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ab
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m
a
1.
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NT
RO
D
UCT
I
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T
o
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ay
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th
e
v
o
lu
m
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o
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e
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ca
ti
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ata
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id
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ar
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in
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ield
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ca
tio
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ata
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in
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h
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s
f
ield
f
o
c
u
s
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o
n
d
ev
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p
in
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m
eth
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d
s
to
ad
d
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ess
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u
ca
ti
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v
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id
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en
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ig
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o
m
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llected
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s
v
ar
io
u
s
ed
u
ca
tio
n
al
en
v
ir
o
n
m
en
ts
[
1
]
.
L
'
ex
is
tin
g
liter
atu
r
e
h
as
m
ain
ly
ai
m
ed
at
p
r
ed
ictin
g
ac
ad
em
ic
p
e
r
f
o
r
m
a
n
ce
b
y
ex
p
lo
r
in
g
th
e
im
p
ac
t
o
f
s
tu
d
en
ts
‘
ex
ter
n
al
en
v
ir
o
n
m
en
t
o
n
th
eir
ac
a
d
em
ic
ac
h
iev
em
en
t
s
tu
d
ies
h
a
v
e
n
o
tab
ly
u
s
ed
in
s
titu
tio
n
al
b
as
es
an
d
in
ter
n
atio
n
al
ass
es
s
m
en
ts
,
s
u
ch
as
T
I
M
SS
,
PISA
an
d
PIRLS,
to
id
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tify
th
e
k
ey
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ac
to
r
s
in
f
lu
e
n
cin
g
th
is
p
er
f
o
r
m
an
ce
[
2
]
,
[
3
]
.
H
o
wev
e
r
,
o
u
r
c
o
n
tr
ib
u
tio
n
s
tan
d
s
o
u
t
b
y
f
o
cu
s
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g
o
n
th
e
im
p
ac
t
o
f
b
eh
av
io
r
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tr
aits
o
n
s
tu
d
en
t
p
er
f
o
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m
an
c
e
[
4
]
,
b
y
in
teg
r
atin
g
d
ata
co
llected
th
r
o
u
g
h
th
e
Kalb
o
a
r
d
3
6
0
p
latf
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m
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d
ap
p
ly
in
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ad
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d
ata
m
in
in
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m
eth
o
d
s
,
o
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r
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tu
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y
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s
to
c
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p
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eh
en
s
iv
ely
h
o
w
t
h
ese
s
p
ec
if
i
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eh
a
v
io
r
s
d
ir
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tly
in
f
lu
en
ce
ac
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e
m
ic
s
u
cc
ess
.
T
h
e
f
o
llo
win
g
s
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tio
n
s
o
f
th
is
ar
ticle
will
d
em
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n
s
tr
ate
h
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w
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r
in
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o
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e
m
eth
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id
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tu
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e
r
esear
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is
cr
u
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ar
ea
[
5
]
,
we
ap
p
ly
s
ix
m
ac
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in
e
lear
n
in
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alg
o
r
ith
m
s
:
d
ec
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tr
ee
,
r
an
d
o
m
f
o
r
ests
(
R
F)
,
k
-
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r
est
n
eig
h
b
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s
(
KNNs)
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d
s
u
p
p
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r
t
v
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to
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m
ac
h
i
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es
(
SVM)
to
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u
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r
o
b
u
s
t
ac
ad
em
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p
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f
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r
m
an
ce
m
o
d
el
[
6
]
,
[
7
]
.
T
h
e
g
o
al
o
f
th
is
s
tu
d
y
is
to
p
r
o
m
o
te
th
e
co
n
tin
u
o
u
s
im
p
r
o
v
e
m
en
t
o
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teac
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in
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m
eth
o
d
s
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
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5
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52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
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0
6
9
-
2
0
7
6
2070
p
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ticu
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b
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teac
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m
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t
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to
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aly
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s
tu
d
en
t
b
eh
av
io
r
in
th
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class
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tu
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as
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co
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in
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f
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ak
in
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ec
is
io
n
s
,
an
d
m
a
k
in
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r
ec
o
m
m
en
d
atio
n
s
.
So
m
e
o
f
th
e
in
f
o
r
m
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n
u
s
ed
as
a
s
o
u
r
ce
f
o
r
th
is
a
r
ticle
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in
clu
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b
elo
w.
T
h
e
au
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r
s
co
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clu
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at
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ch
o
o
l
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m
in
is
tr
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an
d
at
m
o
s
p
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h
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v
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an
im
p
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n
s
tu
d
en
ts
'
ac
ad
em
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p
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f
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r
m
an
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[
8
]
,
[
9
]
On
th
e
o
th
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h
a
n
d
,
T
h
e
au
t
h
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r
s
f
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t
h
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p
r
im
ar
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r
esp
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s
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le
f
o
r
s
tu
d
en
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'
p
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f
o
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m
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[
1
0
]
.
T
h
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r
s
[
1
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1
1
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p
r
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t
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tio
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e
d
ata
s
et
wa
s
g
ath
er
ed
.
I
n
r
ef
er
e
n
ce
[
1
2
]
,
[
1
3
]
th
e
au
th
o
r
s
u
s
e
th
e
ex
p
ec
tatio
n
m
ax
im
izatio
n
alg
o
r
ith
m
(EM
-
cl
u
s
ter
in
g
)
to
ass
ig
n
p
u
p
ils
to
f
i
v
e
g
r
o
u
p
s
ac
co
r
d
in
g
to
h
o
w
well
th
ey
p
er
f
o
r
m
ed
.
T
h
e
alg
o
r
ith
m
is
b
ased
o
n
m
ax
im
u
m
lik
elih
o
o
d
p
ar
a
m
eter
esti
m
ates
in
p
r
o
b
a
b
ilis
tic
m
o
d
els.
I
n
o
th
er
wo
r
k
Sh
an
n
aq
et
a
l
.
