut
il
iz
e
th
is
a
na
ly
ti
c
a
l
to
ol
[
21]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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por
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hi
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t
in
f
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tt
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r
ns
or
out
li
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s
.
2.3. Clu
s
t
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r
in
g t
e
ac
h
e
r
s
(
c
e
n
t
r
oi
d
n
e
u
r
al
n
e
t
w
or
k
al
gor
it
h
m
)
U
ns
upe
r
vi
s
e
d
le
a
r
ni
ng
is
a
s
ubs
e
t
of
m
a
c
hi
ne
le
a
r
ni
ng
th
a
t
d
e
a
ls
w
it
h
th
e
a
n
a
ly
s
is
of
d
a
ta
w
it
hout
a
ny
e
xpl
ic
it
la
be
ls
or
ta
r
ge
t
va
lu
e
s
,
in
th
e
hope
o
f
id
e
nt
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f
yi
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nde
r
ly
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g
pa
tt
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ns
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tr
uc
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e
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ti
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hi
ps
c
ont
a
in
e
d
w
it
hi
n
th
e
da
ta
it
s
e
lf
[
22]
.
C
lu
s
te
r
in
g,
a
f
unda
m
e
nt
a
l
te
c
hni
que
unde
r
uns
upe
r
vi
s
e
d
le
a
r
ni
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i
s
a
popula
r
m
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ie
d
a
c
r
os
s
s
c
i
e
nt
if
ic
,
te
c
hnol
ogi
c
a
l,
a
nd
c
om
m
e
r
c
ia
l
f
ie
ld
s
to
a
na
ly
z
e
m
ul
ti
va
r
ia
te
da
ta
.
T
he
pr
oc
e
s
s
e
nt
a
il
s
th
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di
vi
s
io
n
of
da
ta
in
to
s
ig
ni
f
ic
a
nt
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o
ups
or
c
lu
s
te
r
s
de
r
iv
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c
c
or
di
ng
to
in
he
r
e
nt
s
im
il
a
r
it
ie
s
or
di
f
f
e
r
e
nc
e
s
[
23]
.
T
he
r
e
h
a
s
be
e
n a
la
r
ge
body
of
w
or
k
on
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s
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nd a
s
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r
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ul
t,
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ous
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lg
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it
hm
s
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v
e
be
e
n
pr
opos
e
d
th
a
t
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iz
e
di
f
f
e
r
e
nt
a
ppr
oa
c
he
s
to
e
nh
a
nc
in
g
e
f
f
ic
ie
nt
da
ta
c
a
te
gor
iz
a
ti
on
[
24]
.
C
lu
s
te
r
in
g
a
lg
or
it
hm
s
in
uns
upe
r
vi
s
e
d
le
a
r
ni
ng
ut
il
iz
e
e
poc
hs
a
nd
w
e
ig
ht
s
.
A
n
e
poc
h
m
e
a
ns
a
s
in
gl
e
pa
s
s
or
it
e
r
a
ti
on
ove
r
th
e
w
hol
e
da
ta
s
e
t
w
hi
le
tr
a
in
in
g,
in
w
hi
c
h
th
e
a
lg
o
r
it
hm
s
e
que
nt
ia
ll
y
upda
te
s
da
ta
poi
nt
s
'
or
c
lu
s
te
r
s
'
w
e
ig
ht
s
w
it
h
th
e
a
im
of
e
nha
n
c
in
g
th
e
c
lu
s
te
r
in
g
out
c
om
e
.
T
he
w
e
ig
ht
s
a
r
e
th
e
r
e
le
va
nc
e
or
im
por
ta
nc
e
of
e
ve
r
y
da
ta
poi
nt
f
or
c
lu
s
te
r
in
g.
B
y
a
dj
us
ti
ng
w
e
ig
ht
s
a
t
e
ve
r
y
e
poc
h,
c
lu
s
te
r
in
g
a
lg
or
it
hm
s
a
tt
e
m
pt
to
m
in
im
iz
e
a
s
pe
c
if
ie
d
obj
e
c
ti
ve
f
unc
ti
on,
e
.g.,
w
it
hi
n
-
c
lu
s
te
r
di
s
ta
nc
e
or
be
twe
e
n
-
c
lu
s
te
r
di
s
ta
nc
e
.
T
he
it
e
r
a
ti
ve
pr
oc
e
dur
e
is
r
e
pe
a
te
d
u
nt
il
c
onve
r
ge
nc
e
,
i.
e
.,
th
e
a
lg
or
it
hm
s
ta
bi
li
z
e
s
a
nd t
he
c
lu
s
te
r
in
g s
ol
ut
io
n doe
s
not
c
ha
nge
s
ig
ni
f
ic
a
nt
ly
.
T
he
us
e
of
c
lu
s
te
r
in
g
a
lg
or
it
hm
s
,
a
lo
ng
w
it
h
th
e
ir
m
a
na
ge
m
e
nt
of
e
poc
hs
a
nd
w
e
ig
ht
s
,
e
na
bl
e
s
r
e
s
e
a
r
c
he
r
s
a
nd
pr
a
c
ti
ti
one
r
s
to
e
xt
r
a
c
t
hi
dde
n
pa
tt
e
r
ns
,
de
ve
lo
p
in
s
ig
ht
s
,
a
nd
a
id
de
c
is
io
n
-
m
a
ki
ng
in
num
e
r
ous
a
r
e
a
s
s
uc
h
a
s
da
ta
m
in
in
g,
pa
tt
e
r
n
r
e
c
ogni
ti
on,
im
a
ge
pr
oc
e
s
s
in
g,
m
a
r
ke
t
s
e
gm
e
nt
a
ti
on,
a
nd
m
a
ny
ot
he
r
s
.
D
e
e
p
le
a
r
ni
ng
te
c
hni
que
s
ha
ve
a
ls
o
s
how
n
pr
om
is
e
in
ha
ndl
in
g
hi
gh
-
di
m
e
ns
io
na
l
m
a
th
e
m
a
ti
c
a
l
s
ys
te
m
s
,
de
m
ons
tr
a
ti
ng
th
e
gr
ow
in
g
ve
r
s
a
ti
li
ty
of
ne
ur
a
l
m
ode
ls
in
s
ol
vi
ng
c
om
pl
e
x
pr
obl
e
m
s
[
25]
.
A
m
ong
a
num
be
r
of
c
lu
s
te
r
in
g
a
lg
or
it
hm
s
,
th
e
c
e
nt
r
oi
d
ne
ur
a
l
ne
twor
k
(
C
e
nt
N
N
)
is
a
n
uns
upe
r
vi
s
e
d
c
om
pe
ti
ti
ve
le
a
r
ni
ng
a
lg
or
it
hm
ba
s
e
d
on
th
e
c
onve
nt
io
n
a
l
k
-
m
e
a
ns
c
lu
s
te
r
in
g
a
lg
or
it
hm
in
tr
oduc
e
d
by
P
a
r
k
[
26]
.
I
n
e
ve
r
y
pa
s
s
,
th
e
C
e
nt
N
N
c
om
put
e
s
th
e
c
lu
s
te
r
c
e
nt
r
oi
ds
of
th
e
in
put
da
ta
ve
c
to
r
s
.
W
he
n
a
n
in
put
da
ta
poi
nt
,
x,
is
pr
e
s
e
nt
e
d
to
th
e
n
e
twor
k,
th
e
ne
ur
on
th
a
t
d
e
m
ons
tr
a
te
s
th
e
m
i
ni
m
um
di
s
ta
nc
e
to
x
i
s
s
e
le
c
te
d
a
s
th
e
w
in
ne
r
ne
ur
on a
t
e
poc
h (
k)
.
T
he
ne
ur
on
id
e
nt
if
ie
d
a
s
th
e
vi
c
to
r
dur
in
g
e
poc
h
(
k
-
1)
but
de
s
ig
na
te
d
a
s
th
e
lo
s
e
r
in
e
poc
h
(
k)
is
te
r
m
e
d
th
e
lo
s
e
r
.
