This paper proposes a conceptual framework for mobile learning applications that provides systematic support for mobile learning experience design. It is based on a combination of a game metaphor and several studies of mobile learning contexts. An account of the Ambient Wood project is used to explore the relationship between the framework and mobile learning design requirements in practice.

Quality in a mobile learning system can be assessed both in terms of product quality and in terms of the quality of the learning experience. Though these do overlap in some respects, it is also the case that current quality metrics such as ISO/IEC 9126 do not address what might be regarded as the ‘softer ’ aspects of quality. Research indicates that the quality of a learning experience is not solely based on the quality of the software but also on the conceptual basis upon which the learning experience is constructed. In this paper we explore the quality related aspects of a conceptual framework for mobile learning and propose some metrics that might be adopted to assess the quality of a mobile learning application.

This paper proposes a conceptual framework for the design of mobile learning solutions that address the special requirements of professional development. This framework is based on a combination of a game metaphor and the various dimensions of the mobile learning context. It provides the basis for a paper prototyping approach that uses sticky notes to represent both aspects of the display and supporting media types and components. We describe the framework and the paper prototyping approach and apply the prototyping method to some indicative content from a real world example. We reflect on the relationship between the framework and the paper prototyping method and outline further collaborative research with a partner organisation.

This paper proposes a conceptual framework for mobile learning applications that provides systematic support for mobile learning experience design. It is based on a combination of a game metaphor and several studies of mobile learning contexts. Accounts of four mobile learning projects are used to explore the relationship between the framework and mobile learning design requirements in practice. By applying the framework to previous successful mobile learning implementations, we are better able to understand their key qualities. Similarly, the framework provides forward engineering support for the successful design of future mobile learning systems.

Classification of mobile devices Advantages of their use Technical restrictions and solutions Learning methods and applications

This study validates a pedagogical framework, which integrates a mainstream game to teach the technology supporting that game. Technologies in learning games can engage students and make the learning contents interesting to them. However, technologies themselves are seldom the subject of learning in the game they support. In addition, learning activities in a gaming context usually engage students temporarily in the subject. As soon as the game is over, students ’ desire for learning may cease. The framework tries to achieve the transition of students ’ interests from the initial interest motivated by the game to the later-developed cognitive and personal interests in the subject. Based on the framework, this study designs and experiments a practice to evaluate the proposed framework. Participants ’ subjective responses show their satisfaction about the practice of learning and developing interest of the subject matter. This study not only provides a dual aspect of game technology adoption for motivating students, but also proposes a pedagogical framework for students ’ lasting learning. It is expected to provide a new and valuable idea about the use of technologies and games in education.

Bruk av iPhone som pedagogisk verktøy i skolen Innebygde bevegelsesensorer brukt i applikasjoner for fysikkfaget

Internet has increasingly become the place for online learning, and exchange of ideas. The rapid development in wireless technology offering fast data transfer has lead to mobile device revolution. With the ease of access of mobile devices like mobile phones, PDAs, tablet PCs and high bandwidth through wireless, there is an upsurge of mobile learning or M-learning. It is important to know the opinion of users using m-learning platforms for developing and fine tuning of M-learning systems. The sheer volume of reviews found in the internet blog spot, bulletin board makes it difficult to track and understand customer opinions. Opinion mining also known as sentiment mining is an area of research which attempts at determining the opinion underlying a text written in natural language which summarizes the customer reviews and express whether the opinions are positive or negative. In this paper, we investigate the classification accuracy of machine learning algorithms for opinion mining of M-learning system review.