[
1
4
]
,
[
1
5
]
em
p
l
o
y
ed
a
ca
teg
o
r
izatio
n
m
eth
o
d
to
d
eter
m
in
e
th
e
to
tal
n
u
m
b
er
o
f
en
r
o
lled
s
tu
d
en
ts
b
y
ex
am
in
in
g
th
e
ess
en
tial
tr
aits
th
at
ca
n
af
f
ec
t
t
h
e
s
tu
d
en
ts
'
lo
y
alty
.
Fo
llo
win
g
th
eir
ex
tr
ac
tio
n
o
f
2
0
6
9
s
am
p
le
r
ec
o
r
d
s
f
r
o
m
th
e
s
tu
d
e
n
t
d
atab
ase,
th
e
au
th
o
r
s
ap
p
lie
d
a
d
ec
is
io
n
tr
ee
tech
n
iq
u
e
to
b
u
ild
a
class
if
icatio
n
m
o
d
el
an
d
p
in
p
o
in
t
th
e
k
ey
attr
ib
u
tes
th
at
co
u
ld
af
f
ec
t
s
tu
d
en
ts
.
T
h
is
r
esear
ch
en
ab
les
th
e
u
n
iv
er
s
ity
ad
m
in
is
tr
atio
n
to
p
r
o
v
id
e
th
e
n
ec
ess
ar
y
m
ater
ials
f
o
r
n
ewly
r
eg
is
ter
ed
s
tu
d
en
ts
in
h
ig
h
er
ed
u
ca
tio
n
in
s
titu
tio
n
s
.
I
n
Yaa
co
b
et
a
l
.
[
1
6
]
,
[
1
7
]
em
p
lo
y
ed
th
e
k
-
m
ea
n
s
clu
s
ter
in
g
alg
o
r
ith
m
to
u
s
e
a
d
ata
b
ase
to
f
o
r
ec
ast
s
tu
d
en
ts
'
lear
n
in
g
ac
tiv
ities
,
s
u
ch
as
test
s
an
d
q
u
izze
s
in
cl
ass
.
T
h
e
in
s
tr
u
cto
r
o
f
th
e
cla
s
s
will
r
ec
eiv
e
th
e
g
ath
er
ed
d
ata
p
r
io
r
to
t
h
e
last
ex
am
.
W
ith
tim
ely
in
ter
v
en
ti
o
n
s
,
th
is
s
tu
d
y
h
elp
s
teac
h
er
s
lo
wer
f
ailin
g
r
ates
an
d
r
aise
s
tu
d
en
t
ac
c
o
m
p
lis
h
m
en
t.
I
n
co
n
clu
s
io
n
,
n
u
m
er
o
u
s
s
tu
d
ies
h
av
e
in
v
esti
g
ated
em
p
lo
y
in
g
d
ata
m
in
in
g
ap
p
r
o
ac
h
es
to
s
o
lv
e
ed
u
ca
tio
n
al
ch
allen
g
es.
n
o
n
eth
eless
,
a
d
ea
r
th
o
f
s
t
u
d
ies
h
as
illu
m
i
n
ated
h
o
w
s
tu
d
e
n
ts
b
eh
av
e
d
u
r
in
g
th
e
lear
n
in
g
p
r
o
ce
s
s
an
d
h
o
w
th
is
af
f
ec
ts
th
e
ir
ac
ad
em
ic
ac
h
iev
em
en
t.
T
h
e
im
p
ac
t
o
f
s
tu
d
en
ts
'
in
ter
ac
tio
n
s
with
th
e
e
-
lear
n
in
g
s
y
s
tem
will
b
e
th
e
m
ain
to
p
i
c
o
f
th
is
s
tu
d
y
.
Ad
d
itio
n
ally
,
s
ch
o
o
ls
will
b
en
ef
it
f
r
o
m
th
e
ex
tr
ac
te
d
k
n
o
wled
g
e
b
y
im
p
r
o
v
in
g
s
tu
d
en
ts
'
a
ca
d
em
ic
p
er
f
o
r
m
an
ce
.
a
d
d
iti
o
n
ally
,
to
s
u
p
p
o
r
t
ad
m
in
is
tr
ato
r
s
in
en
h
a
n
cin
g
le
ar
n
in
g
s
y
s
tem
s
.
T
h
e
p
ap
e
r
is
o
r
g
an
ized
as
f
o
ll
o
ws:
th
e
s
ec
tio
n
'
s
in
itial
in
tr
o
d
u
ctio
n
:
An
o
u
tlin
e
o
f
th
e
r
es
ea
r
ch
is
s
u
e,
in
clu
d
in
g
th
e
s
tu
d
y
'
s
h
is
to
r
y
an
d
p
u
r
p
o
s
e,
is
p
r
o
v
id
ed
in
th
i
s
p
ar
t.
I
t
also
in
clu
d
es
an
o
v
er
v
iew
o
f
th
e
p
ap
er
's
p
r
im
ar
y
r
esear
ch
to
p
ics
an
d
g
o
als.
T
h
e
r
esear
ch
s
tr
ateg
y
an
d
m
eth
o
d
o
l
o
g
y
o
f
th
e
s
tu
d
y
ar
e
co
v
er
ed
in
s
ec
tio
n
2
:
m
eth
o
d
,
alo
n
g
with
d
etails
o
n
th
e
d
at
a
g
ath
e
r
in
g
a
n
d
an
al
y
s
is
p
r
o
ce
s
s
es.
Sect
io
n
3
:
r
esu
lts
an
d
d
is
cu
s
s
io
n
:
t
h
is
s
ec
tio
n
in
clu
d
es
im
p
o
r
tan
t
f
ac
ts
an
d
s
tati
s
tics
a
s
wel
l
a
s
a
d
is
cu
s
s
io
n
o
f
th
e
s
tu
d
y
'
s
c
o
n
clu
s
io
n
s
.
Alo
n
g
with
h
ig
h
lig
h
tin
g
a
n
y
p
atter
n
s
o
r
tr
en
d
s
f
o
u
n
d
,
it
also
d
is
cu
s
s
es
th
e
s
ig
n
if
ican
ce
o
f
th
e
f
i
n
d
in
g
s
f
o
r
th
e
f
ield
an
d
s
u
g
g
ests
to
p
ics f
o
r
f
u
r
t
h
er
r
esear
ch
.
