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An investigation of mobile augmented reality-based learning features in cognitive and affective environments

Jamali, Siti S. (2017) An investigation of mobile augmented reality-based learning features in cognitive and affective environments. PhD thesis, Murdoch University.

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This research focuses on the effectiveness of using mobile Augmented Reality (mAR) for learning. Prior research has focused primarily on developing virtual contents for Augmented Reality (AR) and has largely ignored AR in the mobile context. Herein, this research primarily aims to examine the effectiveness of learning through two modes: mobile Augmented Reality (mAR) and the Current Learning Mode (CLM). This research is extended to the development stage of a theoretical model, to evaluate the ability of mAR in improving the learning outcomes that guide a further consideration of growth in learning.

The first phase of this thesis is to examine the impact of how mAR influences the learning outcomes in cognitive ability and affective learning outcomes. The cognitive outcome was measured by the experimental method of using pre/ post-test performance achievement, while the affective learning outcome was measured by perceived usefulness, self-efficacy and satisfaction. This research contributes to cognitive ability and affective learning by investigating the differences in the learning outcomes and performance achievements of mAR within a self-centred learning environment, a classroom. The findings show that students’ performance achievement, learning outcomes, perceived learning effectiveness and self-efficacy were greater in the mAR group, as compared to the CLM group.

Second, a theoretical model was developed and analysed using Structural Equation Modelling (SEM). SEM examines significant relationships between the determinants that integrate and facilitate effective mAR-based learning environments. SEM produces a feasible alternative in measuring the causal relationship amongst the constructs. This model evaluates to implement mAR as a learning aid in student-centred learning and to evaluate the motivation among students through the features of mAR, due to the absence of an in-depth understanding of the motivation of mAR-based learning from the current literature. This model also provides an insight into the causal factors amongst the dimensions of mAR. Finally, in the model, the moderating effects of students’ characteristics, which include their experience and age, are investigated to determine the factors influencing mAR.

The findings of this research will help to verify the learning effectiveness of mAR, to improve the learning experiences, learning outcomes and performance achievements of students. Based on the results, it is confirmed that mAR can be leveraged upon and used as an optimum learning tool, exemplifying the use of technology within an educational context. In the aspects of information retention and learning outcome enhancement, mAR is significant in education as it facilitates students’ understanding by supporting abstract ideas throughout the course, enabling the students to learn in a limited period. Based on the results, it can be concluded that mAR is a technology that aids students with a better understanding of the subject matter and hence, resulting in greater motivation. With regards to the model fitness via the analysis of goodness-of-fit, all the results are confirmed as appropriate and good fit. Also, the model also shows a positive causal path from the mAR features’ determinant. The thesis can also assist educational administrators and educational policy makers in gauging the importance of mAR as a learning tool. This helps mainly to overcome the issue of educators being criticised for the lack of real-life experience that is being exposed to students at the university level. Furthermore, academia can use the model’s findings as appropriate groundwork to initiate other related studies, and this will help to fill the gap in the mAR learning area.

Publication Type: Thesis (PhD)
Murdoch Affiliation: School of Engineering and Information Technology
Supervisor: Shiratuddin, Fariuz and Wong, Kevin
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