Design of Contextualised Mobile Learning Applications (#mlearning)
The chapter 5 in the open licensed ebook “Increasing Access through Mobile Learning”(published by Commonwealth of Learning and Athabasca University, 2014) discusses several issues about linking the user experience to the current context of use. (authored by Marcus Specht)
The linking of the mobile learning support to the current context is seen as key to efficient and effective design of mobile learning applications. The author introduces the Ambient Information Channel (AICHE) model as an approach for building contextualised learning support. The AICHE model and its main components and process are introduced and example applications built from the model are given. The model gives a guideline for the development of applications from the use of sensor information to the specification of the instructional logic. The main added value of the model lies in the systematic support for re-use of instructional patterns for ubiquitous learning, and also in the re-use of technological components for different levels of the application model. The example applications built with the AICHE model range from a simple notification system to the complex installation of embedded sensor technology and multiple displays — that is, input and output channels.
In the development of user interfaces for mobile learning (mLearning), several aspects have been studied. For example:
- Mobile interface design: From a mobile usability perspective, the reduced screen estate has been an essential issue for Human–Computer Interaction (HCI) research and how to design user interfaces for small screens. This is linked to questions of how to enable navigation in complex information spaces with a reduced information channel such as the small display on mobile phones. Today, there are clear style guides and design patterns for mobile user interfaces on smartphones for the major platforms. There are also increasingly more ways to ensure a consistent interaction design across different platforms such as ambient and situated displays, tablets and smartphones. HCI research has also developed flexible methods for mapping user interface functionality to different user interfaces.
- Mobile legacy access: The access of legacy content and learning management solutions has been a topic of research (Glahn & Specht, 2010). Nowadays, most existing learning management systems (or LMS) provide mobile access to the main functionality, but it is still a challenging task to prioritise and structure the access to the functionality and to decide which functionality is actually helpful in a mobile context.
- Contextualised learning support: The contextualised filtering of information and the provision of system functionality based on mobile phone sensors such as location and compass have recently become more popular (Brown, 2010). Even in the late 1990s, new kinds of user interfaces were explored to enable physical movement of users in museum environments for navigation in the information space (Oppermann & Specht, 2000). Recent developments link mobile apps more to the current context of the user and use of the information — about location, other information sources in the vicinity, or the social context of a person — to filter information and functionality in the mobile application.
- Seamless and cross-context support: Mobile apps are being combined and integrated with cloud-based services and multiplatform applications to enable seamless and cross-context performance support for learning (Wong & Looi, 2011). For learning support, this tackles the issue of orchestration of learning (Dillenbourg, 2011) across multiple devices and in ubiquitous learning environments.
The AICHE focuses on the design of integrated experiences that synchronise the user services provided via mobile technology and the resources available in the user’s environment, situation or context.
While at the beginning of mLearning there was a focus on the mobile technology as such, this changed gradually to a focus on the mobility of the learner and the seamless access to learning support. (Traxler, 2009) And, there are three different forms of context should be taken into account for learning support: formalised context, physical context and socialising context (Frohberg et al., 2009).
The field of context-aware computing has developed a variety of context definitions mostly starting from location or object context. In a pragmatic approach, Zimmermann, Lorenz, and Oppermann (2007) give a workable definition of context: “any information that can be used to characterise the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between the user and the application, including the user and the applications themselves.” Moreover, Zimmermann et al. clustered context into five fundamental categories:
- Individuality – Includes information about objects and users in the real world (with respect to users, their profile can include preferences, acquired-desired competences, learning style, etc.). This facet of context can also refer to information about groups and the attributes or properties the members have in common.
- Time – Refers to tempo co-ordinates. These can range from simple points in time to ranges, intervals and a complete history of entities.
- Location – Refers to physical and/or virtual spatial co-ordinates. These can be described based on quantitative or qualitative location models, which allow working with absolute or relative positions, respectively.
- Activity – Refers to what the entity wants to achieve and how. This reflects the entity’s goals, tasks and actions.
- Relations – Captures the relation an entity has established with other entities, and describes social, functional and compositional relationships.
The AICHE model allows the describing of patterns of contextual learning support in a generalised way. It integrates research of the last ten years about context-aware computing, information modelling, adaptive hypermedia and instruction, instructional design, and human–computer interaction.
AICHE uses a simple metaphor of information channels that are ambient all around us. Technically speaking, the underlying assumption is that one can access any kind of information as documents, messages, annotations and services in any given situation. Based on this assumption, one has the freedom to plan for educationally sensible interactions and scaffolds as described in the phases of Luckin’s model (2010) and does not need to think about technical barriers.
Channels, users and artefacts make use of sensor information to aggregate and match contextual information according to the instructional logic. This is related to the filters in the “ecology of resources” model of Luckin. The contextual learning applications in AICHE work in four layers (Specht, 2009). The four layers are: sensor layer, aggregation layer, control layer and indicator layer. In the four layers, different components are used. These are mainly sensors, channels, artefacts and control structures.
Building AICHE applications includes several steps that range from technical integration to instructional logic implementation. Check out the chapter for details and example applications based on AICHE.
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