Active and Passive Machine Learning Predictors to Build Adaptive Virtual Environments

Abstract

Virtual environments are increasingly used for assessment and training. While virtual environments offer ecologically valid stimulus presentations, they still follow a one-size fits all model. Technological innovation provides opportunities to transform the virtual environments into a customized experience for each individual user. This allows for the personalization of the virtual environment to the unique capabilities of a user. Active and passive data logging systems provide data necessary for adaptive virtual environments. Currently, most adaptive systems apply either active or passive data collection for building an adaptive virtual environment. The goal of the current research is to identify an optimal methodology for integrating both active and passive data into an adaptive virtual environment that can employ user data for fine tuning stimulus presentations. The framework suggested provides optimal performance parameters for identifying user cognitive and affective states and keeping users in a flow state. The result is a customized experience that is personalized to the user.