Imagen de marca ¿Quiénes somos?

Multi-device learning environments refer to those environments in which learners use several devices while being active in a learning environment. The concept has been inspired by Weiser’s (1991) vision of ubiquitous computing and is based on the observation that students are increasingly equipped with multiple devices. Learners work in such environments within a persistent information space, regardless of the device they currently use. Multi-device environments challenge mLearning orchestration systems because the interactions with one device may affect the information provided by other devices.

Glahn and Specht (2010) discuss a multi-device learning orchestration framework for the Moodle LMS. It has been designed to support the distribution of learning resources to appropriate interfaces, if they are available and accessible to the

learner, or to provide shared working spaces for group work if learners are present in the same room.

The framework uses Moodle’s LMS capabilities for storing and distributing information. It does not explicitly implement a sensor layer. Rather, it provides a data collection service that allows external sensor networks to be connected to Moodle and the data stored in Moodle’s native action log. This has the benefit that data from external sensors can be aggregated with the learner tracking of the Web-based system from a single source.

The actuator layer of the system relies on a context model. This model represents which devices are available in an environment so they can be used as instruments for providing resources to the learner. Furthermore, the model contains the defining parameters of an environment. These parameters include the sensors and the framing values that are used to determine the presence of learners in the environment. The indicator layer consists of different tools and services that allow connecting to the LMS and providing interfaces to different instruments. The challenge at this level is to enable the device services requesting user-restricted information from the LMS, although the learners did not or even cannot authorise the device directly. This challenge has been solved through the token authentication of the OAuth protocol (Hardt, 2012). However, instead of connecting a token to a user session, the token is connected to the environment. If the context of the environment changes, the system revokes the token and, if necessary, issues a new token that represents the new condition state. These changes to the environment’s context occur, for example, if a second learner enters a room or if another learner leaves it.

The second challenge is device orchestration because the interaction design of Moodle was tailored for explicit interactions with the system through a single interface. For this interaction type, changes of the learning context can be detected as part of the normal interaction with the system. Device and process orchestration becomes challenging if implicit interactions with the learning environment occur. This can happen if external sensors, such as a room-

mounted presence sensor, submit data to the LMS. In order to create a responsive learning environment, it is important to recognise context changes from implicit interactions with time. Therefore, the aggregator layer notifies the actuator layer if data has been received that might change the state of one or more contexts. Figure 11.6 shows the architecture of the system.

Figure 11.6: Actuator-indicator architecture translation for UbiMoodle (adapted from Glahn & Specht, 2010).

Sensor Service Presence Sensors Moodle Services and Plugins

Activity Log Log Aggregator and Event

Detection Event Trigger Indicator Layer Context Model Information Channel Model Presentation Service for Social Interfaces

triggers Moodle Log Function Control Layer Semantic Layer Sensor Layer Moodle

Conclusion

This chapter analysed the characteristics of mLearning operating systems compared with other approaches of technology-enhanced learning. Two types of systems can be distinguished for operating mLearning: mLearning management systems and mLearning orchestration systems. Both system types need to implement features for supporting the variability of the learning context.

Mobile learning management focuses primarily on managing learning resources, tools and actors in the learning process. This type of information systems is very similar to its conventional Web-based counterparts, but they rely on different contextual assumptions, specifically regarding the connection and learning context. Existing learning management solutions can easily integrate these characteristics for better support of the learners’ mobility.

Mobile learning orchestration addresses the co-ordination of learning processes based on rules, tasks and contexts. The main difference between these systems and their Web-based counterparts is that the learning context can no longer be presumed as constant but as a dynamic factor of learning processes. The chapter discussed a generic system architecture for context-aware and context-responsive learning orchestration. Selected examples from the available literature illustrate the application for this system architecture for location-based and anchored instruction, simulated augmented experiences, and multi-device learning environments.

The analysis in this chapter shows that context needs to be considered already during design of learning activities. In order to reflect context factors for mLearning solutions, it is necessary to model the characteristics of mLearning contexts as part of the educational design. Currently, educational designers need to consider all aspects of this modelling process because of missing interoperability standards, as they are present for Web-based learning. Here lies the biggest challenge for scaling up and sustaining mLearning practice: a technical infrastructure needs to support educational practitioners so they can build and extend contextual models and integrate these models into the structures of educational processes. Research has to identify and categorise patterns for sensor networks and semantically meaningful aggregators in order to consider them for process control and system usability.

References

Advanced Distributed Learning (ADL) Initiative (2009). Sharable content object reference model (SCORM) 2004. (4th ed. run-time environment, Version 1.1). Alexandria, VA: ADL Initiative.

Börner, D., Glahn, C., Stoyanov, S., Kalz, M., & Specht, M. (2010). Expert concept mapping study on mobile learning. Campus-Wide Information Systems,

27(4), 240–253.

Chu, H.-C., Hwang, G.-J., Tsai, C.-C., & Tseng, J. C. R. (2010). A two-tier test approach to developing location-aware mobile learning systems for natural science courses. Computers & Education, 55(4), 1618–1627. De Jong, T., Specht, M., & Koper, R. (2008). Contextualised media for learning.

