Researchers, instructors and designers continue to look for the right balance of learner control within learning experiences. Some believe this personal, learner-centric approach will drive the best learning outcomes. But what makes a learning experience personal? Is a personal learning experience driven and controlled solely by a learner or can another entity (e.g., instructor, teacher and system/application) contribute? How can the evolving field of learning analytics support the evolution of these personal learning ecosystems?

Members of the learning, education and training community have been researching and experimenting with a variety of "personal learning environments" (PLEs) as a way of giving a learner more control. PLEs can give the learner the ability to curate an adaptable means of aggregating content, people, applications and tools. This ecosystem helps make the learning experience more personal and also more useful to the person's specific needs and interests.

One often overlooked aspect of driving towards a more personal learning ecosystem is the evolving discipline of learning analytics. Learning analytics can be viewed as the use of a variety of data and analytic techniques to support the analysis and study of the "learning process." Some define learning analytics as the science behind turning data into action, enabling new developments in curriculum mapping, personalization and adaptation, prediction, intervention and competency determination.

This paper examines the characteristics and components of PLEs and looks at the ways PLEs can create more friction-free environments in which learning feels more open and autonomous. It then examines the impacts learning analytics could have in creating a future in which PLEs can help learners leverage the lifelong learning experiences they encounter.