T
he
C
e
nt
N
N
m
odi
f
ie
s
it
s
w
e
ig
ht
s
s
ol
e
ly
w
he
n
th
e
out
put
ne
ur
on'
s
s
ta
tu
s
f
or
th
e
la
te
s
t
da
ta
di
ve
r
ge
s
f
r
om
it
s
c
ondi
ti
on
in
th
e
pr
e
c
e
di
ng
e
poc
h.
F
ur
th
e
r
m
or
e
,
th
e
C
e
nt
N
N
c
om
m
e
nc
e
s
w
it
h
two
in
it
ia
l
c
lu
s
te
r
s
a
nd
in
c
r
e
m
e
nt
a
ll
y
a
ugm
e
nt
s
th
e
num
be
r
of
c
lu
s
te
r
s
to
a
tt
a
in
th
e
opt
im
a
l
c
lu
s
te
r
in
g
out
c
om
e
.
I
n
c
om
pa
r
is
on
w
it
h
c
onve
nt
io
na
l
c
lu
s
te
r
in
g
te
c
hni
qu
e
s
s
uc
h
a
s
s
e
lf
-
or
ga
ni
z
in
g
m
a
ps
(
S
O
M
)
[
27]
–
[
29]
or
k
-
m
e
a
ns
[
24]
,
[
30]
,
th
e
C
e
nt
N
N
a
ppr
oa
c
h
ha
s
a
num
be
r
of
b
e
ne
f
it
s
in
uns
upe
r
vi
s
e
d
c
om
pe
ti
ti
ve
le
a
r
ni
ng.
A
lt
hough
S
O
M
s
a
ls
o
a
ppl
y
a
ne
ur
a
l
ne
twor
k
s
tr
uc
tu
r
e
to
f
or
m
a
ne
ur
on
gr
id
a
nd
a
da
pt
w
e
ig
ht
s
to
pr
ope
r
l
y
m
a
p
in
put
da
ta
to
pol
ogi
c
a
ll
y,
th
e
y
m
a
y
s
uf
f
e
r
f
r
om
th
e
in
f
lu
e
n
c
e
of
in
it
ia
l
le
a
r
ni
ng
r
a
te
s
a
nd
m
a
y
pot
e
nt
ia
ll
y
c
onve
r
ge
to
poor
s
ol
ut
io
ns
[
31
]
.
C
onve
r
s
e
ly
,
th
e
k
-
m
e
a
ns
a
lg
o
r
it
hm
is
a
m
o
r
e
s
tr
a
ig
ht
f
o
r
w
a
r
d
a
ppr
oa
c
h
th
a
t
a
s
s
ig
ns
da
t
a
to
a
pr
e
de
te
r
m
in
e
d
num
be
r
of
c
lu
s
te
r
s
de
pe
ndi
ng
on
th
e
pr
ovi
de
d
c
e
nt
r
oi
ds
.
N
e
ve
r
th
e
le
s
s
,
it
c
a
n
be
in
f
lu
e
nc
e
d
by
th
e
c
hoi
c
e
of
in
it
ia
l
c
e
nt
r
oi
ds
[
32]
.
I
n
c
ont
r
a
s
t,
C
e
nt
N
N
doe
s
not
r
e
ly
on
pr
e
de
te
r
m
in
e
d
le
a
r
ni
ng
ga
in
s
c
he
dul
e
s
or
f
ix
e
d
r
e
pe
ti
ti
ons
,
pr
ovi
di
ng
gr
e
a
te
r
f
le
xi
bi
li
ty
a
nd
de
m
ons
tr
a
ti
ng
s
upe
r
io
r
pe
r
f
or
m
a
nc
e
i
n va
r
io
us
e
xpe
r
im
e
nt
s
.
I
n
our
s
tu
dy,
w
e
f
ir
s
t
e
s
ti
m
a
te
d
th
e
a
ppr
opr
ia
te
num
be
r
of
c
lu
s
te
r
s
ne
c
e
s
s
a
r
y
f
or
e
f
f
e
c
ti
ve
da
ta
c
lu
s
te
r
in
g.
S
ubs
e
que
nt
ly
,
w
e
ut
il
iz
e
d
th
e
C
e
nt
N
N
a
lg
or
it
hm
t
o
gr
oup
te
a
c
he
r
s
in
to
c
lu
s
te
r
s
ba
s
e
d
on
th
e
ir
qua
li
f
ic
a
ti
ons
,
w
hi
c
h
im
pa
c
t
s
tu
de
nt
a
s
s
e
s
s
m
e
nt
.
C
lu
s
te
r
0
r
e
p
r
e
s
e
nt
s
te
a
c
h
e
r
s
w
it
h
qua
li
ti
e
s
th
a
t
ne
ga
ti
ve
ly
a
f
f
e
c
t
s
tu
de
nt
a
c
hi
e
ve
m
e
nt
,
w
hi
le
c
lu
s
te
r
1
c
om
pr
is
e
s
te
a
c
he
r
s
w
ho
pos
it
iv
e
ly
c
ont
r
ib
ut
e
to
s
tu
de
nt
pe
r
f
or
m
a
nc
e
.
2.4. Re
gi
on
s
'
t
e
ac
h
e
r
s
'
q
u
al
it
ie
s
l
e
ve
l
A
f
te
r
c
lu
s
te
r
in
g t
e
a
c
he
r
s
i
nt
o n c
lu
s
te
r
s
(
a
s
s
um
in
g n=
2 ba
s
e
d o
n t
he
e
lb
ow
gr
a
ph plot
)
, w
e
s
or
te
d t
he
c
lu
s
te
r
e
d
da
ta
by
a
r
e
a
,
th
e
n
c
a
lc
ul
a
t
e
d
th
e
num
be
r
of
in
s
tr
uc
to
r
s
a
s
s
ig
ne
d
to
c
lu
s
t
e
r
0
a
nd
th
e
num
b
e
r
of
te
a
c
he
r
s
a
s
s
ig
ne
d
to
c
lu
s
te
r
1
f
o
r
e
a
c
h
r
e
gi
on.
A
f
te
r
w
a
r
ds
,
w
e
c
a
lc
ul
a
te
d
th
e
di
f
f
e
r
e
nc
e
be
twe
e
n
th
e
two
c
ount
s
a
nd
th
e
n
s
c
a
le
d
it
to
f
a
ll
w
it
hi
n
th
e
r
a
nge
of
-
1
to
1,
r
e
la
ti
ve
to
th
e
di
f
f
e
r
e
nc
e
s
in
c
ount
s
s
e
e
n
in
ot
he
r
r
e
gi
ons
.
F
or
th
is
pur
pos
e
,
w
e
pr
opos
e
D
a
s
"
th
e
di
f
f
e
r
e
nc
e
le
ve
l
be
twe
e
n
te
a
c
he
r
s
a
s
s
ig
ne
d
to
c
lu
s
te
r
1
c
om
pa
r
e
d t
o t
e
a
c
he
r
s
a
s
s
ig
ne
d t
o
c
lu
s
te
r
0"
. I
t
is
c
a
lc
ul
a
te
d a
c
c
or
di
ng t
o t
he
f
ol
lo
w
in
g f
o
r
m
ul
a
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 14, No. 5, O
c
to
be
r
2025
:
3647
-
3655
3650
=
1
−
0
[
1
+
0
2
]
(
1)
W
he
r
e
C
1
is
num
be
r
of
te
a
c
he
r
s
a
s
s
ig
ne
d
to
c
lu
s
te
r
1
a
nd
C
0
is
num
be
r
of
te
a
c
he
r
s
a
s
s
ig
ne
d
to
c
lu
s
te
r
0.
