We have already witnessed how the applications of technological advancements have provided increases in learning outcomes for all learner types. For example we have seen; higher levels of motivation and engagement through the adoption of immersive technologies; more readily available relevant data through advancements such as xAPI; greater, more rapidly available understanding of learner outcomes with applications of Artificial Intelligence (AI), and increased capabilities to rapidly gather, analyze, and process all of this data through increased computational capabilities. However, an understanding of overall learner outcomes is just the beginning—technology will continue to become an increasingly integrated part of our lives, and with it, a previously unimaginable quantity of individualized data available at the point of need.  

With this individualized data, we need to look not only to track learner outcomes—but track, analyze, and understand how effective different pedagogical approaches of learning are, at the individual level. In this presentation we will discuss; the critical relationship of individual preferences on near/far transference and training effectiveness; the role that advancements in cloud computing will play in increased data delivery when/where it is most valuable; and how ‘Big Data’ and AI will enable training to be delivered to learners in a way that maximizes its effectiveness for that learner. Further, we will discuss how the focus on training outcomes at the individual level correlates to overall training effectiveness between echelons—paving the way for adaptations in training that by design results in training that can be completed faster, by more individuals, with better outcomes – and ultimately provide sustainability of workforce capabilities in light of uncertainties.

Keywords
ADAPTABILITY,AI,ANALYTICS,BEST PRACTICES,BIG DATA,CLOUD COMPUTING,COMPETENCY BASED TRAINING,DEEP LEARNING,EXPERIENCE API,LEARNING ANALYTICS,PEDAGOGICAL DESIGN

Additional Keywords