Artificial Neural Network Approach to Predict LMS Acceptance of Vocational School Students

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  • Author(s): Ozkan, Umut Birkan (ORCID Ozkan, Umut Birkan (ORCID 0000-0001-8978-3213); Cigdem, Harun (ORCID Cigdem, Harun (ORCID 0000-0001-5958-5216); Erdogan, Tolga (ORCID Erdogan, Tolga (ORCID 0000-0002-1921-5517)
  • Language:
    English
  • Source:
    Turkish Online Journal of Distance Education. Jul 2020 21(3):156-169.
  • Publication Date:
    2020
  • Document Type:
    Journal Articles
    Reports - Research
  • Additional Information
    • Availability:
      Anadolu University. Office of the Rector, Eskisehir, 26470, Turkey. Tel: +90-222-335-34-53; Fax: +90-222-335-34-86; e-mail: [email protected]; e-mail: [email protected]; Web site: http://tojde.anadolu.edu.tr/
    • Peer Reviewed:
      Y
    • Source:
      14
    • Education Level:
      Postsecondary Education
      Higher Education
    • Subject Terms:
      Predictor Variables; Integrated Learning Systems; Expectation; Social Influences; Males; Academic Achievement; Student Behavior; Intention; Educational Environment; Adoption (Ideas); College Students; Technology Integration; Foreign Countries; Vocational Schools; Models
    • Subject Terms:
      Turkey
    • ISSN:
      1302-6488
    • 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.
    • Abstract:
      As Provided
    • Publication Date:
      2020
    • Accession Number:
      EJ1261445