Music teaching strategy and educational resource sharing based on big data
Lixin Sun,
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Qiuying Wang
No information about this author
Journal of Computational Methods in Sciences and Engineering,
Journal Year:
2024,
Volume and Issue:
24(4-5), P. 2391 - 2407
Published: Aug. 14, 2024
With
the
rapid
development
of
information
age,
big
data
technology
has
been
widely
penetrated
into
various
industries,
and
brought
profound
impact
on
its
structure
operation
mode.
In
field
music
education,
provides
advanced
tools
platforms
for
teaching,
a
new
perspective
formulation
teaching
strategies
sharing
educational
resources.
The
purpose
this
study
is
to
deeply
based
make
comparative
analysis
with
traditional
strategies.
Based
an
extensive
literature
review,
summarizes
basic
concepts,
core
features
applications
in
teaching.
order
have
more
comprehensive
understanding
actual
effects
we
designed
series
experiments
compare
performance
terms
student
learning
outcomes,
engagement,
satisfaction,
progress
efficiency.
results
show
that
strategy
can
better
meet
personalized
needs
students,
improve
significantly
effect
quality
resource
sharing.
This
scientific
ideas
methods
hopefully
beneficial
enlightenment
application
education.
Language: Английский
The Application of Big Data and Fuzzy Decision Support Systems in the Innovation of Personalized Music Teaching in Universities
International Journal of Computational Intelligence Systems,
Journal Year:
2024,
Volume and Issue:
17(1)
Published: Aug. 14, 2024
Personalized
music
teaching
in
universities
improves
students'
learning
and
efficiency
through
adaptive
guidance.
This
adaptability
requires
large
study
data
intelligent
decisions
based
on
the
learner's
ability.
article
introduces
a
Definitive
Teaching
Support
System
(DTSS)
exclusive
to
augment
this
concept.
system
is
designed
increase
of
student
interest
The
powered
by
fuzzy
decision
for
identifying
maximum
personalized
processes.
Low-to-high-sorted
personalization
provides
new
endorsements
further
sessions
derivative
process.
Maximum
target
universities.
differs
various
students
from
which
common
level
monotonous
recommendations
identified.
identified
set
as
global
solution
towards
personalization.
defuzzification
reduces
chances
low
expelling
stationary
outcomes.
Language: Английский