A
ne
ga
ti
ve
va
lu
e
of
th
e
s
c
a
le
d
D
va
lu
e
in
di
c
a
te
s
th
a
t
th
e
r
e
gi
on
in
que
s
ti
on
ha
s
a
hi
gh
num
be
r
of
te
a
c
he
r
s
w
hos
e
qua
li
ti
e
s
n
e
ga
ti
ve
ly
i
m
pa
c
t
s
tu
de
nt
a
s
s
e
s
s
m
e
nt
. M
os
t
te
a
c
he
r
s
i
n t
hi
s
r
e
gi
on w
e
r
e
a
s
s
ig
ne
d t
o c
lu
s
t
e
r
0.
3.
R
E
S
U
L
T
S
A
N
D
D
I
S
C
U
S
S
I
O
N
3.1.
S
t
u
d
yi
n
g
t
h
e
i
m
p
ac
t
of
t
e
ac
h
e
r
’
s
q
u
al
it
ie
s
on
s
t
u
d
e
n
t
s
’
as
s
e
s
s
m
e
n
t
T
a
bl
e
1
pr
e
s
e
nt
s
th
e
r
e
s
ul
ts
,
de
m
ons
tr
a
ti
ng
a
s
ig
ni
f
ic
a
nt
a
s
s
oc
ia
ti
on
be
twe
e
n
te
a
c
he
r
qua
li
ti
e
s
a
nd
s
tu
de
nt
pe
r
f
or
m
a
nc
e
.
T
h
e
A
N
O
V
A
t
e
s
t
c
onduc
te
d
r
e
ve
a
le
d
a
s
t
a
ti
s
ti
c
a
ll
y
s
ig
ni
f
ic
a
nt
r
e
la
ti
ons
hi
p
(p
-
va
lu
e
le
s
s
th
a
n
th
e
s
ig
ni
f
ic
a
nc
e
le
v
e
l
of
0.05)
be
twe
e
n
th
e
in
de
pe
nde
nt
v
a
r
ia
bl
e
,
te
a
c
h
e
r
qua
li
ti
e
s
a
nd
qua
li
f
ic
a
ti
ons
,
a
nd
th
e
de
pe
nde
nt
va
r
ia
bl
e
,
s
tu
de
nt
s
'
pe
r
f
or
m
a
nc
e
in
th
e
P
I
S
A
te
s
t
of
2018.
T
hi
s
f
in
di
ng
s
ugge
s
ts
th
a
t
f
a
c
to
r
s
s
u
c
h
a
s
in
it
ia
l
te
a
c
hi
ng
qua
li
f
ic
a
ti
ons
,
e
m
pl
oym
e
nt
s
ta
tu
s
,
a
nd
c
om
pl
e
ti
on
of
a
te
a
c
he
r
tr
a
in
in
g
pr
ogr
a
m
ha
ve
a
n
im
pa
c
t
on
s
tu
de
nt
s
'
a
c
hi
e
ve
m
e
nt
in
t
he
P
I
S
A
te
s
t.
M
or
e
ove
r
,
th
e
F
-
va
lu
e
obt
a
in
e
d
f
r
om
th
e
A
N
O
V
A
te
s
t
c
onf
ir
m
s
a
s
tr
ong
r
e
la
ti
ons
hi
p
b
e
twe
e
n
th
e
in
de
pe
nde
nt
va
r
ia
bl
e
(
te
a
c
he
r
qua
li
ty
)
a
nd
th
e
de
pe
nde
nt
va
r
ia
bl
e
(
s
tu
de
nt
a
c
hi
e
ve
m
e
nt
in
th
e
P
I
S
A
te
s
t
of
2018)
.
I
n
o
th
e
r
w
or
ds
,
i
t
in
di
c
a
te
s
th
a
t
th
e
va
r
ia
ti
on
obs
e
r
ve
d
be
twe
e
n
th
e
s
a
m
pl
e
m
e
a
ns
i
s
s
ig
ni
f
ic
a
nt
ly
hi
ghe
r
th
a
n
th
e
va
r
ia
ti
on
w
it
hi
n
th
e
s
a
m
pl
e
s
,
pr
ovi
di
ng e
vi
de
nc
e
t
o r
e
je
c
t
th
e
nul
l
hypothe
s
is
.
T
a
bl
e
1. O
ne
-
w
a
y A
N
O
V
A
r
e
s
ul
ts
on t
e
a
c
he
r
qua
li
ti
e
s
a
nd s
tu
d
e
nt
pe
r
f
or
m
a
nc
e
T
e
a
c
he
r
qua
l
i
t
i
e
s
F
s
c
or
e
p
-
va
l
ue
I
ni
t
i
a
l
t
e
a
c
hi
ng qua
l
i
f
i
c
a
t
i
ons
225.22
1.10e
-
145
E
m
pl
oym
e
nt
s
t
a
t
us
159.58
3.18e
-
103
C
om
pl
e
t
i
on of
a
t
e
a
c
he
r
e
duc
a
t
i
on or
t
r
a
i
ni
ng pr
ogr
a
m
248.11
3.01e
-
108
A
c
c
or
di
ng
to
th
e
f
in
di
ngs
in
F
ig
ur
e
1,
w
e
c
a
n
s
e
e
th
a
t
th
e
c
om
pl
e
ti
on
of
a
te
a
c
he
r
e
duc
a
ti
on
or
tr
a
in
in
g
pr
ogr
a
m
la
s
ti
ng
lo
nge
r
th
a
n
1
ye
a
r
ha
s
a
s
ubs
ta
nt
ia
l
im
pa
c
t
on
a
c
a
de
m
ic
pr
ogr
e
s
s
.
F
ur
th
e
r
m
or
e
,
s
tu
de
nt
s
w
ho
a
r
e
ta
ught
by
te
a
c
he
r
s
w
it
h
in
it
ia
l
te
a
c
hi
ng
qua
li
f
ic
a
ti
ons
f
r
om
a
n
e
li
gi
bl
e
e
duc
a
ti
ona
l
in
s
ti
tu
te
de
m
ons
tr
a
te
hi
ghe
r
le
ve
ls
of
c
ogni
ti
ve
s
ki
ll
s
.
R
e
s
pe
c
ti
ve
ly
,
it
c
a
n
be
in
f
e
r
r
e
d
th
a
t
e
m
pl
oym
e
nt
s
ta
tu
s
h
a
s
a
be
ne
f
ic
ia
l
im
pa
c
t
on s
tu
de
nt
a
c
a
de
m
ic
a
bi
li
ty
.
F
ig
ur
e
1. B
oxpl
ot
s
di
s
pl
a
yi
ng r
e
la
ti
ons
hi
p be
twe
e
n
s
tu
de
nt
s
’
pe
r
f
or
m
a
nc
e
a
nd t
e
a
c
he
r
’
s
qua
li
ti
e
s
3.2.
C
lu
s
t
e
r
in
g
3.2.1. Dat
a p
r
e
p
r
oc
e
s
s
in
g
F
ol
lo
w
in
g
da
ta
nor
m
a
li
z
a
ti
on,
w
e
a
ppl
ie
d
pr
in
c
ip
a
l
c
om
po
ne
nt
a
na
ly
s
is
(
P
C
A
)
to
r
e
duc
e
th
e
di
m
e
ns
io
na
li
ty
of
th
e
da
ta
s
e
t
w
hi
le
pr
e
s
e
r
vi
ng
s
ig
ni
f
ic
a
nt
va
r
ia
nc
e
.
W
e
s
e
le
c
te
d
pr
in
c
ip
a
l
c
om
pone
nt
s
(
P
C
s
)
w
it
h
a
c
um
ul
a
ti
ve
e
xpl
a
in
e
d
va
r
ia
nc
e
r
a
ti
o e
xc
e
e
di
ng
0.90
to
e
ns
ur
e
th
a
t
th
e
r
e
duc
e
d
da
ta
s
e
t
r
e
ta
in
e
d
m
o
s
t
of
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
A
c
om
pe
ti
ti
v
e
l
e
ar
ni
ng appr
oac
h t
o e
nhan
c
in
g t
e
ac
he
r
e
ff
e
c
ti
v
e
ne
s
s
and
s
tu
de
nt
…
(
I
ly
as
T
am
m
ouc
h
)
3651
th
e
or
ig
in
a
l
in
f
o
r
m
a
ti
on.
