The Future of Education: A Multi-Layered Metaverse Classroom Model for Immersive and Inclusive Learning
Future Internet,
Год журнала:
2025,
Номер
17(2), С. 63 - 63
Опубликована: Фев. 4, 2025
Modern
education
faces
persistent
challenges,
including
disengagement,
inequitable
access
to
learning
resources,
and
the
lack
of
personalized
instruction,
particularly
in
virtual
environments.
In
this
perspective,
we
envision
a
transformative
Metaverse
classroom
model,
Multi-layered
Immersive
Learning
Environment
(Meta-MILE)
address
these
critical
issues.
The
Meta-MILE
framework
integrates
essential
components
such
as
immersive
infrastructure,
interactions,
social
collaboration,
advanced
assessment
techniques
enhance
student
engagement
inclusivity.
By
leveraging
three-dimensional
(3D)
environments,
artificial
intelligence
(AI)-driven
personalization,
gamified
pathways,
scenario-based
evaluations,
model
offers
tailored
experiences
that
traditional
classrooms
often
struggle
achieve.
Acknowledging
potential
challenges
accessibility,
infrastructure
demands,
data
security,
study
proposed
practical
strategies
ensure
equitable
safe
interactions
within
Metaverse.
Empirical
findings
from
our
pilot
experiment
demonstrated
framework’s
effectiveness
improving
skill
acquisition,
with
broader
implications
for
educational
policy
competency-based,
experiential
approaches.
Looking
ahead,
advocate
ongoing
research
validate
long-term
outcomes
technological
advancements
make
more
accessible
secure.
Our
perspective
underscores
shaping
inclusive,
future-ready
environments
capable
meeting
diverse
needs
learners
worldwide.
Язык: Английский
GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 21, 2025
Collaborative
learning
is
a
prevalent
method,
and
modeling
predicting
student
performance
in
such
paradigms
an
important
task.
Most
current
methods
analyze
this
complex
task
solely
based
on
the
frequency
of
activities,
overlooking
rich
spatial
temporal
features
present
these
as
well
diverse
textual
content
provided
by
various
artifacts.
To
address
challenges,
we
choose
software
engineering
course
study
subject,
where
students
are
required
to
team
up
complete
project
together.
In
paper,
propose
novel
Global-local
Optimized
grAph
Transformer
framework
for
collaborative
learning,
termed
GOAT.
Specifically,
first
construct
dynamic
knowledge
concept-enhanced
interaction
graphs
with
nodes
representing
both
relevant
concepts,
edges
illustrating
interactions.
Additionally,
incorporate
spatial-aware
temporal-aware
modules
capture
respective
information,
enabling
interactions
within
across
teams
over
time.
A
global-local
optimization
module
introduced
model
intricate
relationships
between
teams,
highlighting
commonalities
differences
among
members.
Our
backed
theoretical
analysis
validated
through
extensive
experiments
real-world
datasets,
which
demonstrate
its
superiority
existing
methods.
Язык: Английский