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Journal Article

The Effect of Age on Teachers’ Intention to Use Educational Video Games: A TAM Approach  pp355-366

Antonio Sánchez-Mena, José Martí-Parreño, Joaquín Aldás-Manzano

© Aug 2017 Volume 15 Issue 4, Editor: Elizabeth Boyle and Thomas Connolly, pp281 - 366

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Abstract

Educational video games (EVGs) are gaining momentum as a means of increasing students’ motivation in their learning process. Nevertheless, teachers might face several barriers that dissuade them from using educational video games in their courses. This study analyses factors affecting teachers´ behavioural intention to use educational video games in their courses using a Technology Acceptance Model (TAM) approach. The research model is tested via structural equation modelling (SEM) on a sample of 312 teachers in Higher Education institutions. Results suggest that perceived usefulness influences in a direct and positive way teachers’ intention to use educational video games. Results also suggest that perceived ease of use indirectly influence intentions through perceived usefulness. Age was found to moderate the effect of teachers’ perceived ease of use on perceived usefulness of EVGs. Regarding managerial implications, our findings highlight the importance of addressing specific Teacher Training Programmes focusing on teachers’ age and perceived usefulness of EVGs in order to encourage teachers to adopting this educational innovation in their courses. Limitations of the study and future research lines are also addressed.

 

Keywords: Educational Video Games; TAM (Technology Acceptance Model); Higher Education; Behavioural intention; Age

 

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Journal Article

Learner Differences in Perceived Satisfaction of an Online Learning: an Extension to the Technology Acceptance Model in an Arabic Sample  pp412-430

Ahmed Al-Azawei, Karsten Lundqvist

© Oct 2015 Volume 13 Issue 5, Editor: Rikke Ørngreen and Karin Levinsen, pp317 - 445

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Abstract

Abstract: Online learning constitutes the most popular distance‑learning method, with flexibility, accessibility, visibility, manageability and availability as its core features. However, current research indicates that its efficacy is not consistent acro ss all learners. This study aimed to modify and extend the factors of the Technology Acceptance Model (TAM) to examine perceived satisfaction of an Arabic sample in online learning. The integrated factors in the modified model includes: deep level (lea rning styles), surface level (gender), and cognitive (online self‑efficacy) factors. Learning styles were chosen as a central factor. Hence, the online course was purposefully developed to support one pole in each dimension of Felder and Silverman Le arning Styles Model (FSLSM) in order to reveal the pedagogical implications of learning styles on learner satisfaction. A total of 70 learners participated voluntarily in the research. At the end of the online course, they were requested to fill in two questionnaires: the Index of Learning Styles (ILS) and a standard questionnaire. The psychometric properties of the latter were firstly analysed to validate the instrument. Then, Partial Least Squares Structural Equation Modelling (PLS‑SEM) was conduc ted to examine the proposed hypotheses. The model achieves an acceptable fit and explains 44.8% of variance. Perceived usefulness represented the best predictor, whereas online self‑efficacy and perceived ease of use failed to show a direct impact on perc eived satisfaction. Furthermore, neither learning styles nor gender diversity had direct influence on the dependent factors. Accordingly, the research suggested that other variables may have to be integrated to enhance the power of the model.

 

Keywords: Keywords: online learning, learning styles, gender diversity, online self-efficacy, learner satisfaction, Technology Acceptance Model, TAM

 

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Journal Issue

Volume 15 Issue 4 / Aug 2017  pp281‑366

Editor: Elizabeth Boyle, Thomas Connolly

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Editorial

 

Keywords: Sign Language; American Sign Language; Recognition System; Kinect; Expert System; Game-Based Learning; Knowledge Engineering, Visual programming, Education, Computational thinking, K-12, Lightbot, Scratch, Microgames, learning, gender, culture, Multiple intelligences; Game preferences; Game mechanics; Evidence-based; Game design; Learning games, Collaboration, problem solving, online assessment, log stream data, measurement, e-learning, Educational Video Games; TAM (Technology Acceptance Model); Higher Education; Behavioural intention; Age

 

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