Leveraging Generative AI for Course Learning Outcome Categorization Using Bloom’s Taxonomy
Computers and Education Artificial Intelligence,
Год журнала:
2025,
Номер
unknown, С. 100404 - 100404
Опубликована: Апрель 1, 2025
Язык: Английский
Automatic Short Answer Grading in the LLM Era: Does GPT-4 with Prompt Engineering beat Traditional Models?
Опубликована: Фев. 21, 2025
Язык: Английский
LLMs Performance in Answering Educational Questions in Brazilian Portuguese: A Preliminary Analysis on LLMs Potential to Support Diverse Educational Needs
Опубликована: Фев. 21, 2025
Язык: Английский
Evaluating the performance of ChatGPT and GPT-4o in coding classroom discourse data: A study of synchronous online mathematics instruction
Computers and Education Artificial Intelligence,
Год журнала:
2024,
Номер
unknown, С. 100325 - 100325
Опубликована: Окт. 1, 2024
Язык: Английский
HiBenchLLM: Historical Inquiry Benchmarking for Large Language Models
Data & Knowledge Engineering,
Год журнала:
2024,
Номер
unknown, С. 102383 - 102383
Опубликована: Дек. 1, 2024
Язык: Английский
ChatGMP: a case of AI chatbots in chemical engineering education towards the automation of repetitive tasks
Computers and Education Artificial Intelligence,
Год журнала:
2024,
Номер
unknown, С. 100354 - 100354
Опубликована: Дек. 1, 2024
Язык: Английский
Near Feasibility, Distant Practicality: Empirical Analysis of Deploying and Using LLMs on Resource-Constrained Smartphones
Mateus Monteiro Santos,
Aristoteles Peixoto Barros,
Luiz Rodrigues
и другие.
Опубликована: Дек. 9, 2024
Язык: Английский
Investigating the impact of social media advertising and risk factors on customer online buying behavior: a trust-based perspective
Future Business Journal,
Год журнала:
2024,
Номер
10(1)
Опубликована: Дек. 18, 2024
Abstract
In
the
realm
of
ever-changing
e-commerce,
understanding
dynamics
customer
online
buying
behavior
(COBB)
is
pivotal.
This
study
investigates
impact
risk
factors—financial
(FR),
time
(TR),
and
psychological
(PR)
along
with
social
media
advertising
(SMA)
on
COBB
mediating
effect
trust.
Grounded
stimulus–organism–response
theory,
research
targets
diverse
segments
including
students,
businessmen,
employees,
working
women,
housewives.
To
ensure
a
high
response
rate,
an
questionnaire
was
distributed
via
email,
WhatsApp,
groups
buyers.
Convenience
sampling
used
to
collect
primary
data
from
350
respondents.
Data
analysis
that
employed
Statistical
Package
for
Social
Sciences,
descriptive
statistics,
correlation
analysis,
normality
testing,
regression
performed
reliability,
validity
hypothesis
testing.
The
findings
underscore
significant
negative
factors
(FR,
TR,
PR)
building
trust
subsequent
shopping
behavior.
has
positive
COBB.
Furthermore,
emerged
as
determinant
COBB,
thereby
validating
its
pivotal
role
in
consumer
decision-making
processes.
Moreover,
mediates
relationship
between
factors,
SMA
consists
both
practical
theoretical
contributions,
offering
insights
into
nuanced
interplay
perceptions,
effectiveness,
dynamics,
These
are
essential
marketers,
policymakers,
researchers
navigating
evolving
landscape
e-commerce.
Язык: Английский