ARTIFICIAL NEURAL NETWORK APPROACH TO PREDICT LMS ACCEPTANCE OF VOCATIONAL SCHOOL STUDENTS
Abstract
The contribution of e-learning technologies, especially LMS which has become an important component of
e-learning, is significantly increasing in higher education. It is critical to understand the factors that affect the
behavioral intention of students towards LMS use. The aim of this study is to explore predictors of students’
acceptance of Course Portal at a postsecondary vocational school level. We utilised a framework suggested by
Sezer and Yilmaz (2019) for understanding students’ acceptance of LMS. This framework obtains the main
constructs in UTAUT: namely, performance expectancy, effort expectancy, social influence and facilitating
conditions. More external variables, associate degree programs, high school type, academic grade point
average were also adopted. Accordingly, 387 students were answered the questionnaire for investigating
behavioral intention. Artificial neural network analysis (ANN) was used to predict students’ acceptance of
LMS use according to variables associated with their use of LMS technology. ANN analyses in the present
study revealed that performance expectancy, effort expectancy, social influence and facilitating conditions are
important predictors of students’ behavioral intention to use LMS. Nevertheless, performance expectancy
was found to be the most influencing predictor of LMS use. The analyses of this research provides evidence
on the utilization of ANN to predict the determining factors of LMS acceptance.
Keywords
Artificial neural networks, LMS acceptance, UTAUT,, MOODLE,, social influence, vocational school
References
- Haykin, S. (2004). Neural Networks: A Comprehensive Foundation. Pearson Education.