2.
M
E
T
H
O
D
2
.
1
.
P
r
o
po
s
ed
m
o
del
T
h
e
s
u
g
g
ested
ap
p
r
o
ac
h
to
ass
ess
s
tu
d
en
t
p
er
f
o
r
m
an
ce
is
c
o
llected
v
ia
th
e
e
x
p
er
ien
c
e
a
p
i
(
XAPI
)
f
r
o
m
th
e
Kalb
o
ar
d
3
6
0
e
-
lear
n
in
g
p
latf
o
r
m
.
T
h
e
r
aw
d
ata
is
tr
an
s
f
o
r
m
ed
in
to
a
C
SV
f
i
le
to
m
ak
e
ad
d
itio
n
al
an
aly
s
is
ea
s
ier
.
T
h
e
d
ata
ar
e
th
en
p
r
e
p
r
o
ce
s
s
ed
to
g
u
a
r
an
tee
th
eir
d
ep
en
d
ab
ilit
y
an
d
in
te
g
r
ity
.
Data
clea
n
in
g
,
d
u
p
licate
en
tr
y
r
e
m
o
v
al,
m
is
s
in
g
v
alu
e
m
a
n
ag
em
en
t
,
v
ar
iab
le
s
tan
d
ar
d
izatio
n
,
h
an
d
lin
g
o
u
tlier
s
,
n
o
r
m
aliza
tio
n
,
an
d
d
ata
tr
an
s
f
o
r
m
atio
n
ar
e
s
o
m
e
o
f
th
e
m
eth
o
d
s
u
s
ed
to
g
et
th
e
d
ata
r
ea
d
y
f
o
r
f
u
r
th
e
r
a
n
aly
s
is
.
Featu
r
e
s
elec
tio
n
f
in
d
s
a
s
u
b
s
et
o
f
r
ele
v
an
t
f
ea
t
u
r
es
in
th
e
p
r
ep
r
o
ce
s
s
ed
d
ata
af
ter
it
h
as
b
ee
n
p
r
ep
r
o
ce
s
s
ed
.
Mu
ltip
le
class
if
ier
tech
n
iq
u
es,
s
u
c
h
as
lo
g
is
tic
r
eg
r
ess
io
n
,
KNN,
SVM,
d
ec
is
io
n
tr
ee
s
,
R
F,
an
d
XGBo
o
s
t,
th
en
u
s
e
th
is
s
u
b
s
et
as in
p
u
t.
T
h
ese
class
if
ier
s
p
r
o
d
u
ce
p
r
ed
ictio
n
s
an
d
ca
teg
o
r
ize
n
e
w
o
cc
u
r
r
en
ce
s
b
y
u
s
in
g
th
e
s
elec
ted
ch
ar
ac
ter
is
tics
to
f
in
d
p
atter
n
s
an
d
r
elatio
n
s
h
ip
s
in
th
e
d
ata.
T
h
e
p
r
e
p
r
o
ce
s
s
ed
d
ata
is
s
p
li
t
in
to
two
s
u
b
s
ets:
th
e
tr
ain
in
g
s
et
an
d
th
e
test
in
g
s
et
to
as
s
ess
th
e
ef
f
ec
tiv
en
ess
an
d
ca
p
ac
ity
f
o
r
g
en
er
aliza
tio
n
o
f
th
e
tr
ain
ed
m
o
d
els.
W
h
ile
th
e
test
s
et
is
i
n
ten
d
ed
to
ev
alu
ate
d
ata
t
h
at
h
as
y
et
to
b
e
s
ee
n
,
th
e
tr
ain
i
n
g
s
et
is
u
s
ed
to
tr
ain
alg
o
r
ith
m
s
f
o
r
class
if
icatio
n
.
Af
ter
ev
alu
atin
g
th
e
r
esu
lts
,
we
s
u
cc
ee
d
ed
in
p
r
e
d
i
ctin
g
t
h
e
s
tu
d
en
ts
'
s
ta
tu
s
,
wh
ich
h
elp
s
teac
h
er
s
m
ak
e
d
e
cisi
o
n
s
.
W
e
ad
h
er
ed
to
th
e
p
r
o
ce
d
u
r
es d
ep
icted
in
Fig
u
r
e
1
.
2
.
2
.
Da
t
a
c
o
llect
ed
T
h
is
ar
ticle
em
p
lo
y
s
d
ata
o
b
ta
in
ed
f
r
o
m
th
e
Kalb
o
ar
d
3
6
0
E
-
L
ea
r
n
in
g
s
y
s
tem
u
s
in
g
e
x
p
er
i
en
ce
API
(
XAPI
)
[
1
8
]
,
[
1
9
]
.
T
h
e
tr
ain
in
g
an
d
lear
n
in
g
ar
ch
itectu
r
e
(
T
L
A)
'
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XAPI
co
m
p
o
n
en
t
k
ee
p
s
ac
co
u
n
t
o
f
lear
n
er
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
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&
C
o
m
p
Sci
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5
0
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-
4
7
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P
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ed
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s
tu
d
en
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ma
ch
in
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le
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r
n
in
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y
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n
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lyzi
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(
A
b
d
ela
min
e
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l
o
u
a
fi
)
2071
ex
p
er
ien
ce
s
an
d
ac
tio
n
s
,
s
u
ch
r
ea
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g
ar
ticles
o
r
watc
h
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n
g
tr
ain
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g
v
id
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s
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ith
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e
u
s
e
o
f
th
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x
p
er
ien
ce
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lear
n
in
g
ac
tiv
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r
o
v
id
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s
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ec
if
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th
e
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tu
d
en
t,
th
e
a
ctiv
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an
d
th
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item
s
th
at
co
m
p
r
is
e
a
lear
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in
g
ex
p
er
ien
ce
[
2
0
]
,
[
2
1
]
.
