Academic journal article Informatica Economica

Cloud-Based Mobile Learning

Academic journal article Informatica Economica

Cloud-Based Mobile Learning

Article excerpt

As the cloud technologies are largely studied and mobile technologies are evolving, new directions for development of mobile learning tools deployed on cloud are proposed.. MLearning is treated as part of the ubiquitous learning paradigm and is a pervasive extension of E-Learning technologies. Development of such learning tools requires specific development strategies for an effective abstracting of pedagogical principles at the software design and implementation level. Current paper explores an interdisciplinary approach for designing and development of cloud based M-Learning tools by mapping a specific development strategy usedfor educational programs to software prototyping strategy. In order for such instruments to be user effective from the learning outcome point of view, the evaluation process must be rigorous as we propose a metric model for expressing the trainee 's overall learning experience with evaluated levels of interactivity, content presentation and graphical user interface usability.

Keywords: M-learning, Quality Metrics, Cloud Computing, Software design, Mobile Learning

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1 Introduction

As cloud systems development is moving further, more and more web-based and mobile applications are developed using cloud technologies. These technologies provide elasticity and flexibility of the resources for cloud-enabled applications. Cloud computing provides datacenter computing power and storage. E-Learning and M-Leaming tools can benefit from cloud technology expansion and can be easily developed as cloud-enabled applications. Cloud-enabled M-learning applications have the advantage of resource elasticity and will eliminate the device resource limitations.

National Institute of Standards and Technology (NIST) [1] defines cloud computing as a computing model which offers network access to a configurable resource pool, the access being location transparent, convenient and on-demand. These resource pools consist of networks, servers, storage, applications and services which can be used by the end user with a minimum management effort and interaction with the cloud provider. Same report [1] identifies three models for services delivery in cloud computing: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). As [2] extends the list of service model delivery in cloud computing, a Learning as a Service (LaaS) model could be considered for cloudenabled learning tools including cloud Elearning systems and cloud M-learning systems.

According to [3] M-Learning is part of ubiquitous learning paradigm and has two dimensions: (a) physical space and timetables independency (pervasive) and (b) immediate access to resources due to its distributed nature. Consequently, M-Leaming can be a powerful instrument for continuous learning process, as mobile technologies and infrastructures expands.

This paper studies de feasibility and applicability of mobile cloud-based learning solutions, identifies the main requirements and proposes a development strategy of such systems. Accordingly, we designed an MLeaming platform prototype and we conducted an experiment within the students of Business Information Systems department in order to evaluate the end-user experience, utility and satisfaction, identifying the main requirements and challenges based on the users' feedback, work presented in [4], Furthermore we conducted a statistical analysis regarding the four different evaluated issues:

Overall M-Leaming experience in relation with Content presentation, Graphical User Interface interaction and Interactivity, defining a metric to quantify the effectiveness of the prototype from the end-users' point of view.

This paper represents an extended version of [4] and it is structured as follows: section 2 presents some relevant approaches from the existing literature, section 3 details the specific methodology used for development and evaluation of the prototype while section 4 presents the data analysis that was conducted, followed by section 5 where the overall experience metric is built, ending with conclusions and future development. …

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