B
a
s
e
d
on
th
i
s
c
r
i
te
r
io
n
, w
e
c
h
o
s
e
two
P
C
s
f
r
o
m
t
h
e
nor
m
a
l
iz
e
d
da
ta
, w
hi
c
h
e
f
f
e
c
ti
v
e
l
y
c
a
pt
ur
e
d t
he
k
e
y
p
a
tt
e
r
n
s
r
e
l
e
va
nt
to
th
e
C
e
nt
N
N
’
s
p
e
r
f
or
m
a
nc
e
i
n e
va
lu
a
ti
ng
t
e
a
c
h
e
r
e
f
f
e
c
ti
v
e
n
e
s
s
.
3.2.2. E
s
t
im
at
in
g t
h
e
n
u
m
b
e
r
of
c
lu
s
t
e
r
s
A
f
te
r
c
ha
r
ti
ng
th
e
c
u
r
ve
a
s
s
how
n
in
F
ig
ur
e
2,
w
e
s
e
le
c
te
d
2
a
s
th
e
c
ut
-
of
f
poi
nt
.
A
lt
hough
th
e
w
it
hi
n
-
c
lu
s
te
r
s
um
of
s
qua
r
e
s
(
W
C
SS
)
is
s
ti
ll
de
c
r
e
a
s
in
g,
it
do
e
s
n'
t
s
e
e
m
to
be
doi
ng
s
o
a
t
a
bi
g
e
nough
r
a
te
.
T
he
r
e
f
or
e
, a
ddi
ng mor
e
c
lu
s
te
r
s
i
s
not
j
u
s
ti
f
ie
d by the
a
dde
d c
o
m
pl
e
xi
ty
.
F
ig
ur
e
2. E
s
ti
m
a
ti
ng numbe
r
of
c
lu
s
te
r
s
us
in
g e
lb
ow
m
e
th
od
3.2.3. Clu
s
t
e
r
in
g t
e
ac
h
e
r
s
A
f
te
r
e
s
ti
m
a
ti
ng
th
e
num
be
r
of
c
lu
s
te
r
s
to
b
e
us
e
d
in
th
is
in
v
e
s
ti
ga
ti
on.
T
h
e
two
P
C
s
a
r
e
th
e
n
f
e
d
in
to
th
e
C
e
nt
N
N
,
a
ll
ow
in
g
us
to
gr
oup
te
a
c
he
r
s
in
to
gr
oups
ba
s
e
d
on
th
e
ir
qua
li
ti
e
s
f
a
c
to
r
s
th
a
t
in
f
lu
e
nc
e
a
c
a
de
m
ic
a
c
hi
e
v
e
m
e
nt
.
W
e
w
a
nt
to
id
e
nt
if
y
te
a
c
he
r
s
w
ho
ha
ve
c
ha
r
a
c
te
r
is
ti
c
s
th
a
t
ha
ve
a
de
tr
im
e
nt
a
l
im
pa
c
t
on
s
tu
de
nt
pr
ogr
e
s
s
a
nd
br
in
g
th
e
m
to
ge
th
e
r
to
e
s
ta
bl
is
h
s
tr
a
te
gi
e
s
f
or
f
ur
th
e
r
e
duc
a
ti
ona
l
r
e
f
or
m
.
A
c
c
or
di
ng
to
th
e
ba
r
c
ha
r
t
di
s
pl
a
ye
d
in
F
ig
u
r
e
3,
w
hi
c
h
c
om
pa
r
e
s
th
e
tw
o
c
lu
s
te
r
s
-
th
e
num
be
r
of
s
tu
de
nt
s
a
s
s
ig
ne
d
to
e
a
c
h
c
lu
s
te
r
a
nd
th
e
“
s
tu
de
nt
pe
r
f
or
m
a
nc
e
”
m
e
tr
ic
de
r
iv
e
d
f
r
o
m
th
e
m
e
a
n
pl
a
us
ib
le
va
lu
e
s
f
or
m
a
th
,
s
c
ie
nc
e
,
a
nd
r
e
a
di
ng
,
s
our
c
e
d
f
r
om
th
e
in
it
ia
l
in
te
gr
a
te
d
da
ta
s
e
t
-
it
c
a
n
b
e
s
e
e
n
th
a
t
te
a
c
he
r
s
w
it
h
qua
li
ti
e
s
th
a
t
ha
ve
a
ne
ga
ti
ve
im
pa
c
t
on
s
tu
de
nt
a
c
hi
e
ve
m
e
nt
li
e
in
c
lu
s
te
r
0,
w
h
e
r
e
a
s
c
lu
s
te
r
1
de
s
c
r
ib
e
s
t
e
a
c
he
r
s
w
ho
ha
v
e
pos
it
iv
e
e
f
f
e
c
ts
on s
tu
de
nt
a
c
hi
e
ve
m
e
nt
.
F
ig
ur
e
3. B
a
r
c
ha
r
t
of
c
lu
s
te
r
in
g da
ta
us
in
g
C
e
nt
N
N
3.3. E
val
u
at
io
n
an
d
c
om
p
ar
is
on
of
t
h
r
e
e
al
gor
it
h
m
s
T
a
bl
e
2
pr
e
s
e
nt
s
th
e
r
e
s
ul
ts
of
a
c
om
pa
r
a
ti
ve
a
na
ly
s
is
of
t
hr
e
e
a
lg
or
it
hm
s
,
e
va
lu
a
te
d
ba
s
e
d
on
m
ul
ti
pl
e
pe
r
f
or
m
a
nc
e
m
e
tr
ic
s
.
T
h
e
s
tu
dy
e
xa
m
in
e
d
th
e
im
pa
c
t
of
th
e
s
e
a
lg
or
it
hm
s
on
c
lu
s
te
r
in
g
te
a
c
h
e
r
s
by
c
a
lc
ul
a
ti
ng
th
e
s
il
houe
tt
e
c
oe
f
f
ic
ie
nt
(
S
C
)
,
C
a
li
ns
ki
-
H
a
r
a
ba
s
z
in
de
x
(
C
H
I
)
,
a
nd
D
a
vi
e
s
-
B
oul
di
n
in
de
x
(
D
B
I
)
.
T
he
f
in
di
ngs
in
di
c
a
t
e
th
a
t
th
e
C
e
nt
N
N
out
p
e
r
f
or
m
e
d
bot
h
th
e
S
O
M
a
nd
k
-
m
e
a
n
s
a
lg
or
it
hm
s
,
a
s
e
vi
de
n
c
e
d
by
hi
ghe
r
S
C
,
C
H
I
,
a
nd
D
B
I
va
lu
e
s
.
T
hi
s
s
ugge
s
t
s
th
a
t
th
e
C
e
nt
N
N
is
m
or
e
e
f
f
e
c
ti
ve
in
gr
oupi
ng
te
a
c
he
r
s
w
it
h
c
om
pa
r
a
bl
e
a
tt
r
ib
ut
e
s
a
nd qua
li
f
ic
a
ti
ons
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 14, No. 5, O
c
to
be
r
2025
:
3647
-
3655
3652
T
a
bl
e
2. A
na
ly
s
is
a
nd c
om
p
a
r
is
on of
t
hr
e
e
c
lu
s
te
r
in
g a
lg
or
it
hm
s
us
in
g S
C
, C
H
I
, a
nd D
B
I
A
l
gor
i
t
hm
SC
C
H
I
D
B
I
k
-
m
e
a
ns
0.51
68138.47
0.97
C
e
nt
N
N
0.53
68615.31
0.95
S
O
M
0.50
65221.55
1.14
3.4.