I
n
t
h
is
s
tu
d
y
,
v
ar
iab
les
th
at
m
a
y
h
a
v
e
an
im
p
ac
t
o
n
ac
ad
e
m
ic
s
u
cc
ess
ar
e
ev
alu
ated
a
n
d
s
tu
d
en
t
b
eh
av
io
r
is
tr
ac
k
ed
th
r
o
u
g
h
o
u
t
th
e
lear
n
in
g
p
r
o
ce
s
s
u
s
in
g
X
-
API
.
T
h
er
e
ar
e
4
8
0
s
t
u
d
en
t
r
ec
o
r
d
s
with
1
7
attr
ib
u
tes in
th
e
d
ata
co
llec
ted
s
et.
T
h
r
ee
m
ain
c
ateg
o
r
ies
co
m
p
r
is
e
th
e
f
ea
tu
r
es p
r
esen
te
d
in
th
e
T
a
b
le
1
:
−
Dem
o
g
r
ap
h
ic
ch
ar
ac
ter
is
tics
lik
e
g
en
d
er
,
p
lace
o
f
b
ir
th
,
an
d
n
atio
n
ality
.
−
Aca
d
em
ic
b
ac
k
g
r
o
u
n
d
in
clu
d
e
s
s
tag
e,
g
r
ad
e,
s
em
ester
,
an
d
s
ec
t
io
n
.
−
B
eh
av
io
r
al
asp
ec
ts
in
clu
d
e
r
ai
s
in
g
h
an
d
s
in
class
,
ac
ce
s
s
in
g
r
eso
u
r
ce
s
,
p
ar
ticip
atin
g
in
co
n
v
er
s
atio
n
s
,
an
d
r
ev
iewin
g
m
ess
ag
es a
n
d
a
n
n
o
u
n
ce
m
en
ts
.
Fig
u
r
e
1
.
T
h
e w
o
r
k
f
lo
w p
r
o
ces
s
es
T
ab
le
1
.
Descr
ip
tio
n
o
f
v
ar
iab
les
A
t
t
r
i
b
u
t
e
c
a
t
e
g
o
r
y
A
t
t
r
i
b
u
t
e
D
e
scri
p
t
i
o
n
D
e
mo
g
r
a
p
h
i
c
a
l
a
t
t
r
i
b
u
t
e
G
e
n
d
e
r
Th
e
st
u
d
e
n
t
's
g
e
n
d
e
r
(
m
a
l
e
o
r
f
e
m
a
l
e
)
.
R
e
l
a
t
i
o
n
C
o
n
t
a
c
t
p
a
r
e
n
t
o
f
t
h
e
s
t
u
d
e
n
t
,
su
c
h
a
s
f
a
t
h
e
r
o
r
mo
t
h
e
r
.
N
a
t
i
o
n
a
l
i
t
y
S
t
u
d
e
n
t
n
a
t
i
o
n
a
l
i
t
y
.
P
l
a
c
e
o
f
b
i
r
t
h
P
l
a
c
e
o
f
b
i
r
t
h
f
o
r
t
h
e
st
u
d
e
n
t
.
A
c
a
d
e
mi
c
b
a
c
k
g
r
o
u
n
d
a
t
t
r
i
b
u
t
e
S
t
a
g
e
I
D
S
t
u
d
e
n
t
s
a
r
e
c
l
a
ssi
f
i
e
d
i
n
t
o
st
a
g
e
s,
su
c
h
a
s
l
o
w
l
e
v
e
l
,
m
i
d
d
l
e
l
e
v
e
l
,
a
n
d
h
i
g
h
l
e
v
e
l
.
S
e
c
t
i
o
n
I
D
S
e
c
t
i
o
n
s
t
o
w
h
i
c
h
s
t
u
d
e
n
t
s
b
e
l
o
n
g
i
n
c
l
u
d
e
(
A
,
B
,
a
n
d
C
)
.
S
e
mest
e
r
S
e
mest
e
r
s
o
f
t
h
e
sc
h
o
o
l
y
e
a
r
su
c
h
a
s (
F
i
r
st
o
r
S
e
c
o
n
d
)
.
G
r
a
d
e
I
D
Th
e
st
u
d
e
n
t
's
g
r
a
d
e
c
o
r
r
e
s
p
o
n
d
s t
o
(
G
-
0
1
,
G
-
0
2
)
.
To
p
i
c
To
p
i
c
s c
o
v
e
r
e
d
i
n
c
l
a
ss
i
n
c
l
u
d
e
sci
e
n
c
e
,
m
a
t
h
,
En
g
l
i
s
h
,
a
n
d
A
r
a
b
i
c
…
P
a
r
e
n
t
s a
n
sw
e
r
i
n
g
P
a
r
e
n
t
s A
n
sw
e
r
i
n
g
S
u
r
v
e
y
(
Y
e
s
o
r
N
o
)
P
a
r
e
n
t
sc
h
o
o
l
g
r
a
t
i
f
i
c
a
t
i
o
n
P
a
r
e
n
t
sa
t
i
sf
a
c
t
i
o
n
a
t
sc
h
o
o
l
(
G
o
o
d
o
r
B
a
d
)
S
t
u
d
e
n
t
a
b
se
n
c
e
d
a
y
s
Th
e
n
u
m
b
e
r
o
f
d
a
y
s
a
b
s
e
n
t
(
U
n
d
e
r
7
o
r
A
b
o
v
e
7
)
B
e
h
a
v
i
o
r
a
l
a
t
t
r
i
b
u
t
e
R
a
i
s
e
d
h
a
n
d
H
o
w
st
u
d
e
n
t
s r
e
a
c
t
w
h
e
n
u
si
n
g
t
h
e
K
a
l
b
o
a
r
d
3
6
0
e
-
l
e
a
r
n
i
n
g
s
y
st
e
m.
