Predicting Job Performance Using Mobile Sensing

We hypothesize that behavioral patterns of people are reflected in how they interact with their mobile devices and that continuous sensor data passively collected from their phones and wearables can infer their job performance. Specifically, we study day-today job performance (improvement, no change, decline) of N=298 information workers using mobile sensing data and offer data-driven insights into what data patterns may lead to a high-performing day. Through analyzing workers' mobile sensing data, we predict their performance on a handful of job performance questionnaires with an F-1 score of 75%. In addition, through numerical analysis of the model, we get insights into how individuals must change their behavior so that the model predicts improvements in their job performance. For instance, one worker may benefit if they put their phone down and reduce their screen time, while another worker may benefit from getting more sleep.

The recent development of mobile computing technology with various built-in sensors has made it possible to sense human behavior continuously at a low cost. In the context of ubiquitous computing and human–computer interaction, mobile sensing data collected from individuals are often combined with different machine learning algorithms to build models that understand and predict behavioral patterns. These predictive models can be embedded in mobile devices and support people in every aspect of life. In this article, we focus on mobile sensing in the workplace, where people spend much of their time to make a living. We discuss methods to predict workers’ day-to-day job performance using passive sensing data from phones and wearables.