Simulator Pre-Screening of Underprepared Drivers Prior to Licensing On-Road Examination: Clustering of Virtual Driving Test Time Series Data.

Background: A large Midwestern state commissioned a virtual driving test (VDT) to assess driving skills preparedness before the on-road examination (ORE). Since July 2017, a pilot deployment of the VDT in state licensing centers (VDT pilot) has collected both VDT and ORE data from new license applicants with the aim of creating a scoring algorithm that could predict those who were underprepared.

Objective: Leveraging data collected from the VDT pilot, this study aimed to develop and conduct an initial evaluation of a novel machine learning (ML)-based classifier using limited domain knowledge and minimal feature engineering to reliably predict applicant pass/fail on the ORE.

Exploring the roles of motivation and cognition in label-usage using a combined eye-tracking and retrospective think aloud approach.

This study investigates the factors affecting consumers' motivation to engage with food product labelling in the new product context. Using yogurt as a case food, due to its positive association with health, enjoyment and convenience, this study uses eye-tracking experiments, a retrospective think-aloud protocol and semi-structured interviews, to bring to light the conscious and subconscious mechanisms associated with label usage, in order to explore the cognitive processes underlying usage of labels for new product offerings and situate these within the participant's personal context. Key information usage and decision-making strategies and the factors which give rise to these are identified.