V
i
si
t
e
d
c
o
u
r
s
e
D
i
scu
ssi
o
n
g
r
o
u
p
A
n
n
o
u
n
c
e
m
e
n
t
s
v
i
e
w
F
e
a
t
u
r
e
s
C
l
a
s
s
p
e
r
f
o
r
m
a
n
c
e
Th
r
e
e
c
a
t
e
g
o
r
i
e
s a
r
e
u
se
d
t
o
c
l
a
ssi
f
y
s
t
u
d
e
n
t
s
:
l
o
w
e
r
l
e
v
e
l
,
mi
d
d
l
e
l
e
v
e
l
,
a
n
d
h
i
g
h
l
e
v
e
l
.
2
.
3
.
P
re
pa
r
a
t
io
n da
t
a
W
e
em
p
lo
y
s
p
ec
if
ic
p
r
ep
r
o
ce
s
s
in
g
tech
n
iq
u
es
to
en
h
an
ce
t
h
e
d
ata
q
u
ality
af
ter
co
m
p
letin
g
th
e
d
ata
co
llectio
n
task
[
7
]
,
[
2
2
]
.
D
ata
p
r
ep
ar
atio
n
,
w
h
ich
in
cl
u
d
es
d
ata
tr
an
s
f
o
r
m
atio
n
,
d
at
a
r
ed
u
ctio
n
,
d
ata
p
u
r
if
icatio
n
,
an
d
f
ea
tu
r
e
s
elec
tio
n
,
is
th
o
u
g
h
t
t
o
b
e
an
im
p
o
r
tan
t
p
h
ase
in
th
e
k
n
o
wled
g
e
d
is
co
v
e
r
y
p
r
o
ce
s
s
[
2
3
]
.
Data
clea
n
in
g
is
p
er
f
o
r
m
e
d
o
n
th
is
d
ata
s
et
to
elim
in
ate
n
o
is
e
an
d
m
is
s
in
g
v
alu
es.
Fo
llo
win
g
clea
n
in
g
,
th
e
d
ata
s
et
n
o
w
co
n
tain
s
4
8
0
r
ec
o
r
d
s
.
T
h
e
d
atas
et
co
n
tain
s
3
0
5
m
ales
an
d
1
7
5
f
em
ales.
Stag
e
I
D
h
as
1
9
9
lo
we
r
lev
els,
2
4
8
m
id
d
le
lev
els,
an
d
9
9
h
i
g
h
lev
els.
Fu
r
th
er
m
o
r
e,
th
e
s
tu
d
e
n
ts
ar
e
d
iv
id
ed
i
n
to
th
r
ee
s
ec
tio
n
s
:
2
8
3
s
tu
d
en
ts
f
r
o
m
s
ec
tio
n
A,
1
6
7
s
tu
d
en
ts
f
r
o
m
s
ec
tio
n
B
,
an
d
3
0
s
tu
d
en
ts
f
r
o
m
s
ec
tio
n
C
.
T
o
p
ic
attr
ib
u
tes
in
clu
d
e:
9
5
s
tu
d
en
ts
ar
e
as
s
o
ciate
d
to
th
e
I
T
to
p
ic
,
2
1
to
Ma
th
,
4
5
to
E
n
g
lis
h
,
3
0
to
B
io
lo
g
y
,
2
4
to
C
h
em
is
tr
y
,
2
4
t
o
Geo
lo
g
y
,
2
5
to
Sp
an
is
h
,
2
2
to
Qu
r
an
,
5
1
t
o
s
cien
ce
,
1
9
to
His
to
r
y
,
a
n
d
5
9
to
Ar
ab
ic
t
o
p
ic.
R
elatio
n
attr
ib
u
te
in
clu
d
es 2
8
3
s
tu
d
en
ts
,
th
eir
co
n
tact
p
er
s
o
n
is
th
e
f
ath
er
an
d
1
9
7
s
tu
d
en
t
s
,
th
e
co
n
tact
p
er
s
o
n
is
th
eir
m
o
th
er
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
2
0
6
9
-
2
0
7
6
2072
2
.
4
.
T
he
o
bje
ct
iv
e
o
f
pa
per
T
h
e
p
r
im
a
r
y
g
o
al
o
f
t
h
is
s
tu
d
y
is
to
an
aly
ze
s
tu
d
en
ts
'
class
r
o
o
m
b
e
h
av
io
r
to
i
d
e
n
tify
th
e
ch
ar
ac
ter
is
tics
th
at
h
av
e
an
im
p
o
r
tan
t
im
p
ac
t
o
n
th
eir
ac
a
d
em
ic
ac
h
iev
em
en
t.
Ne
x
t,
we
aim
to
id
en
tify
th
e
m
o
d
el
th
at
m
o
s
t
ac
c
u
r
ately
r
e
p
r
esen
ts
s
tu
d
en
t
p
er
f
o
r
m
an
ce
u
s
in
g
p
o
wer
f
u
l
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
.
O
u
r
g
o
al
is
to
b
o
o
s
t
p
r
ed
ictio
n
a
cc
u
r
ac
y
.
a
n
d
th
is
m
o
d
el
will
o
p
tim
ize
in
ter
v
e
n
tio
n
m
ec
h
an
is
m
s
to
p
r
o
v
id
e
s
tu
d
en
ts
with
in
d
iv
id
u
alize
d
h
elp
ad
ap
ted
to
th
eir
i
n
d
iv
id
u
al
n
ee
d
s
.
T
h
is
h
elp
s
p
r
o
f
ess
o
r
s
i
n
th
e
ev
alu
atio
n
o
f
s
tu
d
en
ts
in
th
e
class
r
o
o
m
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
g
iv
es
an
ex
ten
s
iv
e
o
v
er
v
iew
o
f
o
u
r
e
x
p
er
im
e
n
t
al
d
esig
n
,
in
clu
d
in
g
class
r
o
o
m
b
eh
av
io
r
an
aly
s
is
an
d
v
a
r
iab
le
id
en
tific
atio
n
th
at
af
f
ec
ts
s
tu
d
en
t
s
tatu
s
.
W
e
d
escr
ib
e
in
d
etail
th
e
p
r
ec
is
e
s
ettin
g
s
an
d
in
s
tr
u
m
en
ts
em
p
lo
y
ed
to
ca
r
r
y
o
u
t
an
ex
h
au
s
tiv
e
ass
ess
m
e
n
t
o
f
o
u
r
s
u
g
g
ested
m
o
d
el.