De Vries, F., Ternier, S., & Visser, I. (2010). Audio augmented spaces for learning,

ARLearn. (Eindrapport Innovatieregeling Hoger Onderwijs 2010).

Heerlen, The Netherlands: Open University of The Netherlands.

Dick, W., Carey, L., & Carey, J. O. (2009). The systematic design of instruction. Upper Saddle River, NJ: Pearson Education.

Engeström, Y. (1996). Perspectives on activity theory. Cambridge: Cambridge University Press.

Engeström, Y. (1999). Expansive visibilization of work: An activity-theoretical perspective. Computer Supported Cooperative Work, 8: 63–93.

FitzGerald, E., Sharples, M., Jones, R., & Priestnall, G. (2011). Guidelines for the design of location-based audio for mobile learning. International Journal of

Mobile and Blended Learning, 3(4), 70–85.

Florian, B., Glahn, C., Drachsler, H., Specht, M., & Fabregat, R. (2011). Activity- based learner-models for learner monitoring and recommendations in Moodle. In C. D. Kloos, D. Gillet, R. M. Crespo Carcía, F. Wild, & M. Wolpers (Eds.), Towards ubiquitous learning (pp. 111–124). Heidelberg, Berlin: Springer.

Glahn, C. (2009). Contextual support of social engagement and reflection on the

Web. Doctoral dissertation, Open University of The Netherland. SIKS

Dissertation Series, 2009-27.

Glahn, C., Mitsopoulou, E., Nake, I., & Wendel, T. (2012). Mobler cards. [Computer software]. Zurich: ETH Zurich. Retrieved February 9, 2013 from https:// github.com/ISN-Zurich/ISN-Learning-Cards/

Glahn, C., & Specht, M. (2010). Embedding Moodle into ubiquitous computing environments. In M. Montebello (Ed.), Proceedings of the 9th World

Conference on Mobile and Contextual Learning (pp. 100–107). Valletta,

Malta: University of Malta.

Glahn, C., Specht, M., & Koper, R. (2008). Smart indicators to support the learning interaction cycle. International Journal of Continuing Engineering Education

and Lifelong Learning, 18(1), 98–117.

Hardt, D. (Ed.) (2012). The OAuth 2.0 authorization framework (Proposed Standard IETF 6749). Retrieved February 9, 2013, from http://tools.ietf. org/html/rfc6749

Koper, R., Olivier, B., & Anderson, T. (Eds.) (2003). IMS Learning Design

information model (IMS Specification, Version 1.0). IMS Global Learning Consortium.

Lave, J. (1993). The practice of learning. In S. Chaikin & J. Lave (Eds.),

Understanding practice, perspectives on activity and context (pp. 3–33).

Cambridge: Cambridge University Press.

Lave, J., & Wenger, E. (1991). Situated learning, legitimate peripheral participation. Cambridge: Cambridge University Press. Norton, M., & Panar, A. (Eds.) (2003). IMS Simple Sequencing information and behavior model (IMS Specification, Version 1.0). IMS Global Learning Consortium.

Reigeluth, C. M. (1983). Instructional design: What is it and why is it? In C.M. Reigeluth (Ed.), Instructional-design theories and models, an overview of their

current status (pp. 3–35). Hillsdale, NJ and London, UK: Lawrence Erlbaum

Associates.

Reigeluth, C. M. (Ed.). (2009). Instructional design theories and models, building a

common knowledge base. New York and London, UK: Routledge.

Sharples, M., Taylor, J., & Vavoula, G. (2007). A theory of learning for the mobile age. In R. Andrews & C. Haythornthwaite (Eds.), The Sage handbook of

eLearning research (pp. 221–247). London: Sage.

Sharples, M., Arnedillo-Sanchez, I., Milrad, M., & Vavoula, G. (2009). Mobile learning: Small devices, big issues. In N. Balacheff, S. Ludvigsen, T. Jong, A. Lazonder, & S. Barnes (Eds.), Technology-enhanced learning (pp. 233– 249). Dordrecht: Springer Netherlands.

Ternier, S., Gonsalves, A., de Vries, F., & Specht, M. (2012). Supporting crisis simluations with the ARLearn toolkit for mobile serious games. In C. Glahn (Ed.), Mobile learning in security and defense organisations (pp. 6–8). 15 October 2012, Helsinki, Finland.

Verpoorten, D., Glahn, C., Kravcik, M., Ternier, S., & Specht, M. (2009). Personalisation of learning in virtual learning environments. In U. Cress, V. Dimitrova, & M. Specht (Eds.), Learning in the synergy of multiple

disciplines (pp. 52–66). Heidelberg, Berlin: Springer-Verlag.

Weiser, M. (1991). The computer for the 21st century. Scientific American, 265(3), 66–75.

Wenger, E. (1998). Communities of practice: learning, meaning, and identity. Cambridge, New York: Cambridge University Press.

Zimmermann, A., Specht, M., & Lorenz, A. (2005). Personalisation and context management. User Modeling and User-Adapted Interaction, 15(3-4), 275–302.