R
e
gi
on
s
'
t
e
ac
h
e
r
s
’
q
u
al
it
ie
s
l
e
ve
l
T
o
a
s
s
e
s
s
a
nd
c
om
pa
r
e
th
e
qua
li
ty
le
ve
ls
of
te
a
c
he
r
s
a
c
r
o
s
s
di
f
f
e
r
e
nt
r
e
gi
ons
,
th
e
f
ol
lo
w
in
g s
te
ps
a
r
e
c
onduc
te
d:
i)
s
te
p 1
f
or
e
a
c
h r
e
gi
on, we
qua
nt
if
y t
he
numbe
r
of
te
a
c
he
r
s
a
s
s
ig
ne
d t
o c
lu
s
te
r
0 a
nd c
lu
s
t
e
r
1, a
s
il
lu
s
tr
a
te
d
in
T
a
bl
e
3
a
nd
ii
)
s
te
p
2
u
s
in
g
th
e
(
1)
,
w
e
c
a
lc
ul
a
te
D
a
s
“
th
e
di
f
f
e
r
e
nc
e
le
ve
l
be
twe
e
n
th
e
te
a
c
he
r
s
a
s
s
ig
ne
d t
o c
lu
s
te
r
1 c
om
pa
r
e
d t
o t
e
a
c
h
e
r
s
a
s
s
ig
ne
d t
o c
lu
s
t
e
r
0”
.
T
he
f
in
di
ngs
pr
e
s
e
nt
e
d
in
T
a
bl
e
4
pr
ovi
de
a
w
in
dow
in
to
th
e
c
om
pl
e
x
f
r
a
m
e
w
or
k
th
a
t
is
th
e
e
duc
a
ti
ona
l
la
nd
s
c
a
pe
a
c
r
os
s
di
f
f
e
r
e
nt
r
e
gi
ons
.
N
ot
a
bl
y,
th
e
y
of
f
e
r
va
lu
a
bl
e
in
s
ig
ht
s
in
to
th
e
di
s
tr
ib
ut
io
n
of
te
a
c
he
r
s
w
ho
s
e
qu
a
li
ti
e
s
s
ig
ni
f
ic
a
nt
ly
in
f
lu
e
nc
e
s
tu
de
nt
s
'
a
s
s
e
s
s
m
e
nt
s
.
T
hi
s
hi
ghl
ig
ht
s
th
e
c
r
it
ic
a
l
r
ol
e
of
e
duc
a
to
r
s
i
n s
ha
pi
ng t
he
e
duc
a
ti
ona
l
out
c
om
e
s
of
young mi
nds
.
T
a
bl
e
3. N
um
be
r
of
t
e
a
c
he
r
s
a
s
s
ig
ne
d t
o c
lu
s
te
r
0 a
nd t
e
a
c
he
r
s
a
s
s
ig
ne
d t
o c
lu
s
te
r
1
R
e
gi
on
N
um
be
r
of
t
e
a
c
he
r
s
a
s
s
i
gn
e
d t
o (
c
l
us
t
e
r
0)
N
um
be
r
of
t
e
a
c
he
r
s
a
s
s
i
gn
e
d t
o (
c
l
us
t
e
r
1)
T
a
nge
r
-
T
e
t
oua
n
-
A
l
H
oc
e
i
m
a
31
34
O
r
i
e
nt
a
l
33
32
F
è
s
-
M
e
knè
s
31
34
R
a
ba
t
-
S
a
l
é
-
K
é
ni
t
r
a
25
40
B
é
ni
M
e
l
l
a
l
-
K
hé
ni
f
r
a
43
22
C
a
s
a
bl
a
n
c
a
-
S
e
t
t
a
t
18
47
M
a
r
r
a
ke
c
h
-
S
a
f
i
39
26
D
r
â
a
-
T
a
f
i
l
a
l
e
t
30
35
S
ous
s
-
M
a
s
s
a
31
34
G
ue
l
m
i
m
-
O
ue
d N
oun
34
31
L
a
a
youne
-
S
a
ki
a
E
l
H
a
m
r
a
22
43
E
dda
khl
a
-
O
ue
d
E
dda
ha
b
44
21
T
a
bl
e
4. dif
f
e
r
e
nc
e
be
twe
e
n t
he
nu
m
be
r
of
t
e
a
c
he
r
s
i
n e
a
c
h c
lu
s
te
r
R
e
gi
on
D
T
a
nge
r
-
T
e
t
oua
n
-
A
l
H
oc
e
i
m
a
0.09
O
r
i
e
nt
a
l
-
0.03
F
è
s
-
M
e
knè
s
0.09
R
a
ba
t
-
S
a
l
é
-
K
é
ni
t
r
a
0.46
B
é
ni
M
e
l
l
a
l
-
K
hé
ni
f
r
a
-
0.64
C
a
s
a
bl
a
n
c
a
-
S
e
t
t
a
t
0.89
M
a
r
r
a
ke
c
h
-
S
a
f
i
-
0.40
D
r
â
a
-
T
a
f
i
l
a
l
e
t
0.15
S
ous
s
-
M
a
s
s
a
0.09
G
ue
l
m
i
m
-
O
ue
d N
oun
-
0.09
L
a
a
youne
-
S
a
ki
a
E
l
H
a
m
r
a
0.64
E
dda
khl
a
-
O
ue
d E
dda
ha
b
-
0.70
E
xa
m
in
in
g
th
e
r
e
gi
ona
l
va
r
ia
ti
ons
r
e
ve
a
ls
a
c
ons
id
e
r
a
bl
e
va
r
ia
ti
on
in
th
e
di
s
tr
ib
ut
io
n
of
te
a
c
he
r
s
w
ho
pos
s
e
s
s
bot
h
po
s
it
iv
e
a
nd
ne
ga
ti
ve
e
f
f
e
c
ts
on
s
tu
de
nt
s
'
r
a
t
in
gs
.
'
C
a
s
a
bl
a
nc
a
-
S
e
tt
a
t'
i
s
one
of
th
e
r
e
gi
on
s
th
a
t
s
ta
nds
out
c
le
a
r
ly
w
it
h
a
hi
gh
r
a
te
of
te
a
c
he
r
s
w
ho
po
s
s
e
s
s
c
ha
r
a
c
te
r
is
ti
c
s
w
hi
c
h
e
xe
r
t
pos
it
iv
e
e
f
f
e
c
ts
on
th
e
ir
s
tu
de
nt
s
'
a
c
a
de
m
ic
de
v
e
lo
pm
e
nt
.
T
hi
s
f
in
di
ng
hi
ghl
ig
ht
s
th
e
pos
s
ib
il
it
y
of
r
e
pr
oduc
in
g
a
nd
e
xpa
ndi
ng
th
e
s
tr
a
te
gi
e
s
e
m
pl
oye
d
by
th
e
s
e
te
a
c
he
r
s
in
or
de
r
to
im
p
r
o
ve
th
e
ge
ne
r
a
l
s
ta
nda
r
d
of
e
duc
a
ti
on
a
c
r
os
s
di
f
f
e
r
e
nt
a
r
e
a
s
.
O
n
th
e
ot
he
r
h
a
nd,
ha
vi
ng
a
l
a
r
ge
r
num
be
r
of
te
a
c
he
r
s
w
it
h
pot
e
nt
ia
ll
y
n
e
ga
ti
ve
tr
a
it
s
in
r
e
gi
ons
s
uc
h
a
s
'
E
dda
khl
a
-
O
ue
d
E
dda
ha
b'
a
nd
'
B
é
ni
M
e
ll
a
l
-
K
hé
ni
f
r
a
'
r
a
is
e
s
a
la
r
m
r
e
ga
r
di
ng
it
s
e
f
f
e
c
t
on
s
tu
de
nt
s
'
a
c
hi
e
ve
m
e
nt
. T
he
s
e
f
in
di
ngs
r
e
qui
r
e
a
c
om
pr
e
he
ns
iv
e
e
xpl
or
a
ti
on
of
th
e
va
r
ia
bl
e
s
th
a
t
c
ont
r
ib
ut
e
t
o
th
is
s
it
ua
ti
on,
th
us
pr
om
pt
in
g
e
duc
a
ti
ona
l
s
ta
ke
hol
de
r
s
to
de
ve
l
op
ta
r
ge
te
d
in
te
r
ve
nt
io
ns
a
nd
s
uppor
t
s
ys
te
m
s
f
or
te
a
c
he
r
s
in
th
e
s
e
f
ie
ld
s
w
it
h
a
vi
e
w
to
e
nr
ic
hi
ng
th
e
ir
pe
da
gogi
c
a
l
pr
a
c
ti
c
e
s
a
nd
e
f
f
e
c
ti
ve
ne
s
s
.