W
e
th
en
g
o
o
v
er
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
ev
alu
at
io
n
'
s
f
in
d
in
g
s
.
I
n
co
n
clu
s
io
n
,
we
g
o
in
to
d
etail
ab
o
u
t
t
h
ese
f
in
d
in
g
s
an
d
em
p
h
asize
h
o
w
well
th
e
m
o
d
e
l w
o
r
k
s
to
f
o
r
ec
ast s
tu
d
en
t a
ch
iev
em
en
t b
ased
o
n
b
e
h
av
io
r
in
th
e
class
r
o
o
m
.
3
.
1
.
Co
rr
el
a
t
io
n
An
im
p
o
r
tan
t
s
tag
e
in
o
u
r
r
esear
ch
was
to
in
v
esti
g
ate
th
e
c
o
r
r
elatio
n
s
[2
4
]
b
etwe
en
v
ar
io
u
s
f
ea
tu
r
es
u
s
in
g
h
ea
t
m
ap
m
eth
o
d
o
lo
g
ie
s
.
T
h
e
Fig
u
r
e
2
v
is
u
ally
d
ep
i
cts
th
ese
co
r
r
elatio
n
s
,
f
o
cu
s
in
g
o
n
th
e
lin
k
a
g
es
b
etwe
en
v
ar
io
u
s
tr
aits
an
d
s
tu
d
en
t
s
tatu
s
.
A
h
ea
t
m
ap
h
ig
h
lig
h
ts
p
o
ten
tial
p
atter
n
s
o
r
lin
k
a
g
es
th
at
co
u
ld
h
av
e
an
im
p
ac
t
o
n
s
tu
d
e
n
t
o
u
tco
m
es
wh
ile
o
f
f
er
in
g
a
th
o
r
o
u
g
h
a
n
d
u
n
d
er
s
tan
d
ab
le
p
ictu
r
e
o
f
h
o
w
v
ar
io
u
s
elem
en
ts
in
ter
ac
t.
W
ith
th
ese
ch
ar
ac
ter
is
tics
,
it
is
p
o
s
s
ib
le
t
o
o
b
tain
i
n
f
o
r
m
atio
n
th
at
h
as
a
d
ir
ec
t
im
p
ac
t
o
n
s
tu
d
en
t
p
er
f
o
r
m
an
ce
,
wh
ich
h
as
b
ee
n
th
e
o
b
jectiv
e
o
f
s
ev
er
al
p
r
ev
i
o
u
s
s
tu
d
ies.
H
o
wev
er
,
th
ese
s
tu
d
ies
h
av
e
m
ain
ly
f
o
c
u
s
ed
o
n
ch
a
r
ac
ter
i
s
tics
in
f
lu
en
cin
g
s
tu
d
en
t
p
er
f
o
r
m
an
ce
i
n
ter
m
s
o
f
k
n
o
wled
g
e.
I
n
co
n
tr
ast,
o
u
r
r
esear
ch
em
p
h
asizes th
e
im
p
a
ct
o
f
class
r
o
o
m
b
e
h
av
io
r
o
n
s
t
u
d
en
t p
e
r
f
o
r
m
an
ce
.
Fig
u
r
e
2.
Qu
alities
ass
o
ciate
d
with
th
e
s
tu
d
en
t'
s
s
tatu
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
P
r
ed
ictin
g
s
tu
d
en
t sta
tu
s
u
s
in
g
ma
ch
in
e
le
a
r
n
in
g
b
y
a
n
a
lyzi
n
g
…
(
A
b
d
ela
min
e
E
l
o
u
a
fi
)
2073
W
ith
th
is
h
ea
tm
ap
,
we
ca
n
q
u
ick
ly
d
eter
m
in
e
wh
ic
h
f
ea
t
u
r
e
s
h
av
e
t
h
e
m
o
s
t
s
ig
n
if
ican
t
i
n
f
lu
en
ce
o
n
s
tu
d
en
t statu
s
:
−
Fo
u
r
m
o
s
t im
p
ac
tf
u
l
f
ea
tu
r
es (
p
o
s
itiv
ely
)
:
a.
Stu
d
en
ts
'
p
er
m
an
en
t v
is
it to
co
u
r
s
e
co
n
te
n
t h
as a
p
o
s
itiv
e
i
m
p
ac
t o
n
t
h
eir
s
tatu
s
.
b.
R
aisi
n
g
y
o
u
r
h
a
n
d
in
class
to
p
ar
ticip
ate
h
as a
p
o
s
itiv
e
im
p
a
ct
o
n
s
tu
d
en
ts
'
s
tatu
s
.
c.
T
h
e
n
u
m
b
er
o
f
in
ter
ac
tio
n
s
s
tu
d
en
ts
h
a
v
e
with
p
o
s
ted
a
n
n
o
u
n
ce
m
en
t
s
h
as
a
f
a
v
o
r
a
b
le
im
p
ac
t
o
n
th
eir
s
tatu
s
.
d.
Gr
o
u
p
d
is
cu
s
s
io
n
ap
p
ea
r
s
to
i
n
f
lu
en
ce
s
tu
d
e
n
t le
ar
n
in
g
.
−
T
wo
m
o
s
t im
p
ac
tf
u
l
f
ea
tu
r
es (
n
eg
ativ
ely
)
:
a.
Stu
d
en
t
abs
enc
es
have
an i
nv
er
se
i
m
pact
on t
hei
r
l
ear
ni
ng.
b.
The
r
elatio
n
s
h
ip
bet
w
een
par
ent
s a
nd st
udent
s a
l
so i
nf
l
ue
nces
t
he s
t
at
us of
st
udent
s.
I
n
th
is
w
o
r
k
,
we
will
f
o
cu
s
o
n
class
r
o
o
m
b
eh
a
v
io
r
,
co
n
s
id
er
in
g
t
h
e
f
o
llo
win
g
ch
ar
ac
ter
is
t
ics:
r
aised
h
an
d
,
v
is
ited
co
u
r
s
e,
d
is
cu
s
s
io
n
g
r
o
u
p
s
,
a
n
d
an
n
o
u
n
ce
m
e
n
t
v
iew.
