A scalable framework to quantitatively evaluate success factors of mobile learning systems

There has been an enormous increase in the use of mobile learning (m-learning) systems in many fields due to the tremendous advancement in information and communication technologies. Although, there are many frameworks that have been developed for identifying and categorising the different components of m-learning systems, most of them have some limitations, drawbacks, and no support for quantitative assessment for the success factors (global weights) of the system criteria. In this paper, a new scalable hierarchal framework is developed, which identifies and categorises all components that may affect the development and deployments of costeffective m-learning. Furthermore, due to the hierarchal structure of the framework, any of the analytic hierarchy process techniques can be used to quantitatively estimate the success factors of the system criteria. In order to demonstrate the benefits and flexibility of the new framework, we develop an interactive software tool for computing success factors of the different system criteria. The tool is referred to as SFacts, and it is used to compute success factors for different sets of preferences.