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Two broad approaches exist for learning technology implementation: The adoption of a centralized learning management approach. This may include development of a central learning support lab where new courses are developed in a team-based approach—consisting of subject matter expert, graphic designers, instructional designer, and programmers. This model can be effective for creation of new courses and programs receiving large sources of funding. Most likely, however, enterprise-wide adoption (standardizing on a single LMS) requires individual departments and faculty members to move courses online by themselves. Support may be provided for learning how to use the LMS, but moving content online is largely the responsibility of faculty. This model works well for environments where faculty have a high degree of autonomy, though it does cause varying levels of quality in online courses. Personal learning environments (PLEs) are a recent trend addressing the limitations of an LMS. Instead of a centralized model of design and deployment, individual departments select from a collage of tools—each intending to serve a particular function in the learning process. Instead of limited functionality, with highly centralized control and sequential delivery of learning, a PLE provides a more contextually appropriate toolset. The greater adaptability to differing learning approaches and environments afforded by PLEs is offset by the challenge of reduced structure in management and implementation of learning. This can present a significant challenge when organizations value traditional lecture learning models.

Self-organised learning networks provide a base for the establishment of a form of education that goes beyond course and curriculum centric models, and envisions a learner-centred and learner controlled model of lifelong learning. In such learning contexts learners have the same possibilities to act that teachers and other staff members have in regular, less learner-centred educational approaches. In addition these networks are designed to operate without increasing the workload for learners or staff members.

Instead of learning housed in content management systems, learning is embedded in rich networks and conversational spaces. The onus, again, falls on the university to define its views of learning.

Interactions with one’s artifact increase intrinsic motivation and create connection between people and knowledge.

I have introduced in the Plearn blog the principle of ELIS Wheel : Explore, Learn, Interact and Socialize in the learning process. We need to create something and share it with others in the cloud and we need to interact to enhance intrinsic and extrinsic motivations in the crowd’s spirit.

Our inferences, therefore, are based on salience, where salience may be thought of as the importance, relevance or vivacity of some property or perception. We 'pick out' those perceptions that will be of use to us, and disregard the rest.

This is not often even a conscious process; it is based in part on innate reactions (such as jumping when we hear a loud sound) and largely on prior expectations. Our past knowledge has led us to recognize that something that looks and sounds like a tiger is something we should pay attention to, and so our inference engine kicks into high gear.

I must admit that I struggled with some of the basic concepts when I first read Siemens’ paper, and I found the lack of practical examples frustrating.