T
h
ese
ass
o
ciatio
n
s
ar
e
r
ep
r
esen
ted
v
is
u
ally
in
Fig
u
r
e
3
,
wh
ich
also
s
h
o
w
s
th
e
d
is
tr
ib
u
tio
n
o
f
s
tu
d
en
t
s
tatu
s
an
d
th
e
r
elatio
n
s
h
ip
s
b
e
twee
n
ea
ch
p
air
o
f
attr
ib
u
tes.
T
o
an
aly
ze
th
e
d
iag
r
am
r
ep
r
e
s
en
tin
g
th
e
d
en
s
ity
o
f
s
tu
d
en
t
s
tatu
s
ac
co
r
d
in
g
to
th
e
v
ar
ia
b
les
r
aised
h
an
d
,
v
is
ited
co
u
r
s
e,
an
n
o
u
n
c
em
en
t
v
iew,
an
d
d
is
cu
s
s
io
n
g
r
o
u
p
,
s
ev
e
r
al
k
ey
o
b
s
er
v
atio
n
s
em
er
g
e.
Fig
u
r
e
4
g
r
ap
h
ically
r
e
p
r
esen
ts
th
ese
d
is
tr
ib
u
tio
n
s
.
First,
it
is
n
o
tab
l
e
th
at
s
tu
d
en
ts
with
a
lo
w
lev
el
o
f
p
ar
ticip
atio
n
,
esp
ec
ially
in
th
e
"lo
w
lev
el"
g
r
o
u
p
,
ar
e
s
tr
o
n
g
ly
co
n
ce
n
t
r
ated
in
th
e
lo
wer
r
an
g
es
o
f
th
e
v
ar
iab
les.
Fo
r
ex
am
p
le,
m
a
n
y
o
b
s
er
v
atio
n
s
s
h
o
w
th
at
f
o
r
"r
aised
h
an
d
"
an
d
"v
is
ited
c
o
u
r
s
e,
"
th
ese
s
t
u
d
en
ts
ar
e
m
ain
ly
lo
ca
ted
b
etwe
en
0
an
d
2
0
o
n
a
s
ca
le
o
f
0
t
o
1
0
0
.
On
t
h
e
o
t
h
e
r
h
an
d
,
s
tu
d
en
ts
with
a
h
ig
h
le
v
el
o
f
p
ar
ticip
atio
n
(
"h
ig
h
le
v
el")
clu
s
ter
m
ain
l
y
t
o
war
d
s
th
e
u
p
p
er
en
d
s
o
f
th
e
v
ar
iab
les,
s
u
ch
as
8
0
to
1
0
0
f
o
r
"r
aised
h
an
d
"
an
d
7
5
to
9
4
f
o
r
"v
is
ited
co
u
r
s
e"
.
f
o
r
"a
n
n
o
u
n
ce
m
en
t
v
iew"
,
alt
h
o
u
g
h
m
o
s
t
s
tu
d
en
ts
s
h
o
w
a
l
o
w
lev
el
o
f
v
iewin
g
an
n
o
u
n
ce
m
en
ts
(
0
–
2
0
)
,
t
h
o
s
e
with
in
ter
m
ed
iate
an
d
h
ig
h
lev
els
m
o
r
e
e
v
en
ly
d
is
tr
ib
u
te
th
eir
o
b
s
er
v
atio
n
s
o
v
er
a
wid
e
r
r
an
g
e
o
f
2
0
to
9
0
.
Fin
ally
,
f
o
r
th
e
"d
is
cu
s
s
io
n
g
r
o
u
p
,
"
a
s
ig
n
if
ican
t
p
a
r
t
o
f
th
e
o
b
s
er
v
atio
n
s
is
co
n
ce
n
tr
ated
ar
o
u
n
d
0
to
2
0
,
with
a
m
o
r
e
s
p
r
ea
d
d
is
tr
ib
u
tio
n
f
o
r
in
ter
m
ed
iate
an
d
h
ig
h
le
v
els
b
etwe
en
2
0
an
d
9
0
.
T
h
ese
o
b
s
er
v
atio
n
s
s
u
g
g
es
t
a
p
o
ten
tial
c
o
r
r
elatio
n
b
etwe
en
th
e
lev
el
o
f
p
ar
ticip
atio
n
o
f
s
tu
d
en
ts
an
d
t
h
eir
s
tatu
tes,
p
o
in
tin
g
to
war
d
f
u
r
th
er
ex
p
lo
r
atio
n
o
f
th
e
im
p
ac
ts
o
f
ac
tiv
e
en
g
a
g
em
en
t
o
n
ac
ad
em
ic
o
u
tco
m
es
an
d
s
tu
d
en
t w
ell
-
b
ein
g
.
Fig
u
r
e
3.
T
h
e
r
elatio
n
s
h
ip
b
et
wee
n
f
ea
tu
r
e
p
air
s
an
d
th
e
d
is
tr
ib
u
tio
n
o
f
s
tu
d
en
t statu
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
2
0
6
9
-
2
0
7
6
2074
Fig
u
r
e
4
.
Dis
tr
ib
u
tio
n
o
f
s
tu
d
e
n
ts
ac
co
r
d
in
g
to
class
b
eh
av
io
r
3
.
2
.
M
o
del
co
ns
t
ruct
io
n
Du
r
in
g
th
e
m
o
d
el
b
u
ild
in
g
s
tag
e,
we
cr
ea
ted
p
r
ed
icted
m
o
d
els
o
f
ac
ad
em
ic
ac
h
iev
em
en
t
u
s
in
g
s
ix
d
if
f
er
en
t
m
ac
h
in
e
lear
n
in
g
te
ch
n
iq
u
es.
Am
o
n
g
th
ese
tech
n
iq
u
es
ar
e
SVM,
KNNs,
R
F,
an
d
d
ec
is
io
n
tr
ee
s
.
