Personalized Learning Environments

The new personalized learning environment is a digital environment that has the ability to adapt to the student or learner (Ark, 2013). Learning in this environment is modified or customized to meet the learners’ needs. The classroom rotation model is one type of blended learning approach in which students rotate through stations in their physical classroom (Ark, 2013). The use of mobile devices and cloud computing allow students to learn and work in this flexible environment. The instructional software that was once loaded on individual hard drives of personal computers or lab computers is now accessible online, through cloud computing. Learners can access instructional materials or data through their Internet browser, on a mobile device, without leaving the classroom. Cloud computing provides apply-on-demand and apply-in-time resources and tools to learners (Chao & Yue, 2013).

The strengths of personalized learning 2.0 are the adjustments that can be made for individual learners. The learning experience can be personalized based on how the learner is performing while using the environment, in-time or dynamically (Walters, 2014). Additionally, these learning systems can be facilitator-driven or assessment-driven. A facilitator-drive system allows the teacher to make adjustments, while the assessment-drive system make adjustments in the instruction automatically (Walters, 2014). A weakness of personalized learning systems is the lack of social interaction with other students, however many of these new systems allow group collaboration as part of the design. The upfront costs for the devices, upgraded Internet and wireless capabilities, and software access are one downside to integrating this emerging technology. The evolution into personalized learning 3.0 will likely provide true responsiveness. As Walters explains, many personalized learning platforms are currently not truly “adaptive” and provide more of a recommendation based on performance, similar to an Amazon book recommendation. Instead, an evolved personalized learning system would be individualized, provide in-time responsiveness and feedback to the learner.

Ark, T. V. (2013). The future of learning: Personalized, adaptive, and competency-based. Retrieved from:

Chao, C. & Yue, Z. (2013). Research on the m-llearning model based on the cloud computing. Chinese Automation Congress (CAC), 806-811.

Waters, J. K. (2014). The great adaptive learning experiment. Teaching and Learning. Retrieved from:

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