Maximizing Human Potential Using Machine Learning-Driven Applications

The elusive quest to identify and place skilled professionals has become an obsession in the talent wars of the tech industry (not to mention in schools from K though Postdoc). Respected companies such as Google have applied enormous resources to predicting the best developers and managers, and yet they also periodically acknowledge the shortcomings of their existing methodology (e.g., no more brainteasers). We will discuss the concept of continuous passive-implicit assessment, applied to both learners and professionals, from kindergärtners to (future) CEOs. Building cognitive models using unstructured data and ubiquitous sensors allows the assessment not only of concept mastery, but meta-learning development as well (e.g., "Grit" and "Social-Emotional Intelligence"). Such models can then be used to predict which content will be an effective learning experience for a given learner, identify ad hoc cohorts for collaborative learning, and access the value added across educational institutions.

Speaker: Vivienne Ming, Chief Scientist at Gild