W
h
ile
d
ec
is
io
n
tr
ee
s
ar
e
u
s
ef
u
l
f
o
r
ca
p
tu
r
in
g
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
in
d
ata,
R
F
co
m
b
in
e
s
ev
er
a
l
d
ec
is
io
n
tr
ee
s
to
m
an
ag
e
o
v
er
f
itti
n
g
an
d
in
cr
ea
s
e
p
r
ed
ictio
n
ac
cu
r
ac
y
.
KNNs,
ap
p
r
o
p
r
iate
f
o
r
lo
ca
li
ze
d
p
atter
n
s
in
th
e
d
ata,
u
s
es
s
im
ilar
ity
m
etr
ics
to
p
r
ed
ict
o
u
tco
m
es
b
ased
o
n
th
e
clo
s
est
tr
ain
in
g
s
am
p
les.
T
o
m
a
x
im
ize
th
e
m
ar
g
in
b
etwe
en
class
es
f
o
r
r
el
iab
le
p
r
ed
ictio
n
s
,
SVM
ca
teg
o
r
ize
d
ata
p
o
in
ts
in
to
d
is
tin
ct
cl
ass
es
u
s
in
g
k
er
n
el
ap
p
r
o
ac
h
es.
T
o
m
ax
im
ize
m
o
d
el
p
ar
am
eter
s
an
d
g
u
ar
an
tee
g
en
er
aliza
b
ilit
y
ac
r
o
s
s
a
v
a
r
iety
o
f
d
atasets
,
cr
o
s
s
-
v
alid
atio
n
tech
n
i
q
u
es
wer
e
em
p
lo
y
ed
th
r
o
u
g
h
o
u
t
th
e
t
r
a
in
in
g
an
d
f
in
e
-
t
u
n
in
g
o
f
ea
ch
alg
o
r
ith
m
.
T
h
is
co
m
p
r
eh
e
n
s
iv
e
ap
p
r
o
ac
h
allo
wed
u
s
to
ex
p
l
o
r
e
v
ar
io
u
s
f
ac
ets
o
f
s
tu
d
en
t
p
er
f
o
r
m
a
n
ce
p
r
ed
ictio
n
,
lev
e
r
ag
in
g
th
e
s
tr
en
g
th
s
o
f
ea
c
h
alg
o
r
ith
m
to
u
n
c
o
v
er
m
ea
n
in
g
f
u
l in
s
ig
h
ts
f
r
o
m
ed
u
ca
tio
n
al
d
ata
[
2
5
]
,
[
2
6
]
.
3
.
3
.
Co
m
pa
riso
n o
f
t
he
re
s
ults o
f
cla
s
s
if
ica
t
io
n a
lg
o
rit
h
m
s
Af
ter
ap
p
ly
in
g
class
if
icatio
n
tech
n
iq
u
es
to
th
e
d
ataset,
o
u
r
s
tu
d
y
d
em
o
n
s
tr
ates
th
at
th
e
r
esu
lts
ar
e
d
is
tin
ct
d
ep
en
d
in
g
o
n
th
e
c
lass
if
icatio
n
alg
o
r
ith
m
s
u
s
ed
.
T
ab
le
2
p
r
esen
ts
th
e
r
esu
lts
u
s
in
g
d
if
f
er
e
n
t
class
if
icatio
n
alg
o
r
ith
m
s
(
L
o
g
is
tic
r
eg
r
ess
io
n
,
KNNs
,
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e,
d
ec
is
io
n
tr
ee
,
RF
,
an
d
XGBo
o
s
t)
.
T
h
er
e
ar
e
n
o
tab
l
e
d
if
f
er
en
ce
s
in
th
e
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els'
ca
p
ac
ity
to
class
if
y
at
th
r
ee
d
if
f
er
en
t
lev
els
o
f
ca
teg
o
r
iza
tio
n
,
as
s
h
o
wn
b
y
th
e
co
m
p
ar
ativ
e
tab
le
o
f
p
er
f
o
r
m
an
ce
m
ea
s
u
r
es.
W
ith
an
o
v
er
all
ac
cu
r
ac
y
o
f
8
4
%
an
d
h
ig
h
F1
s
co
r
es,
n
o
tab
ly
0
.
9
5
f
o
r
th
e
h
ig
h
-
lev
el
ca
teg
o
r
y
,
lo
g
is
tic
r
eg
r
ess
io
n
s
tan
d
s
o
u
t.
KNNs
,
o
n
th
e
o
th
er
h
an
d
,
p
r
o
d
u
ce
f
ewe
r
g
o
o
d
f
in
d
in
g
s
,
with
an
ac
c
u
r
ac
y
o
f
6
9
%
an
d
g
e
n
er
ally
lo
wer
F1
s
co
r
es.
W
ith
b
alan
c
ed
r
ec
all
an
d
F1
s
co
r
es,
th
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
ac
h
iev
es
7
2
%
ac
c
u
r
ac
y
,
wh
ich
is
a
litt
le
less
th
an
th
e
lo
g
is
tics
r
eg
r
ess
io
n
.
Alth
o
u
g
h
Dec
is
io
n
-
T
r
ee
'
s
o
v
er
all
ac
c
u
r
ac
y
is
ju
s
t
6
0
%,
it
p
er
f
o
r
m
s
ex
ce
p
tio
n
ally
well
at
a
h
ig
h
lev
el,
with
a
h
ig
h
F1
-
s
co
r
e
o
f
0
.
8
4
.
B
o
th
RF
an
d
XGBo
o
s
t
d
em
o
n
s
tr
ate
s
tr
o
n
g
r
esu
lts
;
RF
attain
s
8
3
%
ac
cu
r
ac
y
,
wh
ile
XGBo
o
s
t
s
tan
d
s
o
u
t
f
o
r
h
av
in
g
a
f
lawless
F1
-
s
co
r
e
o
f
1
.
0
0
at
th
e
in
ter
m
ed
iate
lev
el.
I
n
co
n
clu
s
io
n
,
th
e
ch
o
ice
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[
1
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A
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