Does digital task-based instruction make a difference in EFL university students’ motivation in Saudi Arabia? An Active theory perspective
Learning and Motivation,
Journal Year:
2025,
Volume and Issue:
90, P. 102115 - 102115
Published: Feb. 27, 2025
Language: Английский
What Deserve Studying the Most? A Q‐Methodology Approach to Explore Stakeholders' Perspectives on Research Priorities in GenAI‐Supported Second Language Education
European Journal of Education,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 27, 2024
ABSTRACT
Recently,
there
has
been
a
significant
increase
in
research
on
Generative
Artificial
Intelligence
(GenAI)
the
domain
of
second
language
(L2)
education.
Given
limited
resources,
it
is
essential
for
GenAI
to
focus
key
areas.
However,
still
uncertainty
about
which
topics
should
be
prioritised.
Research
priorities
are
often
shaped
by
individual
researchers'
personal
interests,
can
skew
many
studies.
Additionally,
stakeholder
perspectives
these
vary
widely.
Therefore,
this
study
employs
Q
methodology
reveal
consensus
among
different
groups.
To
end,
total
19
participants,
including
six
researchers,
teachers
and
seven
students,
engaged
Q‐sort
exercise
involving
34
statements.
Through
KADE
software,
subsequent
Centroid
Factor
Analysis
varimax
rotation
were
used
extract
patterns.
The
analysis
revealed
three
common
across
groups:
psychological
factors
multiple
scenarios
measurement
improvement
L2
competence.
These
findings
provide
valuable
insights
that
inform
refine
agendas
education,
optimising
allocation
resources.
Language: Английский
University Learners' Readiness for ChatGPT‐Assisted English Learning: Scale Development and Validation
European Journal of Education,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 21, 2024
ABSTRACT
Recent
AI‐based
language
learning
research
highlights
learners'
crucial
role,
yet
university
learner
readiness
in
ChatGPT‐based
English
remains
unexplored.
Accordingly,
this
current
attempted
to
develop
and
validate
a
tool
evaluate
for
ChatGPT‐assisted
(LRCEL)
address
the
gap
that
prior
instruments
have
not
taken
into
account
features
characteristics
of
ChatGPT
teaching
as
well
students'
achievement
emotions.
Three
hundred
forty‐seven
Chinese
learners
participated
help
explore
confirm
constructs
LRCEL.
Guided
by
theory
planned
behaviour
control‐value
emotions,
results
first‐order
second‐order
confirmatory
factor
analysis,
exploratory
structural
equation
modelling,
convergent
validity
discriminant
supported
an
18‐item
questionnaire
comprising
seven
dimensions.
The
LRCEL
has
been
proven
valid
reliable,
enabling
educational
educators
understand
ChatGPT‐supported
with
domain‐specific
items.
Language: Английский