Detalle de los productos o servicios

While LMSs support the organisational tasks that are related to educational and learning processes, learning orchestration systems support the implementation of educational designs. From the viewpoint of the activity system, model learning orchestration systems are directly related to the procedural factors of learning activities: rules, task and contexts.

A basic learning orchestration system relies on a process model that defines task sequences based on a set of rules within a learning environment. The rules can be related to learner performance, learner characteristics or learner preferences. Such

models arrange how the actors in a learning process have access to the available instruments and resources. These process models are typically referred to as instructional or educational designs. Currently, two specifications are available for providing and exchanging process models between learning orchestration systems: IMS Simple Sequencing (Norton & Panar, 2003) and IMS Learning Design (Koper et al., 2003).

The IMS Simple Sequencing specification defines the semantics to describe process models for individual learning that can be interpreted by SCORM 2004 run-time environments (ADL Initiative, 2009). IMS Simple Sequencing provides no explicit representation of different actors in the activity. Figure 11.3 illustrates the relation of IMS Simple Sequencing constructs to the activity system model.

Figure 11.3: Focus of IMS Simple Sequencing in relation to the activity system model.

A more generic approach is provided by the IMS Learning Design specification. This specification defines high-level process models based on roles, resources, services, activities and conditions. In the semantics of IMS Learning Design, “activities” refers to tasks in which actors are exposed to selected resources and instruments. Through conditions it is possible to arrange the tasks into processes. Additionally, IMS Learning Design provides the “environment” construct that allows resources and instruments to be combined in order to be re-used across different tasks. An IMS Learning Design environment implies data persistence across the tasks to which the environment is connected. As such, the environment construct refers to a rudimentary learning context. Figure 11.4 illustrates the relation of IMS Learning Design concepts in relation to the activity system model. Figure 11.4: Focus of IMS Learning Design in relation to the activity system model.

The conditional frameworks of IMS Simple Sequencing and IMS Learning Design are based on explicitly modelled user interactions with resources or instruments of the associated learning management component. In both cases, contextual factors are considered as framing the learning activity and cannot influence the flow of educational processes. As mLearning scenarios reflect learning in and across contexts, context factors are no longer framing learning activities but are structuring components of learning processes. This has two important implications for mLearning orchestration and challenges the design of mLearning orchestration systems:

1. Contextualising factors of the learning environment need to be considered as structuring components of learning processes.

2. Learning processes are no longer influenced only by direct interactions with the supporting learning orchestration system but also by factors that are emerging from the dynamics of the learners’ mobility.

To tackle these challenges, the actuator-indicator architecture has been proposed as a generic attempt for designing and developing context-aware systems

(Zimmermann, Specht, & Lorenz, 2005). This architecture has been proven to be practically relevant for building context-aware and context-responsive systems for different educational settings (De Jong, Specht, & Koper, 2008; Florian, Glahn, Drachsler, Specht, & Fabregat, 2011; Glahn & Specht, 2010; Glahn, Specht, & Koper, 2008). The architecture allows conceptualising the different phases of data processing for context-aware interactive systems using four primary layers: the sensor layer, the semantic layer, the actuator layer and the indicator layer (see Figure 11.5).

Figure 11.5: Core components of the actuator-indicator architecture (Glahn, 2009).

The sensor layer defines the ways in which actors can interact with the system. As in mLearning scenarios, interactions can be explicit by interacting with a user interface on a device or implicit by performing in an environment. These sensors form a “sensor network” that allows combining the data from these sensors for creating richer information. Larger sensor networks can span physical space, such as movement sensors in a building, and even extend to a global scale such as

tsunami warning systems. The sensor layer defines a sensor network that captures explicit and implicit interactions within a learning environment.

The simplest sensor networks are built directly into mobile devices. For example, recent smartphones provide the following sensors:

• microphone • camera • GPS receiver • compass • proximity sensor • accelerometer

• touch-sensitive surface (touchscreen)

The semantic layer collects the data provided by the sensor network and processes this data into higher-level information. This processing is also called “data aggregation.” An aggregator is a function that transforms sensor data into semantically meaningful information. The aggregation of sensor information can identify traces of

activities. For example, a GPS receiver provides the current location of the device. By aggregating a sequence of locations, it is possible to determine movements and the orientation of a device. If the time of location measurement is also known, an aggregator can also provide information about how fast a device has travelled. In the context of technology-enhanced learning, the definition of appropriate aggregators is the subject of the research on learning analytics.

The actuator layer uses the semantic information of one or more aggregators for determining the state of a process and for activating system behaviour accordingly. This layer controls the behaviour of a context-aware system by applying different strategies. A strategy defines the system behaviour under certain conditions. These conditions include activation and termination rules for a strategy. Strategies can be predefined or automatically generated by the system. A set of predefined strategies is also called a script, such as an educational design.

The indicator layer provides human interpretable interfaces that reflect the system behaviour. The actuator layer controls the information that is presented by the indicator layer. The indicator layer is subject primarily to user interface design and system usability.