ABSTRACT
The
quality
of
education
hinges
on
the
proficiency
and
training
educators.
Due
to
importance
teacher
training,
innovative
platform
Teacher
Moments
creates
simulated
classroom
scenarios.
In
this
scenario‐based
learning,
confusion
is
an
important
indicator
detect
users
who
struggle
with
simulations.
Through
Moments,
we
gathered
7975
audio
recording
responses
from
participants
self‐labelled
their
recordings
according
whether
they
sounded
confused.
Our
dataset
stands
out
for
its
size,
not
including
actor‐generated
audio,
measuring
confusion,
a
neglected
emotion
in
artificial
intelligence
(AI).
experiments
tested
unimodal
approaches
feature‐level,
model‐level
decision‐level
fusion.
Feature‐level
fusion
demonstrated
superior
performance
methods,
achieving
balanced
accuracy
0.6607
test
set.
This
outcome
highlights
necessity
further
investigation
overlooked
area
detection,
particularly
employing
realistic
datasets
like
one
used
study
exploring
new
methods.
Beyond
insights
research
also
extend
other
directions,
such
as
professionals
making
critical
decisions,
user
interface
design
or
adaptive
learning
systems.
Applied Sciences,
Год журнала:
2025,
Номер
15(2), С. 856 - 856
Опубликована: Янв. 16, 2025
Online
misogyny
is
a
significant
societal
challenge
that
reinforces
gender
inequalities
and
discourages
women
from
engaging
fully
in
digital
spaces.
Traditional
moderation
methods
often
fail
to
address
the
dynamic
context-dependent
nature
of
misogynistic
language,
making
adaptive
solutions
essential.
This
study
presents
framework
integrates
advanced
natural-language
processing
techniques
with
strategic
data
augmentation
improve
detection
content.
Key
contributions
include
emoji
decoding
interpret
symbolic
communication,
contextual
expansion
using
Sentence-Transformer
models,
LDA-based
topic
modeling
enhance
richness
understanding.
The
incorporates
machine-learning,
deep-learning,
Transformer-based
models
handle
complex
nuanced
language.
Performance
analysis
highlights
effectiveness
selected
comparative
results
emphasize
transformative
role
augmentation.
significantly
enhanced
model
robustness,
improved
generalization,
strengthened
