Journal of Intelligence,
Год журнала:
2024,
Номер
12(11), С. 111 - 111
Опубликована: Ноя. 5, 2024
Cultivating
scientific
literacy
is
a
goal
widely
shared
by
educators
and
students
around
the
world.
Many
studies
have
sought
to
enhance
students'
proficiency
in
through
various
approaches.
However,
there
need
explore
attributes
associated
with
advanced
levels
of
literacy,
especially
influence
contextual
factors.
In
this
context,
our
study
employs
machine
learning
technique-the
SVM-RFE
algorithm-to
identify
critical
characteristics
strong
Asia,
Europe,
South
America.
Our
research
has
pinpointed
30
key
factors
from
broader
set
162
that
are
indicative
outstanding
among
15-year-old
secondary
school
students.
By
utilizing
student
samples
three
continents,
provides
comprehensive
analysis
these
across
entire
dataset,
along
comparative
examination
optimal
between
continents.
The
findings
highlight
importance
factors,
which
should
be
considered
educational
policymakers
leaders
when
developing
policies
instructional
strategies
foster
most
effective
development
literacy.
Behavioral Sciences,
Год журнала:
2024,
Номер
14(9), С. 838 - 838
Опубликована: Сен. 19, 2024
Artificial
intelligence
and
positive
psychology
play
crucial
roles
in
education,
yet
there
is
limited
research
on
how
these
psychological
factors
influence
learners'
use
of
AI,
particularly
language
education.
Grounded
self-determination
theory,
this
study
investigates
the
influencing
Chinese
English
intention
to
AI
for
learning.
Utilizing
structural
equation
modeling,
examines
mediating
grit,
flow,
resilience
relationship
between
basic
needs
AI.
Data
were
analyzed
using
AMOS
26
SPSS
26.
The
findings
reveal
that
mediate
adopt
tools
This
provides
valuable
insights
into
educational
environments
can
be
designed
fulfill
needs,
thereby
fostering
greater
engagement
acceptance
Journal of Research in Science Teaching,
Год журнала:
2024,
Номер
unknown
Опубликована: Март 31, 2024
Abstract
Through
the
lens
of
science
capital,
this
research
aims
to
detect
key
factors
and
their
main
effects
in
identifying
students
with
science‐related
career
expectations.
A
machine
learning
approach
(i.e.,
random
forest)
was
employed
analyze
a
dataset
519,334
15‐year‐old
from
Programme
for
International
Student
Assessment
(PISA)
2015.
The
global
analysis
identified
25
out
88
contextual
features:
(1)
“how
you
think,”
making
feel
is
relevant,
enjoyable,
interesting
relatively
more
crucial
than
being
ambitious
confident;
(2)
“what
know,”
students'
math
literacy,
epistemological
beliefs,
awareness
environmental
matters
were
factors;
(3)
“who
parents
valuing
science,
expecting
children
enter
providing
emotional
support
as
similar
or
even
important
economic,
social,
cultural
status
(ESCS)‐related
constructs,
while
teachers
fairness
ranked
top
among
all
teaching‐related
features;
(4)
do,”
appropriate
time,
engagement
activities,
ICT
use
schoolwork
factors.
These
findings
indicate
optimistic
situation,
most
capitals
malleable
educators.
Accumulated
local
effect
plots
further
discriminated
how
these
related
expectations
four
distinct
ways:
“increasing,”
“S‐shaped,”
“inverted‐U‐shaped,”
“decreasing,”
shedding
light
on
we
could
optimize
resources
enhance
aspirations.
comparison
between
Hong
Kong
analyses
suggests
by
model
generally
effective
but
not
necessarily
essential
specific
region.
cross‐cultural
generalizability
prevalence
might
vary
forms.
International Journal of Advanced Computer Science and Applications,
Год журнала:
2024,
Номер
15(6)
Опубликована: Янв. 1, 2024
Utilisation
of
Educational
Data
Mining
(EDM)
can
be
useful
in
predicting
academic
performance
students
to
mitigate
student
attrition
rate,
allocation
resources,
and
aid
decision-making
processes
for
higher
education
institution.
This
article
uses
a
large
dataset
from
the
Programme
International
Student
Assessment
(PISA)
consisting
612,004
participants
79
countries,
supported
by
machine
learning
approach
predict
performance.
Unlike
most
literature
that
is
confined
one
geographical
location
or
with
limited
datasets
factors,
this
studies
other
factors
contribute
success
data
various
backgrounds.
The
accuracy
proposed
model
achieved
74%.
It
discovered
Gradient
Boosted
Trees
surpass
classification
models
were
considered
(Logistic
Regression,
Naïve
Bayes,
Deep
Learning,
Random
Forest,
Fast
Large
Margin,
Generalised
Linear
Model,
Decision
Tree
Support
Vector
Machine).
Reading
skills
habits
are
highest
importance
students.
British Journal of Educational Psychology,
Год журнала:
2024,
Номер
94(4), С. 1224 - 1244
Опубликована: Сен. 22, 2024
Abstract
Background
Given
that
students
from
socio‐economically
disadvantaged
family
backgrounds
are
more
likely
to
suffer
low
academic
performance,
there
is
an
interest
in
identifying
features
of
resilience,
which
may
mitigate
the
relationship
between
socio‐economic
status
and
performance.
Aims
This
study
sought
combine
machine
learning
explainable
artificial
intelligence
(XAI)
technique
identify
key
resilience
mathematics
during
COVID‐19.
Materials
Methods
Based
on
PISA
2022
data
79
countries/economies,
random
forest
model
coupled
with
Shapley
additive
explanations
(SHAP)
value
not
only
uncovered
but
also
examined
contributions
each
feature.
Results
Findings
indicated
35
were
identified
classification
academically
resilient
non‐academically
students,
largely
validated
previous
framework.
Notably,
gender
differences
shown
distribution
some
features.
Research
findings
tended
have
a
stable
emotional
state,
high
levels
self‐efficacy,
truancy
positive
future
aspirations.
Discussion
has
established
research
paradigm
essentially
methodological
nature
bridge
gap
psychological
theories
big
field
educational
psychology.
Conclusion
To
sum
up,
our
shed
light
issues
education
equity
quality
global
perspective
times
COVID‐19
pandemic.