H
ow
e
ve
r
,
a
s
w
e
unpa
c
k
th
e
s
e
f
in
di
ngs
f
ur
th
e
r
,
it
is
c
r
uc
ia
l
to
no
te
th
a
t
r
e
gi
ona
l
di
s
pa
r
it
ie
s
a
r
e
onl
y
pa
r
t
of
th
e
c
om
pl
e
x
e
duc
a
ti
on
pi
c
tu
r
e
.
E
ve
n
a
r
e
a
s
th
a
t
e
xhi
bi
t
r
e
la
ti
ve
ly
s
m
a
ll
di
f
f
e
r
e
nc
e
s
,
m
a
ybe
to
w
a
r
ds
a
va
lu
e
of
c
lo
s
e
to
z
e
r
o,
ought
not
to
be
di
s
m
is
s
e
d
in
f
or
w
a
r
d
th
in
ki
ng
f
or
e
duc
a
ti
on
r
e
f
or
m
s
.
D
e
s
pi
te
th
e
ir
a
ppa
r
e
nt
ly
s
m
a
ll
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A
c
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l
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ar
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oac
h t
o e
nhan
c
in
g t
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ac
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r
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ff
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c
ti
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s
and
s
tu
de
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…
(
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ly
as
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am
m
ouc
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)
3653
di
f
f
e
r
e
nc
e
s
,
th
e
s
e
r
e
gi
ons
c
oul
d
ha
v
e
hi
dde
n
pot
e
nt
ia
l
or
pa
r
t
ic
ul
a
r
c
ont
e
xt
ua
l
is
s
ue
s
th
a
t
w
il
l
b
e
u
s
e
f
ul
to
in
f
or
m
t
he
ove
r
a
ll
de
ve
lo
pm
e
nt
of
t
he
e
duc
a
ti
ona
l
s
ys
te
m
.
H
a
r
ne
s
s
in
g
th
is
unt
a
ppe
d
pot
e
nt
ia
l
in
a
s
ound
w
a
y
r
e
qui
r
e
pol
i
c
ym
a
ke
r
s
a
nd
s
ta
ke
hol
de
r
s
in
te
r
e
s
te
d
in
e
duc
a
ti
on
ne
e
d
to
ha
ve
a
n
ove
r
a
r
c
hi
ng
a
nd
hol
is
ti
c
a
ppr
oa
c
h
to
e
duc
a
ti
on
r
e
f
or
m
.
I
ns
te
a
d
of
f
o
ll
ow
in
g
a
bl
a
nke
t
pol
ic
y,
a
n
a
tt
e
m
pt
s
houl
d
be
m
a
d
e
to
de
c
ip
he
r
th
e
pe
c
ul
ia
r
it
ie
s
a
nd
th
e
e
nvi
r
onm
e
nt
of
e
ve
r
y
pl
a
c
e
.
I
n
th
is
w
a
y,
r
e
gi
on
-
s
pe
c
if
ic
in
te
r
ve
nt
io
ns
c
a
n
be
m
a
de
th
a
t
c
a
te
r
to
th
e
s
pe
c
if
ic
r
e
qui
r
e
m
e
nt
s
a
nd
c
ha
ll
e
nge
s
of
th
e
s
tu
de
nt
s
a
nd
te
a
c
h
e
r
s
in
v
a
r
io
us
a
r
e
a
s
.
A
c
om
pr
e
h
e
ns
iv
e
s
tr
a
te
gy
not
onl
y
m
a
ke
s
c
e
r
ta
in
th
a
t
e
ve
r
yt
hi
ng
is
a
ddr
e
s
s
e
d
but
a
l
s
o
gua
r
a
nt
e
e
s
a
f
e
e
li
ng
of
ow
ne
r
s
hi
p
a
nd
e
m
pow
e
r
m
e
nt
by
th
e
lo
c
a
l
c
om
m
uni
ti
e
s
. W
he
n t
e
a
c
he
r
s
, pa
r
e
nt
s
, a
nd pupil
s
be
c
om
e
a
c
ti
v
e
s
ta
ke
hol
de
r
s
i
n t
he
di
r
e
c
ti
on of
t
he
ir
l
e
a
r
ni
n
g
e
xpe
r
ie
nc
e
,
a
r
ip
pl
e
e
f
f
e
c
t
of
c
on
s
tr
uc
ti
ve
c
ha
ng
e
in
f
il
tr
a
te
s
th
e
w
hol
e
ju
r
is
di
c
ti
on,
r
e
s
ul
ti
ng
in
a
f
a
ir
e
r
a
nd
m
or
e
e
f
f
ic
ie
nt
e
duc
a
ti
ona
l
s
ys
te
m
.
O
ve
r
a
ll
,
th
e
r
e
s
ul
ts
pr
e
s
e
nt
e
d
in
T
a
bl
e
4
not
onl
y
il
lu
m
in
a
te
th
e
di
s
pa
r
it
ie
s
in
te
a
c
h
e
r
qua
li
ty
a
c
r
os
s
r
e
gi
ons
but
a
ls
o
a
s
a
n
im
pe
tu
s
f
or
ta
ki
ng
a
n
in
te
gr
a
te
d
a
nd
c
om
pr
e
he
ns
iv
e
a
ppr
oa
c
h
to
e
duc
a
ti
on
r
e
f
or
m
.
E
m
br
a
c
in
g
th
e
di
ve
r
s
it
y
of
our
e
duc
a
ti
ona
l
s
ys
te
m
a
nd
le
ve
r
a
gi
ng
th
e
s
tr
e
ngt
hs
of
e
a
c
h
r
e
gi
on
w
il
l
undoubte
dl
y
pr
ope
l
us
to
w
a
r
ds
a
f
ut
ur
e
w
he
r
e
e
ve
r
y s
tu
de
nt
r
e
c
e
iv
e
s
a
hi
gh
-
qua
li
ty
e
du
c
a
ti
on,
ir
r
e
s
pe
c
ti
ve
of
w
he
r
e
th
e
y
li
ve
.
S
uc
h
a
f
ut
u
r
e
is
not
onl
y
a
s
pi
r
a
ti
ona
l
but
a
f
un
da
m
e
nt
a
l
r
ig
ht
th
a
t
w
il
l
f
os
te
r
a
ge
ne
r
a
ti
on
o
f
e
m
pow
e
r
e
d
in
di
vi
dua
ls
,
r
e
a
dy
to
t
a
c
kl
e
th
e
c
ha
ll
e
nge
s
of
to
m
o
r
r
ow
a
nd
c
ont
r
ib
ut
e
to
th
e
pr
ogr
e
s
s
of
s
oc
ie
ty
a
s
a
w
hol
e
.
4.
C
O
N
C
L
U
S
I
O
N
S
c
hool
s
ne
e
d
to
id
e
nt
if
y
w
hi
c
h
e
le
m
e
nt
s
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[
1]
D
.
C
a
r
d
a
nd
A
.
B
.
