A framework for the adoption of mobile augmented reality in university learning: case study Islamic University in uganda.

Abstract

The purpose of the study was to develop a framework for Adoption of Mobile Augmented Reality(MAR) in learning in resource limited environments. People are trying to understand how the mobile devices will help in attaining better education. In most recent years, due to increasing need for effective knowledge communication, new technologies have emerged. Critical of those is augmented reality (AR) that involves the overlay of computer graphics. AR allows for combining or supplementing real world objects with virtual objects or superimposed information. As a result virtual objects seem to coexist in the same spacewith the real world (Shneiderman, 2010.). Whereas mobile augmented reality is a promising platform for learning, it is at the same time an immature technology. This immaturity is not only tagged to the technology capability but also training and policy implementation. To address these issues involved, this study suggests a framework that can be used to guide the education stakeholders. The framework is based on a case study Islamic University In Uganda (IUIU). In this study Information that was analyzed using SPSS software and discussed was supported by views, theories and findings from previous related research to obtain the requirements of the framework. This Framework is structured into three namely; Significant issues, limited resource environment (training, policy, infrastructure and maturity) and Effective intergration MAR with existing learning systems. Each of these components has various attributes that need to be emphasized on if the University is to successfully implement MAR in teaching and learning as illustrated in the Framework. All these were guided by the objectives of this study as presented in chapter 1 of this dissertation. This study employed quantitative method of collecting data using self-administered questionnaires. These questionnaires also collected qualitative information of the respondents like that of experts on e-learning and learning technologies about certain variables but this was coded and analyzed as discrete information