JTP - Jurnal Teknologi Pendidikan,
Год журнала:
2023,
Номер
25(3), С. 496 - 513
Опубликована: Дек. 31, 2023
This
research
aims
to
improve
the
learning
quality
of
Computer
Network
course
through
implementation
Waterfall
method
in
development
model.
method,
with
its
focus
on
systematic
and
sequential
approach
software
development,
is
adapted
design
implement
effective
structure.
study
uses
qualitative
data
collection
observation,
interview,
documentation.
Data
analysis
was
conducted
using
content
evaluate
effectiveness
Waterfall-based
model
implementation.
The
results
show
that
facilitates
structured
planning,
materials,
continuous
evaluation,
which
overall
contribute
improvement
quality.
developed
encourages
students'
active
participation
improves
understanding
key
concepts
Networking.
confirms
can
be
effectively
used
outside
context
particularly
improving
academic
field.
International Journal of Educational Technology in Higher Education,
Год журнала:
2024,
Номер
21(1)
Опубликована: Янв. 19, 2024
Abstract
Although
the
field
of
Artificial
Intelligence
in
Education
(AIEd)
has
a
substantial
history
as
research
domain,
never
before
rapid
evolution
AI
applications
education
sparked
such
prominent
public
discourse.
Given
already
rapidly
growing
AIEd
literature
base
higher
education,
now
is
time
to
ensure
that
solid
and
conceptual
grounding.
This
review
reviews
first
comprehensive
meta
explore
scope
nature
(AIHEd)
research,
by
synthesising
secondary
(e.g.,
systematic
reviews),
indexed
Web
Science,
Scopus,
ERIC,
EBSCOHost,
IEEE
Xplore,
ScienceDirect
ACM
Digital
Library,
or
captured
through
snowballing
OpenAlex,
ResearchGate
Google
Scholar.
Reviews
were
included
if
they
synthesised
solely
formal
continuing
published
English
between
2018
July
2023,
journal
articles
full
conference
papers,
had
method
section
66
publications
for
data
extraction
synthesis
EPPI
Reviewer,
which
predominantly
(66.7%),
authors
from
North
America
(27.3%),
conducted
teams
(89.4%)
mostly
domestic-only
collaborations
(71.2%).
Findings
show
these
focused
on
AIHEd
generally
(47.0%)
Profiling
Prediction
(28.8%)
thematic
foci,
however
key
findings
indicated
predominance
use
Adaptive
Systems
Personalisation
education.
Research
gaps
identified
suggest
need
greater
ethical,
methodological,
contextual
considerations
within
future
alongside
interdisciplinary
approaches
application.
Suggestions
are
provided
guide
primary
research.
Computers,
Год журнала:
2025,
Номер
14(3), С. 83 - 83
Опубликована: Фев. 27, 2025
This
study
investigates
the
use
of
educational
data
mining
(EDM)
techniques
to
predict
student
performance
and
enhance
learning
outcomes
in
higher
education.
Leveraging
from
Moodle,
a
widely
used
management
system
(LMS),
we
analyzed
450
students’
academic
records
spanning
nine
semesters.
Five
machine
algorithms—k-nearest
neighbors,
random
forest,
logistic
regression,
decision
trees,
neural
networks—were
applied
identify
correlations
between
courses
grades.
The
results
indicated
that
with
strong
(+0.3
above)
significantly
enhanced
predictive
accuracy,
particularly
binary
classification
tasks.
kNN
networks
emerged
as
most
robust
models,
achieving
F1
scores
exceeding
0.8.
These
findings
underscore
potential
EDM
optimize
instructional
strategies
support
personalized
pathways.
offers
insights
into
effective
application
data-driven
approaches
improve
foster
success.
Teaching and Learning in Medicine,
Год журнала:
2024,
Номер
unknown, С. 1 - 13
Опубликована: Апрель 8, 2024
Phenomenon:
Educational
activities
for
students
are
typically
arranged
without
consideration
of
their
preferences
or
peak
performance
hours.
Students
might
prefer
to
study
at
different
times
based
on
chronotype,
aiming
optimize
performance.
While
face-to-face
during
the
academic
schedule
do
not
offer
flexibility
and
cannot
reflect
students'
natural
learning
rhythm,
asynchronous
e-learning
facilitates
studying
one's
preferred
time.
Given
ubiquitous
accessibility,
can
use
resources
according
individual
needs
preferences.
E-learning
usage
data
hence
serves
as
a
valuable
proxy
certain
behaviors,
presenting
research
opportunities
explore
patterns.
This
retrospective
aims
investigate
when
how
long
undergraduate
used
medical
modules.
Approach:
We
performed
cross-sectional
analysis
one
faculty
in
Netherlands.
from
562
multimedia
modules
pre-clinical
students,
covering
various
topics
over
span
two
years
(2018/19
2019/20).
employed
educational
mining
approaches
process
subsequently
identified
patterns
access
durations.
Findings:
obtained
70,805
sessions
with
116,569
module
visits
1,495,342
page
views.
On
average,
16.8
min
daily
stopped
using
after
10.2
min,
but
varied
widely.
was
seven
days
week
an
hourly
pattern
business
hours
weekdays.
Across
all
other
times,
there
smooth
increase
decrease
usage.
During
week,
more
started
morning
(34.5%
vs.
19.1%)
while
fewer
afternoon
(42.6%
50.8%)
evening
(19.4%
27.0%).
'early
bird'
'night
owl'
user
groups
that
show
distinct
Insights:
reveals
new
insights
into
complete
student
cohort
outside
lecture
These
findings
underline
value
24/7
accessible
material.
In
addition,
our
may
serve
guide
researchers
educationalists
seeking
develop
individualized
programs.
International Journal of Information and Communication Technology Education,
Год журнала:
2024,
Номер
20(1), С. 1 - 14
Опубликована: Июль 17, 2024
This
study
examines
the
current
research
on
educational
data
mining,
learning
support
services,
personalized
and
paths
in
education.
The
authors
aim
to
integrate
concepts
into
traditional
services
by
drawing
latest
theoretical
practical
research.
Using
multimodal
fusion
techniques,
conduct
exploratory
analyses
various
types,
including
learner
academic
performance,
psychological
assessments,
behavior,
physiological
information.
leads
construction
of
a
education
service
model.
model
focuses
objectives
such
as
monitoring
identifying
preferences,
recognizing
abilities,
optimizing
paths,
recommending
resources.
goal
is
provide
learners
with
sustained
throughout
process,
addressing
individual
needs,
fostering
enthusiasm,
maintaining
long-term
motivation.