K
r
ue
ge
r
,
“
D
oe
s
s
c
hool
qua
l
i
t
y
m
a
t
t
e
r
?
r
e
t
ur
ns
t
o
e
duc
a
t
i
on
a
nd
t
he
c
ha
r
a
c
t
e
r
i
s
t
i
c
s
of
publ
i
c
s
c
hool
s
i
n
t
h
e
U
ni
t
e
d S
t
a
t
e
s
,”
J
our
nal
of
P
ol
i
t
i
c
al
E
c
onom
y
, vol
. 100, no. 1, pp. 1
–
40, 1992, doi
:
10.1086/
261805.
[
2]
S
.
G
e
r
r
i
t
s
e
n,
E
.
P
l
ug,
a
nd
D
.
W
e
bbi
nk,
“
T
e
a
c
he
r
qua
l
i
t
y
a
nd
s
t
ude
nt
a
c
hi
e
ve
m
e
nt
:
e
vi
de
nc
e
f
r
om
a
s
a
m
pl
e
of
D
ut
c
h
t
w
i
ns
,”
J
our
nal
of
A
ppl
i
e
d E
c
onom
e
t
r
i
c
s
, vol
. 32, no. 3, pp. 643
–
660, 2017, doi
:
10.10
02/
j
a
e
.2539.
[
3]
P
.
G
l
e
w
w
e
,
E
.
H
a
nus
he
k,
S
.
H
um
pa
g
e
,
a
nd
R
.
R
a
vi
na
,
“
S
c
hool
r
e
s
our
c
e
s
a
n
d
e
duc
a
t
i
ona
l
out
c
om
e
s
i
n
d
e
ve
l
opi
ng
c
ount
r
i
e
s
:
a
r
e
vi
e
w
of
t
he
l
i
t
e
r
a
t
ur
e
f
r
om
1990
t
o 2010,”
i
n
E
duc
at
i
on P
o
l
i
c
y
i
n D
e
v
e
l
opi
ng C
ount
r
i
e
s
, 2011, pp. 13
–
64
, doi
:
10.3386/
w
17554.
[
4]
D
.
G
ol
dha
be
r
,
“
E
ve
r
yone
’
s
doi
ng
i
t
,
but
w
h
a
t
doe
s
t
e
a
c
he
r
t
e
s
t
i
ng
t
e
l
l
us
a
bout
t
e
a
c
he
r
e
f
f
e
c
t
i
ve
ne
s
s
?
,”
J
ou
r
nal
of
H
um
an
R
e
s
our
c
e
s
, vol
. 42, no. 4, pp. 765
–
794, 2007, doi
:
10.3368/
j
hr
.xl
i
i
.4.765.
[
5]
F
.
H
of
f
m
a
nn
a
nd
P
.
O
r
e
opoul
os
,
“
P
r
of
e
s
s
or
qua
l
i
t
i
e
s
a
nd
s
t
ude
nt
a
c
hi
e
ve
m
e
nt
,”
R
e
v
i
e
w
of
E
c
onom
i
c
s
and
St
at
i
s
t
i
c
s
,
vol
.
91,
no. 1, pp. 83
–
92, 2009, doi
:
10.1162/
r
e
s
t
.91.1.83.
[
6]
C
.
M
.
H
oxby
a
nd
A
.
L
e
i
gh,
“
P
ul
l
e
d
a
w
a
y
or
pus
he
d
out
?
e
xpl
a
i
ni
ng
t
he
de
c
l
i
ne
of
t
e
a
c
he
r
a
pt
i
t
ude
i
n
t
he
U
ni
t
e
d
S
t
a
t
e
s
,
”
A
m
e
r
i
c
an E
c
onom
i
c
R
e
v
i
e
w
, vol
. 94, no. 2, pp. 236
–
240, 2004, doi
:
10.1257/
0002828041302073.
[
7]
B
.
J
a
c
ob
a
nd
L
.
L
e
f
gr
e
n,
“
P
r
i
nc
i
pa
l
s
a
s
a
ge
nt
s
:
s
ubj
e
c
t
i
ve
pe
r
f
or
m
a
nc
e
m
e
a
s
ur
e
m
e
nt
i
n
e
duc
a
t
i
on,”
C
a
m
br
i
dge
,
M
a
s
s
a
c
hus
e
t
t
s
,
2005
, doi
:
10.3386/
w
11463.
[
8]
T
. J
. K
a
ne
, J
. E
. R
oc
kof
f
, a
nd D
. O
. S
t
a
i
ge
r
, “
W
ha
t
doe
s
c
e
r
t
i
f
i
c
a
t
i
on t
e
l
l
us
a
b
out
t
e
a
c
he
r
e
f
f
e
c
t
i
ve
ne
s
s
?
e
vi
de
nc
e
f
r
om
N
e
w
Y
or
k
C
i
t
y,”
E
c
onom
i
c
s
of
E
duc
at
i
on R
e
v
i
e
w
, vol
. 27, no. 6, pp. 615
–
631, 2008, doi
:
10.1016/
j
.e
c
one
dur
e
v.2007.05.005.
[
9]
S
.
G
.
R
i
vki
n,
E
.
A
.
H
a
nus
he
k,
a
nd
J
.
F
.
K
a
i
n,
“
T
e
a
c
he
r
s
,
s
c
hool
s
,
a
nd
a
c
a
d
e
m
i
c
a
c
hi
e
ve
m
e
nt
,”
E
c
onom
e
t
r
i
c
a
,
vol
.
73,
no.
2
,
pp. 417
–
458, 2005, doi
:
10.1111/
j
.1468
-
0262.2005.00584.x.
[
10]
J
.
E
.
R
oc
kof
f
,
“
T
he
i
m
pa
c
t
of
i
ndi
vi
dua
l
t
e
a
c
he
r
s
on
s
t
ude
nt
a
c
hi
e
ve
m
e
nt
:
e
vi
de
nc
e
f
r
om
pa
ne
l
da
t
a
,
”
A
m
e
r
i
c
an
E
c
onom
i
c
R
e
v
i
e
w
, vol
. 94, no. 2, pp. 247
–
252, 2004, doi
:
10.1257/
0002828041302244.
[
11]
S
.
S
i
r
a
i
t
,
“
D
oe
s
t
e
a
c
he
r
qu
a
l
i
t
y
a
f
f
e
c
t
s
t
ude
nt
a
c
hi
e
ve
m
e
nt
?
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n
e
m
pi
r
i
c
a
l
s
t
udy
i
n
i
ndone
s
i
a
,”
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nal
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T
e
a
c
he
r
qua
l
i
f
i
c
a
t
i
on
a
nd
s
t
ude
nt
s
a
c
a
d
e
m
i
c
pe
r
f
or
m
a
nc
e
i
n
s
c
i
e
nc
e
m
a
t
he
m
a
t
i
c
s
a
nd
t
e
c
hnol
og
y
s
ubj
e
c
t
s
i
n
K
e
nya
,”
I
nt
e
r
nat
i
onal
J
our
nal
of
E
duc
at
i
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dm
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a
r
m
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,
“
D
oe
s
m
ot
i
va
t
i
on
m
a
t
t
e
r
?
–
t
he
r
e
l
a
t
i
ons
hi
p
be
t
w
e
e
n
t
e
a
c
he
r
s
’
s
e
l
f
-
e
f
f
i
c
a
c
y
a
nd
e
nt
hus
i
a
s
m
a
nd
s
t
ude
nt
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’
pe
r
f
or
m
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nc
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,”
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r
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l
l
e
a
de
r
s
hi
p
a
nd
s
t
ude
nt
s
a
c
a
de
m
i
c
pe
r
f
or
m
a
nc
e
:
m
e
di
a
t
i
ng
e
f
f
e
c
t
s
of
t
e
a
c
he
r
’
s
or
ga
ni
z
a
t
i
ona
l
c
om
m
i
t
m
e
nt
,”
I
nt
e
r
nat
i
onal
J
our
nal
of
L
e
ar
ni
ng,
T
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s
i
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de
ve
l
opm
e
nt
of
t
e
a
c
he
r
s
,
c
om
pe
t
e
n
c
i
e
s
,
e
du
c
a
t
i
ona
l
f
a
c
i
l
i
t
i
e
s
a
nd
i
nf
r
a
s
t
r
uc
t
ur
e
on
t
e
a
c
he
r
pe
r
f
or
m
a
nc
e
a
nd
l
e
a
r
ni
ng
a
c
hi
e
ve
m
e
nt
of
h
i
gh
s
c
hool
s
t
ude
nt
s
i
n
M
a
ka
s
s
a
r
C
i
t
y,”
G
ol
de
n
R
at
i
o
of
Soc
i
al
Sc
i
e
nc
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oui
c
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a
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oua
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“
U
nc
ove
r
i
ng
ke
y
f
a
c
t
or
s
of
s
t
ude
nt
pe
r
f
or
m
a
nc
e
i
n
m
a
t
h:
a
n
e
xpl
a
i
na
bl
e
de
e
p l
e
a
r
ni
ng a
ppr
oa
c
h us
i
ng T
I
M
S
S
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t
a
,”
I
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s
ur
i
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t
he
i
m
pa
c
t
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of
t
e
a
c
he
r
s
I
I
:
t
e
a
c
he
r
va
l
ue
-
a
dde
d
a
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s
t
ude
nt
out
c
om
e
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i
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ol
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or
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m
a
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r
?
t
he
e
f
f
e
c
t
s
of
a
dj
unc
t
s
on
s
t
ude
nt
s
’
i
nt
e
r
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s
t
s
a
nd
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c
t
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m
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i
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a
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i
on
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t
he
s
c
hool
pe
r
f
o
r
m
a
nc
e
of
na
t
i
ve
s
:
c
r
os
s
c
ount
r
y
e
vi
de
nc
e
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i
ng
P
I
S
A
t
e
s
t
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i
t
h
pa
r
e
nt
s
a
nd
e
duc
a
t
i
ona
l
out
c
om
e
s
i
n
de
ve
l
opi
ng
c
ount
r
i
e
s
:
e
m
pi
r
i
c
a
l
e
vi
de
nc
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f
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A
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J
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T
he
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s
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i
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N
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Y
or
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gor
i
t
hm
A
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:
a
k
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m
e
a
ns
c
l
us
t
e
r
i
ng
a
l
gor
i
t
hm
,”
J
our
nal
of
t
he
R
oy
al
St
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m
ouc
h,
a
nd
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c
hc
ha
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“
T
w
o
-
di
m
e
ns
i
ona
l
K
l
e
i
n
-
G
or
don
a
nd
S
i
ne
-
G
or
don
num
e
r
i
c
a
l
s
ol
ut
i
ons
ba
s
e
d
on
de
e
p
n
e
ur
a
l
ne
t
w
or
k,”
I
A
E
S
I
nt
e
r
nat
i
onal
J
our
nal
of
A
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t
i
f
i
c
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al
I
nt
e
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nc
e
,
vol
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1548
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j
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“
C
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nt
r
oi
d
ne
ur
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l
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w
or
k
f
or
uns
upe
r
vi
s
e
d
c
om
pe
t
i
t
i
ve
l
e
a
r
ni
ng,”
I
E
E
E
T
r
ans
ac
t
i
ons
on
N
e
ur
al
N
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a
p,”
P
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di
ngs
of
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hr
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“
F
l
e
xi
bl
e
s
e
l
f
-
or
ga
ni
z
i
ng
m
a
ps
i
n
kohone
n
3
.0,”
J
our
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of
St
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Sof
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e
l
l
i
ge
nt
Sy
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t
e
m
s
, B
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G
ül
m
e
z
,
G
.
Ö
z
t
ür
k,
a
nd
S
.
Ö
z
e
r
,
“
F
C
-
K
m
e
a
ns
:
f
i
xe
d
-
c
e
nt
e
r
e
d
K
-
m
e
a
n
s
a
l
gor
i
t
hm
,”
E
x
pe
r
t
Sy
s
t
e
m
s
w
i
t
h A
ppl
i
c
at
i
ons
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j
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s
w
a
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
A
c
om
pe
ti
ti
v
e
l
e
ar
ni
ng appr
oac
h t
o e
nhan
c
in
g t
e
ac
he
r
e
ff
e
c
ti
v
e
ne
s
s
and
s
tu
de
nt
…
(
I
ly
as
T
am
m
ouc
h
)
3655
[
31]
A
.
U
l
t
s
c
h,
“
K
ohone
n’
s
s
e
l
f
or
ga
ni
z
i
ng
f
e
a
t
ur
e
m
a
p
f
or
e
xpl
or
a
t
o
r
y
da
t
a
a
na
l
ys
i
s
,”
P
r
oc
e
e
di
ngs
I
N
N
C
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90
-
P
A
R
I
S
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.
M
.
L
e
c
a
m
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nd
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e
ym
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n,
P
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e
e
di
ng
s
of
t
he
f
i
f
t
h
be
r
k
e
l
e
y
s
y
m
pos
i
um
o
n
m
at
he
m
at
i
c
al
s
t
at
i
s
t
i
c
s
and
pr
obabi
l
i
t
y
.
L
ondon
,
E
ngl
a
nd:
U
ni
ve
r
s
i
t
y of
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a
l
i
f
or
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s
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B
I
O
G
R
A
P
H
I
E
S
O
F
A
U
T
H
O
R
S
Dr.
Ilyas
Tammouch
is
a
distinguished
researcher
at
Ibn
Tof
ail
University,
located
in
Kenitra,
Morocco.
His
scientific
inter
ests
are
varied
a
nd
cover
areas
such
as
machine
learning,
deep
learning,
data
analysis,
and
evalu
ation
systems
.
He
can
be
contacted
at
email:
ilyas.tammouc
h@
uit.ac.ma.
Soumaya
Nouna
is a rese
arch sc
ientist
in the systems
analysis
and
modelling and
decision
support
research
laboratory
at
Hassan
First
University,
Setta
t.
She
holds
expertise
in
mathematics,
ML
and
DL.
A
Ph.D.
resear
cher
in
mathematics
and
co
mputer
science,
she
ha
s
ample
experience
in
her
field.
Her
fields
of
inte
rest
are
the
analysis
of
differential
equations,
and
ML
algorithms.
She
is
also
believed
to
be
the
writer
of
seve
ral
research
papers
and
constant
ly stri
ves to p
rogress i
n her fiel
d. She
can be
contacted
at email
:
s.nouna@
uhp.ac.ma.
Abdelamine
Elouafi
is
a
Ph.D.
candidate
at
Ibn
Tofail
Universi
ty
in
Kenitra
,
Morocco,
and
a
dedicated
secondary
school
teacher.
He
holds
a
bachelor’s
in
computer
engineerin
g
(2014)
and
a
master’s
in
decision
-
making
informatics
(2019)
from
Sultan
Moulay
Slimane
Univer
sity,
plus
a
teach
ing
licens
e
from
Fes
(2016)
.
Pa
ssionate
about
machin
e
learning,
deep
learning,
and
data
analysis
.
He
can
be
contacted
at
email:
abdelamin
e.elouafi@
uit.ac.m
a.
Assia
Nouna
is
a
researcher
at
the
Systems
Analysis
and
Modelling
and
Decision
Support
Resea
rch
Labor
atory
at
Hassa
n
First
Univer
sity
in
Settat.
A
doctor
al
resea
rche
r
in
mathematics
and
computer
science.
She
is
currently
working
on
deep
learning
and
satellite
imagery
for
agricultur
al
applications
.
Her
resear
ch
aims
to
enhanc
e
agricultur
al
practice
s
through
precise
soil
analysis,
improving
crop
manageme
nt
,
and
yield
predictions.
Additionally
,
she
has
contributed
to
various
projects
and
pub
lications
in
the
field,
demonstrating
her
expertise
in
applying
advanced
computational
te
chniques
to
solve
real
-
world problems. S
he can be contacted at email:
a.nouna@
uhp.ac.ma.
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